Saturday 24 December 2022

Merry Christmas!!


May the sparkle and joy of Christmas fill your heart, I wish you a season filled with happiness, peace, health and Pleasure. Lupine Crew wishing you a magical holiday season.


Wishing you and your loved ones a Blessed Christmas!

Friday 23 December 2022

Lupine Publishers | Quality Issues Relating to Composition of Imported Honey into Saudi Arabia

 Lupine Publishers | Journal of Food and Nutrition


This study evaluated the quality of honey imported from eighteen different countries into Saudi Arabia. Twenty pesticides, 5-hydroxymethlfurfural and the antibiotic chloramphenicol were analysed. Approximately 20% of honey was rejected out of 712 consignments. Ten countries breached the regulations for one or more of the following: exceeding the MRLs, using banned pesticides or presence of chloramphenicol. Three neonicotinoids; acetamiprid, imidacloprid and thiamethoxam was found in combination with other pesticides. The HMF content of honey from eight exporting countries exceeded 80mg/kg. Despite the years of monitoring for pesticides, breaches of MRLs continue to be reported. Recommendation for more stringent approaches to the management of pesticide along the supply chain are suggested as the implications to bee pollinators, environment and human life are wide, varied and unsafe.

Keywords: Pesticides; Monitoring; Honey; Saudi Arabia


Honey is defined by Codex Alimentarius [1] as the natural sweet substance produced by honey bees from the nectar of plants or from secretions of living parts of plants or excretions of plant sucking insects on the living parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate, store and leave in the honey comb to ripen and mature. The demand for natural sweeteners is on the increase globally [2-3] and many consumers prefer honey since it has a multitude of uses and benefits [4-5]. In Saudi Arabia, and in many other countries, the major motivators for consuming honey include health and wellbeing, medicinal and nutritional value [6]. However, bee products can also be a source of toxic substances [7-8]-antibiotics (such as chloramphenicol) [9], pesticides (neonicotinoids) [10] and heavy metals (e.g lead, cadmium and arsenic) [7] due to environmental pollution and misuse of beekeeping practices. Pesticide residues have been implicated in genetic mutations and cellular degradation while the presence of antibiotics may increase resistance in human or animal pathogens. In addition, Abeshu and Gelata [6] reported there have been cases of infant botulisms that have been attributed to contaminated honey. Honey that has not been analysed and sterilized should not be used in infants and should not be applied to wounds or used for medicinal purposes. The Maximum Residue Limit (MRL) set for neonicotinoids by the European Union Commission are 50ng/g for acetamiprid, imidacloprid and thiacloprid and 10ng/g for clothianidin and thiamethoxam [11]. Due to their high acute toxicity and concern, the European Food Safety Authority re-assessed the risks and placed a moratorium in 2013 on three [12-14] of the most harmful neonicotinoids (imidacloprid, clothianidin and thiamethoxam). The poisoning of bee pollinators is a result of serious adverse effect of insecticide use, which leads to a drastic decrease in the insect numbers, reduction of honey yields, destruction of plant life, presence of insecticide residues in food, and ultimately, to significant losses in the income of beekeepers. Thus, the main purposes for monitoring bee products are to assist in public health protection, global commercial competition and to realise better quality products. In addition, this provides a greater understanding of some of the issues in the supply chain with regard to pesticide loads as bee pollinators have been recognised as bioindicators of environmental pollution [15].

Saudi Arabia imported US$73 million worth of honey and the worldwide importation of honey totalled US$2.01 billion in 2019 [16]. In international trade, the quality of honey will vary depending on a number of factors. These may include the authenticity (nature, organic, region etc) type of honey (blossom honey, honeydew honey, comb honey, filtered honey, bakers honey etc), moisture content, electrical conductivity, diastase activity, 5-hydroxymethylfurfural (HMF) content, antibiotics, colour and sugar content (glucose and fructose together and sucrose). According to Codex Alimentarius Standard [17] these quality standards are not compulsory for governments and can be voluntarily agreed upon, while according to the EU draft they have to be fulfilled by all commercial retail honeys. Many organisations use the Codex Standard for Honey but importing countries may use this standard with their own stipulation that vary in specifications. This work examined the quality of imported honey arriving at the Port of Jizan in Saudi Arabia. The objective of this study was to analyse the imported honey from different regions around the world in order to highlight the variances in the quality of honey by country. In addition, the results of this study will provide guidance to importers as well as competent authorities about breaches and practices in the countries of origin. Recommendations for Good Agricultural Practice (GAP) that incorporate HACCP and rigorous auditing are made.

Materials and Methods

Sample Collection

Our approach utilises the sampling method by Grainger (2000) [18]. Product arriving at the Port of Jizan during 2018 was placed on hold until the final results were obtained. Each consignment was randomly sampled at 2.5% of the volume of shipment. Drums were thoroughly mixed using a paint mixer for five minutes. After allowing two minutes for settling of contents, three samples were removed, one from the top, one from the centre and one from the bottom. Each sample was analysed in triplicate. Any product in bottles within cartons was also sampled at the above rate.

Analytical Procedures

Determination of Pesticides

Pesticide analysis was conducted using the procedure by Camino-Sanchez et al (19) as reported in Khatri et al. (20). The pesticides acetamiprid, imidacloprid, carbendazim, methomyl, metalaxyl, pyridaben, indoxacarb, azoxystrobin, difenoconazole, tebuconazole, boscolid, linuron, ethion, metalaxl-m, chlorpyrifos, thiamethoxam, mycobutanil, hexythiazox, chinomethionat and biphenyl were determined by means of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) using standards obtained from Dr. Ehrenstofer GmbH (Germany).

Extraction Procedure

Accurate sample weights of 10±0.1g were measures and then samples were transferred into a 50ml PTFE tube (extraction kits). To this 10ml acetonitrile was added and shaken vigorously for 1 min. Buffer salt was added. The mixture was then shaken vigorously for 1 min and centrifuged at 10 000 RPM for 10 min. The upper clear solution was transferred into dispersive solid phase extraction tubes (15ml Polyethylene tube) containing 150mg primary secondary amine (PSA) and 900mg anhydrous magnesium sulphate. The tube was capped and the extract was mixed with sorbent and vigorously mixed for 1 min followed by centrifugation at 4000 RPM for 5 min. Two millilitres of the clear extract was transferred into stoppered vials.

Analytical Procedure

The preferred technique for determination of multiresidue methods reported for fruits and vegetables are based mostly on the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). LC‐MS/MS was performed with an Agilent 1200 series HPLC instrument coupled to an API 3200 Qtrap MS/MS from Applied Biosystems with electrospray ionization interface (ESI) (AB SCIEX, Dublin, CA, USA) and operated under unit mass resolution. The pesticide analysis procedure was conducted as reported in [15] by Sanchez et al (2010). The samples were extracted following the quick, easy, cheap, effective, rugged and safe method known as QuEChERS.

A 20μl sample extract was injected for chromatography into a C18 column ZORBAX Eclipse XDB‐C18 4.6x150mm, 5μm particle size (Agilent, Santa Clara, CA, USA), in which Mobile Phase A contained 5mM ammonium format and Mobile Phase B was methanol. An ESI source was used in the positive mode, with nitrogen as the nebulizer curtain gas. Other gas settings were optimized according to recommendations made by the manufacturer; source temperature was 300 °C, gradient elution programme was 0.3ml/min flow, ion spray potential: 5500 V, de‐cluster potential and collision energy were optimized using a syringe pump by introducing individual pesticide solutions into the MS instrument to allow optimization of the MS/MS conditions.

Identification and Quantification

The selected reaction monitoring (SRM) mode was used in which one transition ion product was used for quantification and the other for confirmation. The identification of a pesticide residue was considered to be confirmed when the retention time of the pesticide matched with that of the pesticide in the pure standard in and the appearance of two product ion transitions that matched the relative intensity criterion under SRM conditions. Once the presence of a pesticide residue was confirmed in an extract, the concentration of the residue was obtained from the appropriate calibration function which corresponds to the matrix‐matched calibration standards. Calibration standard curves were produced by plotting the peak areas for each pesticide versus its concentration with the matrixmatched standard solution and used for the quantification of each pesticide in the sample extract. All sample analyses were conducted in triplicate. The standard curves were linear in the range 0.005- 0.200μg/g with correlation coefficients greater than 0.998. The concentration of the pesticide in the sample extract, Cs (μg/g), was calculated using the following formula:

Cs = Ci x Vtot/Ve x Vf/W Where:
Cs = sample concentration (μg/g)
Ci = injection concentration (μg/ml)
Vtot = total volume of extraction (ml)
Ve = volume for evaporation (ml)
Vf = final volume (ml)
W = sample weight (g)

Antibiotic Determination

Chloramphenicol testing was achieved using the method provided by Ortelli et al. [10]. LC-MS/MS was utilised to test samples against a chloramphenicol standard from Thermo Fisher Scientific (UK). The AB SCIEX Triple Quad 3500 system enables relatively rapid laboratory performing antibiotic testing and was operated with Turbo V source and Electrospray Ionization (ESI) probe set to 500°C. QuEChERS extracts were diluted 10 times with water to minimize possible matrix effects. Honey samples were diluted with 5 times water and injected directly. LC separation was achieved using a Phenomenex Kinetex Biphenyl 2.6u (50 x 2.1mm) column and a fast gradient of water and acetonitrile with 0.1% formic acid at a flow rate of 0.5 mL/min. An injection volume of 10μl was used.

Determination of 5-Hydroxymethylfurfural

HMF content was measured using method by Winkler [23] as reported in Zapalla et al. [24]. Ten grams of honey were dissolved in 20ml water and transferred to a 50ml volumetric flask. Exactly 2ml of the diluted honey solution and 5.0ml of p-toluidine solution were placed in two separate test tubes; to the first tube 1ml of distilled water was added (this acted as a reference solution); to the second tube, 1ml of 0.5% barbituric acid solution was added (this was the sample solution). The absorbance of the sample was measured against the blank at 550nm was determined using a Varian UVVIS Cary 400 spectrophotometer. For the calibration, a standard solution of 0.300μg of HMF was spectrophotometrically assayed. The quantitative value of HMF was calculated using the proposed formula for the method [25].

Statistical Analyses

Data analysis was performed using SPSS software, version 19.0 (IBM Corporation, Armonk, NY). Descriptive statistics for frequencies and ranges were used to summarise the variables of interest.

Results and discussion

Table 1: Pesticides Detected in Imported Honey.


A total of 712 batches of product from 18 countries (Benin, New Zealand, Poland, Bulgaria, Pakistan, USA, Morocco, Hungary, Portugal, Kazakhstan, Kyrgyzstan, Tajikistan, Slovenia, Turkey, Italy, France, UK and Germany) were analysed. Products were rejected based on any one of the following - exceeding the pesticide MRLs or presence of banned pesticides, detection of the antibiotic chloramphenicol or HMF greater than 80mg/kg. Figure 1 shows that 19.9% of product was rejected (n=142) with 80.1% being accepted (n = 570). The countries breaching the limits are shown in Figure 2. These countries were: Benin 8 batches, Pakistan 6 batches, Kazakhstan 12 batches, Kyrgyzstan 52 batches, Tajikistan 6 batches (all being rejected), Slovenia 12 batches, Turkey 9 batches, Italy 27 batches and France 8 batches. The number of accepted batches were 13, 161, 3, 8, 10, 2, 8, 2, 1, 32, 69, 78, 25, 126, 24, 8, and 3 for Benin, New Zealand, Poland, Bulgaria, Pakistan, USA, Morocco, Hungary, Portugal, Kazakhstan, Kyrgyzstan, Slovenia, Turkey, Italy, France, UK and Germany respectively. The number of samples from various batches containing different pesticides is shown in Table 1. There were 192 breaches of MRLs in the 712 batches. One or more of these pesticide residues was present in some of the imported batches. Thus, neonicotinoids coexisted with other pesticides which could increase harmful effects to pollinators and humans. Ethion, acetamiprid, carbendazim and imazalil are banned in Saudi Arabia and their presence is a concern for importers as well as exporting bodies. Table 2 the reasons applied for rejection are provided by country of origin. Benin, Kazakhstan, Kyrgyzstan and Tajikistan had consignments rejected due to the presence of chloramphenicol, exceeding the MRLs authorised for human consumption and levels of HMF greater than 80mg/kg. Batches from Morocco, Slovenia, Turkey and Italy contravened the MRLs as well as the HMF requirements while honey from Pakistan and France breached the MRLs only.

Figure 1: Percentage of Imported Honey Rejected/Accepted.


Figure 2: Compliance Rate for Imported Honey.


Table 2: Reasons for Rejecting Imported Honey from Various Countries.


In the study by Mitchell et al. [11], they found neonicotinoids in 75% of the samples, although, concentrations in all cases were below the admissible levels. Many pesticides were present in tandem with others. Despite this fact, evidence from two fairly recent studies [26-27] on the impact of neonicotinoids on human health could warrant re-evaluation of the MRLs towards more stringent levels and control measures, especially, when up-regulation of nicotinic a4b2 Achars receptors in mammalian brains during long-lasting exposure and higher affinity metabolites have been found using imidacloprid. Sub-lethal effects of neonic pesticides on bees have been documented as suppression of the immune system, cognitive ailments, impaired reproductive function, queen survival and poor honing capacity [11]. The level of HMF in honey is an indicator of freshness and quality. It is formed from reducing sugars on heating in the Maillard reaction under acidic conditions. Typically, HMF is absent in honey (or is present in only very small amounts in fresh honeys), while its concentration tends to rise during processing and/or because of storage. HMF has been shown [22, 28] to have negative effects on human health, such as cytotoxicity toward mucous membranes, the skin and the upper respiratory tract, mutagenicity, chromosomal aberrations and carcinogenicity toward humans and animals. The maximum levels of HMF used in international trade is 40mg/kg. However, a level of 80mg/kg is used for tropical honeys and bakers honey.

Chloramphenicol is normally used to control bee brood disease [8]. Both Codex and EU Standards prohibit the use of antibiotics in honey. It is permitted in some countries such as India and Iran [29], Turkey [30] and has also been detected in samples from China that were imported into Canada [31]. Concerns relating to antibiotics include allergic reactions in hypersensitive individuals and disorder of the haemopoietic system, or problems indirectly through induction of resistant strains of bacteria. It is quite clear that the quality of honey provides invaluable information about certain aspects in the supply chain. Measuring pesticide MRLs and antibiotic residues (in this case chloramphenicol) have revealed issues about misuse of neonicotinoids and prohibited pesticides and excessive use beyond internationally recognised standards, several pesticides used in combination as well as some of the countries flaunting the regulations and utilising banned substances. Measuring the HMF level reveals information on further processing which includes heating and also of its age. Bee and other pollinators, the environment and human health are at risk and therefore agricultural authorities are urged to provide appropriate and rigorous training for the use of pesticides, provide a clear understanding between importing and exporting bodies as well as importing country legislation. Furthermore, auditing of facilities with rigid specifications should include HACCP requirements on farm in line with GAP.


Imported honey into Saudi Arabia from eighteen countries was analysed for pesticides, chloramphenicol and HMF. Approximately 20% of imported honey was rejected. Moreover, imported honey from 10 countries breached the MRLs and in some cases, with pesticides that have been banned as well as the presence of chloramphenicol from four countries. HMF was in excess of 80mg/ kg from 8 countries. Routine monitoring programmes for pesticides in honey can assist in the prevention, control and reduction of pollution of the environment and minimise risks to health. However, more rigid approaches to the management of pesticide along the supply chain are necessary. These include training for all individuals concerned with specified objectives, audit schemes on bee farms, assisting bee farmers to reduce the risks of contamination, understanding of legal requirements and specifications in domestic and international trade as well as cooperation between competent authorities and exporting countries. The reduction of pesticide use, in particular neonicotinoids is essential as bee and other pollinators are at high risk. With dwindling bee populations across the globe, the long-term production of honey that is sustainable and safe for human consumption will require agriculture authorities, policy makers and epidemiologists to intervene rapidly as the supply of honey may be threatened.

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Thursday 22 December 2022

Lupine Publishers | Cancer: Our Body’s Global Warming Warning

 Lupine Publishers | Journal of Oncology and Medicine


Monday 4 February 2019 was yet another World Cancer Day. We celebrated our efforts to find and eradicate cancer without admitting to ourselves that cancer isn’t a single disease, but rather a group of diseases all caused by our bodies responding to the toxic environment we are exposing ourselves to, just as our planet is responding to the toxic wastes we are dumping into it. The question is WHY do our bodies run amok. The answer lies in the same answer to why Global Warming exists. It is the untold unimaginable continued damage and destruction we cause to the world and to ourselves that accounts for the destruction of the planet and the development of cancer within us. While some individuals may have a genetic predisposition [1] for certain types of cancers, it is nonetheless this constant bombarding of ourselves with toxins, which our bodies try to react to and when overwhelmed the climate change of our bodies occur and call for the eradication of first the damaged cells and then eventually ourselves. Just as the continued bombardment of our Earth with environmental toxins is resulting in violent changes on the planet in an attempt to respond to and eradicate the cause of the toxins to the plant, so too are our bodies reacting to try to eradicate these toxins and their effect within our bodies. This process on a biological basis is reflected in how the cells of our body respond to the cellular environment as shown in Figure1. The process of developing cancer or not developing cancer is not a sudden change but rather a transitional series of events resulting from the interaction between the specific responses and expression of the genome of the cell involved and the specific environment in which the cell is immersed. This environment includes both carcinogenic and non-carcinogenic factors. As the insults occur, the cellular mechanisms to respond to those insults include a variety of responses, including cellular repair and immunologic reactions. The outcome is determined by the interaction between these two opposing sets of factors. Progression or regression is determined by these responses. Clearly no one wants to have cancer or for the planet to be destroyed. Confusion comes from not knowing what is helpful and what is hurting us. Until recently [2], we have been limited by testing (qualitative imaging, biomarkers, etc.), which at best can only provide a yes/no answer to the question of whether a person has cancer or not. Many of these tests do not even provide a yes/no answer, but rather infer there could be a problem. These qualitative approaches are unable to provide us with information warning us that these transitional changes are happening [3]; changes which we could act upon if we only knew they were occurring. Changes which when measured could be used to determine if a given treatment is working [4], harming us or having no effect. Measurable changes which can show us cancer in its early stages [5]. These transitional changes can now be measured using FMTVDM [2]. The only question is whether we use this tool to help find these transitional changes and guide our treatment regimens or whether we will continue to pretend that what we are doing is working?

Figure 1: Quantification of the “Health-Spectrum” for Cancer.


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Wednesday 21 December 2022

Lupine Publishers| Digit Ratio and Soccer

 Lupine Publishers| Journal of Orthopedics and Sports Medicine


The ratio between the lengths of index finger in relation to the length of ring finger of a palm is noticed to as second to fourth digit ratio. Digit ratio tented to shows the quantity of male hormone, to which an individual is exposed in the womb of the mother. Several investigations establish the negative relationship between lower digit ratio and various sports performance as the lower digit ratio settle the high prenatal testosterone hormone. The exposure of prenatal androgenisation masculinizes the human body that impacts the efficiency of sports. Soccer player perceives numerous situations and makes instantaneous decisions. Experienced players are reading the game and anticipating the next move of the opponent. Previous studies have demonstrated the link between cognitive ability and the digit ratio. Similarly, players have numerous cognitive skills to dictate soccer game such as spatial intelligence, awareness, and visual spatial ability. Here, we explore the possible causes of negative associations between lower second to fourth digit ratio and soccer performance. We also think soccer-specific skill performance is likely to be associated with lower digit ratio.

Keywords: Digit ratio; Testosterone; Estrogen; Soccer


The peripheral blood cannot be extracted in utero from fetuses [1]. The second to fourth digit ratio (2D:4D) was therefore suggested as a prenatal testosterone marker [2]. Manual second to fourth digit ratio (2D:4D) at the end of the first trimester of pregnancy is believed to be a biomarker of the balance between prenatal testosterone and prenatal estrogen hormone [2-4]. Thereafter, the digit ratio (2D:4D) probably remain unchanged throughout the life [2]. However, Manning (2002) indicated that the digit ratio (2D:4D) was set particularly between week 8 and 12 at the end of the first trimester. The male fetuses mainly produce large quantities of testosterone hormone, primarily from their testis and adrenal glands [5]. This influences brain and other organ systems development [2-6]. In general, the length of index (2D) and ring fingers (4D) in women is about the same (digit ratio=1.00), whereas in men the ring finger is generally slightly longer (digit ratio=0.98) [7]. The length difference between the two digits is higher for men than for women [8-9]. Testosterone influences the growth of the ring finger (4D), whereas estrogen exposure stimulates the growth of the index finger (2D) [7]. The ratio of the index finger to the ring finger (2D:4D) has been shown to be a sexually dimorphic trait [9-10]. Additionally, the ratio of digits (2D:4D) measured by the length of index finger divided by the length of ring finger [7-11]. Therefore, researchers found an index of prenatal testosterone exposure relative to prenatal estrogen exposure [7-12]. The prenatal androgen is likely to be increase, if the digit ratio goes down [7]. Several studies portray the second to fourth digit ratio in which prenatal testosterone hormone was associated. The researches recommend that the lower ratio of digit is a noninvasive feasible indicator for sport success rate Manning and Taylor [13] ; Manning and Hill [14] ;Manning et al. [8]; Hone and McCullough [15] ; Longman et al. [16]; Sudhakar et al. [17]; Sudhakar et al. [18]; Bennett et al. [19]; Kim [20]. As because, adult lower digit ratio (2D:4D) promote the masculine feature [2]. The testosterone (T) is a steroid hormone that develops and maintain masculine feature of human body [21]. The specific aim of this review study was to explore the relationship between digit ratio (2D:4D) and soccer performance.

How does the digit ratio (2d:4d) fixed?

The adult finger length ratio is becoming a widely used research tools to know the tentative trait of prenatal androgens, a diversity of physiological and psychological conditions, athletic ability and sexual orientation [10,22,23]. The differentiation of gonads, fingers, and toes is influenced by HOXA and HOXD genes. HOXA and HOXD genes are also necessary for finger length development and differentiation [24]. Congenital Adrenal Hyperplasia (CAH) is an anomalous hormonal environment that does not function correctly with the adrenal glands [25]. The 21-hydroxylase deficiency, results in the production of surplus quantities of masculine hormones by the adrenal glands [26]. However, researchers Okten and his colleagues studied digit ratio (2D:4D) and 21-hydroxylase deficiency in male (right palm) patients and reported lower digit ratio confirm the 21-hydroxylase deficiency than female and male controls. Women with CAH had a much lower second to fourth digit ratio than women without CAH on the right hand and on the left hand, men with CAH had a much lower digit ratio (2D:4D) than men without CAH [27]. Similarly, researchers [28] reveal the relationship between low digit ratio and CAH. This characteristic also supports a combination of low digit ratio and elevated Fetal Testosterone concentrations [29].

Relationship Between Digit Ratio (2d:4d) With Sports Performance

Researchers [20] widely reviewed the most correlational studies and postulated that low second to fourth digit ratios (high prenatal testosterone and low estrogen hormone) could be a determinant of high sport performance. However, the high performance of rugby depends on low digit ratios [19]. The researchers also discovered differences in the low right-left digit ratios to be a determining factor in elite rugby performance. Keshavarz and his team (2017) studied on three male groups of Wrestlers; they are:

a) World class elite Greco-Roman wrestlers.
b) Collegiate non-elite wrestlers.
c) Sedentary age matched control.

The lower right- and left-hand digit ratios of world class wrestlers were predictors of high wrestling performance compared to other groups [30]. The achievement of the competition phase in team sports was also associated with the ratio of digits (2D:4D). The second to fourth digit ratio was therefore likely to have an impact on the possible athleticism [31]. Similarly, lower digit ratio (high prenatal androgens) has been shown to indicate the sport performance of soccer, surfing, sprinting, endurance, hand grip strength, rowing, kabaddi, swimming, Tennis [8,13-18,32].

Digit Ratio as Soccer Performance Determinant

High prenatal testosterone and low prenatal estrogen hormones are likely to be a strong predictor of soccer performance [7]. Competitive achievement is a major objective of soccer in connection with prenatal androgenization [31]. This prenatal situation influences the judgment of the visual perception [13]. Therefore, according to [7], “Striking a moving opponent or ball requires fine judgment of distance. Determining the exact point of impact demands an accurate perception of the surface of the target as it moves through space” (p.128). However, researchers studied on different types of soccer players and noticed ‘professional’, ‘International’ and ‘1st team players’ had lower digit ratio (2D:4D) than the ‘control group’, ‘youth team’ and the ‘players who had not represent their country’ respectively [13]. Similarly, the International presence of the player in a match is greater for the lower digit ratio individuals [7]. The lower digit ratio could therefore provide an additional discriminator to help estimate soccer capability. Prenatal testosterone exposure also influences professional soccer players’ aggressive behavior. Researchers indicated that exposure to adult and prenatal testosterone detects the number of fouls per match that confirm the aggressiveness of players [33]testified by a low second-to-fourth digit ratio (2D:4D . Furthermore, aggression guarantees the dominant behavior that is essential in competitive sport.

Association among Digit Ratio, Visual-Spatial Ability and Left Handedness

Digit ratio is a putative indicator of sport performance differences [34]. A study concerning several sports related psychological variables (mental toughness, aggression, optimism scale, coping strategies, and goal orientations) with masculine digit ratio reported high scores of optimistic dispositions than those with feminine digit ratio. The study also claims that mental toughness partly determined on gestation period [34] that benefited for gender, age and sporting experience [35]. Mental rotation score test [36] can measure the visual-spatial intelligence [36-38]. Manning and Taylor found negative association between lower digit ratio and high mental rotation scores in males. So, the visual spatial intelligence may partially develop on intrauterine life [13]. High prenatal testosterone exposure is likely to associated with handedness [39,40]. Left-handed people dominated by the right hemisphere and assists visual spatial ability [41]. Interestingly, androgenisation exposure influence the right palm more than the left palm [27,42,43]. Right palm digit ratio is also significantly connected with several psychological and behavioral traits compared to the left palm digit ratio [43].

Cognitive Abilities Influence on Soccer Performance

Most team sports, particularly in soccer players, need to pursue numerous situations that are changing quickly [44]. Elite players perceive the situations and make the appropriate choice at the right moment [45,46]. Therefore, technical and tactical ability influence the outcome of the match [47,48]. Elite players perform the technical and tactical skills better in compared non-elite counterparts [49]. However, researchers postulated that distinguished correlations in male between more masculine digit ratio (lower digit ratio) and in visual-spatial ability [13,22]. On the other side, females have prone to more feminine digit ratios (higher second to fourth digit ratio) should relate to higher scores for depression (Repeat). Therefore, high prenatal testosterone exposure is likely to be predictor of soccer performance as well as cognitive ability [13]. Research also shows that human behavior and the status of cognition can result from prenatal androgenization [50].


Most correlational study reveals the negative relationships between lower digit ratio and sports performance. Low second to fourth digit ratio (2D:4D) can be an indicator in scouting potential athletes especially soccer players. In multifaceted aspects, lower digit ratio is likely to be a potential indicator of soccer specific skill performance. Further studies are required to clarify whether lower second to fourth digit ratio could predict the soccer skill performance in multifaceted aspects including passing, dribbling, control, shooting and decision making within a dynamic situation. In addition, we realized that sporting success might be depended on our hands’ fingers length ratio a long with other variables.

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Tuesday 20 December 2022

Lupine Publishers| Variability in Plasma FGF21 Levels in Rats Fed A Standard 15% Protein Diet is not Sensitive Enough to Reflect Differences in Protein Requirements

 Lupine Publishers| Journal of Diabetes and Obesity 


Fibroblast growth factor 21 (FGF21) is a hepatokine member of a subfamily of “fibroblast growth factors” that responds to multiple metabolic stresses as protein deficiency [1-4]. FGF21 is produced in various tissues but the FGF21 circulating form is primarily of hepatic origin [1,2]. FGF21 affects numerous metabolic and behavioural parameters, and in particular, increases appetite for protein in subjects fed a protein-deprived diets [5,6]. In a recent still unpublished study, we observed that plasma FGF21 levels were higher in adult male Wistar rats fed a standard diet, formulated according the AIN93 recommendations for rats’ feed, containing 15% protein by energy [7] than in rats fed a 30% protein diet. In addition, inter-individual variability of plasma FGF21 levels was larger in rats fed the standard 15% protein diet than in rats fed the 30% protein diet. We therefore considered the hypothesis that higher levels and inter-individual variability in plasma FGF21 levels in rats fed a standard 15% protein diet would reflect the variability in protein requirements between individuals and thus, that measurement of plasma FGF21 levels can be used as a simple, rapid, and minimally-invasive test to estimate the adequacy of protein intake.
Dietary self-selection is a method that has been largely used in farm animals and laboratory rodents to study the requirements for macronutrients (carbohydrates, lipids and proteins), vitamins and minerals [8,9]. Many studies using this method, in our lab and others, showed that rats self-selecting between a protein diet and a protein-free diet often ingest up to 30-50% of total energy intake as protein [10-15], so much higher than the level considered as sufficient for an optimal growth in adult rats (10-15% by energy), which comforted our hypothesis that 15% dietary protein was possibly not the optimal dietary content.
The objective of this study was to verify that variability in plasma FGF21 levels in rats fed a standard 15% protein diet was indicative of differences in protein requirements. To this end, we have analyzed the relationship between FGF21 levels, and the level of protein subsequently selected during self-selection between a protein diet and a protein-free diet.

Experimental Procedure

24 adult male rats (215-240g) of the Wistar RccHan strain (ENVIGO) were used and individually housed (22°C ± 1°C, 12/12 L/D, cycle lights on at 08:00). After 1 week of adaptation to the laboratory conditions, the rats were fed for 12 days (Basal period) a standard diet formulated according to the AIN93 requirements [7] that contained 15% protein (15P); then, for 28 days (Choice period), 6 rats (Control group) continued to be fed with the standard diet and 18 (Self-selecting group) were given a choice between a pure protein diet (100P) and a protein-free diet containing a mix of fat (soy oil) and carbohydrate (corn starch and sucrose) in which carbohydrate amounted 60% by energy. The diets were provided, as necessary.

The food pellets were prepared twice a week by mixing the macronutrients, vitamins, and mineral mix with the amount of water required to make a thick dough. Food intake (g/day) was measured twice a week and converted in kJ/day based on the energy content of the diets (Table 1).

Table 1: Composition and energy content of the 3 used diets.


100P: diet containing only proteins; 60C: protein-free diet containing only lipids and carbohydrates and in which carbohydrates amounted 60% by energy; 15P: standard diet containing 15% of protein by energy.

Blood samples (0.5 mL) were collected from the tail vein in EDTA tubes: once during the basal period and once during the choice period. Blood collection was made in the morning (10:00- 12:00) in rats that were not previously fasted. Blood samples were centrifuged (5000g, 15min, 4°C) and the plasma stored at -20°C. Plasma FGF21 levels (pg/ml) were measured by ELISA tests using commercial kits from Bio Vendor (Mouse/Rat FGF-21 ELISA RD291108200R).

Statistical Analysis

Statistical tests were performed using RStudio software, 2015. Changes in protein intake and plasma FGF21 level were compared using mixed two-factor ANOVA tests (parameter ~ group*period), which were followed by the main effects analysis by Bonferroni adjusted pairwise comparisons. Values are presented as means ± standard error of the mean (SEM). Linear regression analysis was used to study the link between plasma FGF21 levels during the basal period and protein intake during the choice period and was performed using Excel software. Significance of correlations was assessed using the Pearson correlation coefficient. A threshold of P≤0.05 was chosen as significant.

Results and Discussion

Protein intake was similar between the control and selfselecting group during the basal period but increased by 80% in the self-selecting group during the choice period (+37.8 kJ/d, p<0.0001) (Figure 1). This response significantly increased the contribution of protein to total energy intake from 15.0% to 23.5% (p<0.001). Mean plasma FGF21 levels averaged ~1,100 pg/mL in both groups during the basal period and decreased to 131 pg/mL in self-selecting group during the choice period (P<0.001) (Figure 2). Finally, contrary to our hypothesis, not only did we not observe a positive correlation between plasma FGF21 levels during the basal period and protein intake during the choice period, but instead we observed a weak and inverse correlation (Figure 3).

Figure 1: Protein intake (kJ/d) according to diet group and period.
(*:0.05; **:0.01; ***:0.001; ****:0.0001) Values are represented as means ±SEM, only the p-value of the interaction of ANOVA tests are indicated.


Figure 2: FGF21 level in plasma (pg/ml) according to diet group and period.
(*:0.05; **:0.01; ***:0.001; ****:0.0001) Values are represented as means ±SEM, only the p-value of the interaction of ANOVA tests are indicated.


Figure 3: Protein intake (kJ/d) during the choice period as a function of plasma FGF21 levels during the basal period in the self-selecting group.



In conclusion, inter-individual variability in plasma FGF21 levels in rats fed a standard 15% protein diet did not appear to be a parameter sensitive enough to reflect inter-individual differences in protein requirements. Therefore, plasma FGF21 level cannot be used as a test to determine inter-individual variability in protein requirements in individuals. Nevertheless we observed that plasma FGF21 levels in P15 fed rats were ~7 fold higher than in selfselecting rats ingesting 23.5% protein, which points on the fact that changes in plasma FGF21 levels are very sensitive to dietary protein intake, even when protein intake is well above essential protein requirements (~8-10 % in adult male rats).

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Monday 19 December 2022

Lupine Publishers| The Global Scope of Sudden Cardiac Death

 Lupine Publishers| Journal of Cardiology Research & Reports


Frequently there are people who, directly or indirectly, have suffered the loss of a loved one in circumstances that make it more surreal and painful; There are instances of individuals who, being well, on the street, will abruptly fall to the ground and cannot receive medical assistance; or that being young, healthy and strong, like some athletes, die without previous signs, leaving behind an impact of recognized importance on their friends and family. This phenomenon or process, known as sudden death (SD), as it occurs, is characterized by being a natural type of death, as it is not related, in its cause, to any violent event; it is unexpected in its presentation and it is quick in its installation, since it takes a short period of time from the start of the demonstrations, if any, to the diagnosis and certification of death. Sudden death can be related to diseases that cause it by affecting various systems; in the case of sudden cardiovascular death (SCVD) it is a natural death from cardiac causes, preceded by sudden loss of consciousness, which typically occurs within 1 hour after onset of acute symptoms, in an individual who is known to present a pre-existing heart disease, known or not by the patient, but in which the time and mode of death are unexpected. In the case of not being witnessed (it occurs in two thirds of the cases) it is considered sudden if the victim was seen alive 24 hours prior to the event. If life is maintained thanks to the use of artificial devices, the time from the moment of putting the patient under these supports is considered [1].
It appears, as a recognized entity, in the International Classification of Diseases and Health-Related Problems (ICD- 10), under different codes: (I.46.1: Sudden cardiac death, thus described; R95: Sudden infant death syndrome, (R96: Other sudden deaths of unknown cause) and represents, according to experts, one of the main challenges for healthcare systems in this century, this statement is justified by its high incidence [2]. It is known that cardiovascular deaths in global statistics represent an important cause of death worldwide, affecting age groups and gender [3]. But in the case of the SCVD, such is its magnitude that between 4 and 5 million events occur annually worldwide which means 10 events for every minute spent reading these lines [4]. In the United States, the SCVD becomes the first victimizer of the population, causing 400,000 deaths annually, with an incidence that exceeds deaths caused by cerebrovascular diseases, lung cancer, HIV-S IDA infection and breast cancer, just for name a few ones 1. In Cuba, based on the research work carried out during the last 25 years by the Research Group on Sudden Death (GIMUS) and the information published by the statistical yearbook of the Ministry of Public Health (MINSAP), is estimated the occurrence of 8021 sudden events, for 2019 which means 2 deaths per day and 1 episode every 65 minutes, with a rate of 71.6 x 100, 000 inhabitants representing a 7.8 % of natural deaths in that year [5]. But the great impact of the figures presented, has added by an element of a family, social and economic nature that transcends the personal sphere; is about the drama in its presentation, since it constitutes an important cause of years of life potentially lost when the event occurs abruptly, in subjects, often young, apparently healthy, in working-age groups [6].
Another element to consider in the approach to the global nature of the SCVD lies in aspects that clearly make it difficult for the scientific community to do projections (despite not escaping to any region of the world with that high number of deaths), including data and studies that make it possible to standardize the analyzes and the actions, since there is no total consensus regarding its definition and which is worse, although derived from its statistical registry. Special mention for the need to standardize the study of the marker, predictor and triggering factors that exist as well as that of coronary arterial disease, due to their etiological importance in this death [7].
The treatment of this health problem worldwide, based upon its etiopathogenical complexity and the diversity of population groups in which it occurs, goes beyond the field of study of any particular discipline, based on the fact that the relationship between disciplines in medicine is part of the disciplinary interaction between the sciences. This unquestionably leads us to Piaget, who points out that “interdisciplinarity ceases to be a luxury or an occasional product to become the very condition of progress” [6]. The complex nature of the problems currently facing the medical, science requires coherence of the knowledge - based on an interdisciplinary approach of problems from different areas of expertise to achieve their solution. This is a great challenge to face SCVD, resort to interdisciplinarity, which must be seen and understood as a process that allows to resolve controversies, exchange criteria, collate and evaluate contributions, integrate data and even reach new definitions; thus interdisciplinary cooperation is ultimately the rational alternative for addressing this and other issues, which go beyond the limits of traditional specialties.
An increase in the incidence and prevalence of cardiovascular diseases in the coming decades worldwide, forces us to review the current approaches from all the aforementioned aspects, without ever renouncing the strength that intersectorality represents. The circumstances surrounding intersectorality in the health sector are, to a large extent, present in the theoretical and practical approach that is needed to deal with sudden death; the gap between them is difficult to navigate, since in the first place it is necessary to achieve a clear definition by the health sector of the specific weight that each sector has in the epidemiological situation as a consequence and result, in order to properly insert them into the strategy and action plan, through programs and projects. The domain that the health sector has of the problem and its solutions is vital to achieve the participation of the other sectors, in addition to bearing in mind the importance of the participation of the sectors from the beginning, in the identification of problems in order to achieve a comprehensive reaction to them.
Here is the great challenge.

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Saturday 17 December 2022

Lupine Publishers | Downs and Ups of Decompression Diving: a Review

 Lupine Publishers | Journal of Oceanography and Petrochemical Sciences


The question of deep and shallow decompression stops is interesting and fraught with controversy in diving circles and operations, training, exploration and scientific endeavors. Plus, fraught with some misunderstanding which is understandable as the issues are complex. We accordingly detail a short history of deep and shallow stops, physical aspects, staging differences, diving tests, models, data correlations, data banks, diver statistics and DCS outcomes for diving amplification. Pros and cons of deep stop and shallow stop staging are presented. Misinformation is corrected. Training Agency Standards regarding deep and shallow stops are included. A tabulation of well-known and popular dive computers and software algorithms is given. From diving data, tests, DCS outcomes, data banks and field usage, we conclude that both deep stops and shallow stops are safely employed in recreational and technical diving today. For diver safety this is important.

Keywords: Data banks; models; risk; correlations; tests; validation; profile downloads; statistics


Real diving and decompression protocols presently accommodate both shallow and deep stop staging safely judging from recent experiment, data and collective diver outcomes. The record of tables, meters and diveware is a safe and sane one in both instances. One approach (shallow stops) treats the bubble and the other (deep stops) controls the bubble. Staging is thus a mini-max problem of doing both (eliminating dissolved gas and controlling bubble growth) optimally. Analyses are evolving and bubble models (BM) seem the best hope to accommodate both safely and sanely. Dissolved gas models (GM) are 100 yrs old and dynamically incomplete though devotees today apply patches such as GFs, Pyle stops, variable M-values or R-values and variants to deco schedules to mimic bubble behavior. Bubble models reduce to dissolved gas models in the limit of little phase separation (NDL diving). Let’s take a closer look at both deep stops and shallow stops, models, history, data and scattered tests.

Shallow Stops

Haldane was commissioned in 1908 by the Royal Navy to investigate the problem of human air decompression by subjecting goats to high pressure and devising safe ascent protocols. Using tissue compartments in the halftime range 5-40 min and exponential tissue equations for dissolved gas buildup he suggested that safe decompressions from any depth need only restrict gas buildup across all compartments to twice the ambient pressure to allow safe ascent. This was called the 2 to 1 law. Later it was determined that the ratio need be increased and that each tissue compartment had its own ratio. Switching from ratios to permissible gas loadings in each compartment called M-values the staging algorithm evolved and changed in time mostly drive by Navies [1]. Limiting dissolved gas buildup by M-values using exponential tissue functions resulted in a staging strategy that always tried to bring the diver as close to the surface as possible (GM). The stop structure is consequently shallow across all tissue compartments which across the years has had an extended halftime range 5-240 min. Analyses and diving wet tests resulted in new DBs with requisite M-value modifications to accommodate diving trials and DCS outcomes [2-4]; Hennessy and Hemplmen [5] Walder [1,6]. Extension to helium mixtures followed in lockstep [7]. Shallow stops thus relate directly to Haldane and dissolved gas models (GM) used for staging over a span of a century or so [2,8,1,3]. Shallow stops have been extensively tested and validated since 1908 forming the nexus of diver staging until roughly the 1960s when open water and laboratory tests strongly suggested alternative staging and diving protocols. The history of testing and GM algorithm modifications is extensive since the time of Haldane and interesting in wet testing scope.

Submarine Escape Trials: In 1930, USN submarine personnel suggested that Haldane’s 2 to 1 law was too conservative. Some 2143 dives were performed over 3 years and revaluation of the data resulted in higher decompression ratios for the fast compartments while the slower compartments stayed close to the Haldane limit.

USN Exceptional Exposure Tables: The standard USN Tables in 1956 were found to be problematic for deep dives to 300 fsw for long bottom times in the 2-4-hour range. To address this problem, the USN [1] introduced an 8 compartment Haldanian (GM) model with halftimes ranging 5-240 min and no repetitive diving allowed. The compilation addressed many of the shortcomings of earlier dissolved gas models and DB fits for deep and long decompression diving on air and helium. This work is monumental in diving importance.

Early Doppler: Ultrasound studies in 1970s portended the era of Doppler measurements to follow. Reductions in air NDLs [9] were published and implemented in tables of the time. Interestingly, Doppler also suggested that deep stops reduced bubble counts dramatically [10,11] also portending the upcoming deep stop evolution and bubble model growth and meter implementation.

VVAL18 Compilation: The recent VVAL18 synthesis [12] by USN investigators is both a massive undertaking and update to USN diving data and operational protocols. With a data base of many 1000s of dives, Thalmann correlated a linear-exponential model (LEM) to data [12] and all present USN Tables and protocols are based on it. Some impetus for this undertaking was a need for safe constant ppO2 staging regimens after traditional GM approaches proved unsafe. The USN LEM is an exponential gas uptake and linear gas elimination GM model whereas traditional GM algorithms are exponential in both gas uptake and elimination. Linear gas elimination is slower than exponential gas elimination. In marketed dive computers, the same effect of slowing outgassing can be accomplished by increasing tissue halftimes whenever instantaneous total gas tension is greater than ambient pressure in what is called the asymmetric tissue model (ATM) [13,14]. Longer tissue halftimes slow outgassing resulting in increased dissolved gas loadings and subsequent decompression debt. Asymmetric gas uptake and elimination can be applied to any GM or BM protocol with the same result. In the case of BM algorithms, slower outgassing contributes to bubble growth with increasing decompression requirements. A later impetus was the need for a USN dive computer for SEAL Team operations and recorded higher incidence of DCS in very warm waters. This compilation of shallow stop data is a very important undertaking in recent diving history.

Deep Stops

Deep stops track more recently to Hills and phase models (BM). Haldane as mentioned above also found that deep stops where necessary in his early tunnel work Golding. It was real diving that initially tweaked interest in deep stops which was something of heresy before the1960s.

Australian Pearl Divers: Pearling fleets operating in the deep tidal waters off Northern Australia employed Okinawan divers who regularly journeyed to depths of 300 fsw for as long as one hour, two times a day, six days per week and ten months out of the year. Driven by economics and not science these divers developed optimized decompression schedules empirically even with the sad loss of 1000s of lives. What a wet test. As reported and analyzed by LeMessurier and Hills, deeper decompression stops but shorter decompression times than required by Haldane theory were characteristics of their profiles [15]. Recorders placed on these divers attest to the fact. Such protocols are consistent with minimizing bubble growth and the excitation of nuclei through the application of increased pressure. Even with a high incidence of surfacing decompression sickness following diving, the Australians devised a simple but very effective in-water recompression procedure. The stricken diver is taken back down to 30 fsw on oxygen for roughly 30 min in mild cases, or 60 min in severe cases. Increased pressures help to constrict bubbles while breathing pure oxygen maximizes inert gas washout (elimination). Recompression times scale as bubble dissolution experiments in the lab [16] which is extraordinary.

Hawaiian Diving Fishermen: Similar schedules and procedures evolved in Hawaii among diving fishermen according to Farm and Hayashi [17]. Harvesting the oceans for food and profit, Hawaiian divers make between eight and twelve dives a day to depths beyond 350 f sw. Profit incentives induce divers to take risks relative to bottom time in conventional tables. Repetitive dives are usually necessary to net a school of fish. Deep stops and shorter decompression times are characteristics of their profiles. In step with bubble and nucleation theory, these divers make their deep dive first, followed by shallower excursions. A typical series might start with a dive to 220 fsw followed by two dives to 120 fsw and culminate in three or four more excursions to less than 60 fsw. Often little or no surface intervals are clocked between dives. Such types of profiles literally clobber conventional GM tables but with proper reckoning of bubble and phase mechanics acquire some credibility. With ascending profiles and suitable application of pressure, gas seed excitation and bubble growth are likely constrained within body capacity to eliminate free and dissolved gas phases. In a broad sense, the final shallow dives have been tagged as prolonged safety stops and the effectiveness of these procedures has been substantiated in vivo (dogs) by Kunkle and Beckman [18,19]. In-water recompression procedures similar to the Australian regimens complement Hawaiian diving practices for all the same reasons. Australian and Hawaiian diving practices ushered in a new era of diving practices especially deep stops and related protocols. And this diving was real world and certainly not academic in scheduling. The early thermodynamic model (TM) of Hills played heavily in analyses of these dives as published and analyzed in excellent sources [20] Hennessy and Hempleman. Profile and model comparisons can be seen therein. As might be expected this caused quite a stir then with opposition almost religious in some quarters. That is certainly strange when you look at the collective practices of pearl and fishing deep divers applying the diving idiom “what works, works” [3,21].

Open Ocean Deep Stop Trials: Starck and Krasberg in open ocean conducted a series of important deep stop tests [22]. In deep waters in over 800 dives for up to an hour and down to 600 fsw they recorded only 4 DCS cases. Extensions to 800 fsw followed. This effort was part of a massive program to test new RB designs. The impact at the time was notable and still is today across the full spectrum of diving.

Recreational 1/2 Deep Stops and Reduced Doppler Scores: Analysis of more than 16,000 actual dives by Divers Alert Network (DAN) prompted suggestions that decompression injuries are likely due to ascending too quickly [23]. Bennett found that the introduction of deep stops, without changing the ascent rate, reduced high bubble grades to near zero from 30.5% without deep stops. He concluded that a deep stop at half the dive depth should reduce the critical fast gas tensions and lower the DCS incidence rate. Earlier Marroni concluded studies with the DSL European sample with much the same thought [24]. Although he found that ascent speed itself did not reduce bubble formation, he suggested that a slowing down in the deeper phases of the dive (deep stops) should reduce bubble formation. Both have been conducting further tests along those lines. The Bennett and Marroni findings were formally incorporated into NAUI Recreational Air and Nitrox Tables [25] for both conventional USN and No Group RGBM Tables. The recreational regimen adopted for nonstop and light decompression diving in the NAUI Tables is straightforward and simple:

1) make a 1 min stop at 1/2 bottom depth;
2) make a 2 min stop at 1/4 bottom depth and if necessary and deeper than 160 fsw;
3) make a 3 min stop at 1/8 bottom depth and all 1/2 deep stops made within any requisite light decompression schedules Shallow safety stops [21] are also made inside the deep stop recreational regimes. Obviously shallow safety and 1/2 deep stops can overlap in the 20-30 fsw range.

Trondheim Pig Decompression Study: Brubakk and Wienke also found that longer and shallower decompression times are not always better when it comes to bubble formation in pigs [26]. They found more bubbling in chamber tests when pigs were exposed to longer but shallower decompression profiles, specifically staged shallow decompression stops produced more bubbles than slower (deep) linear ascents. RGBM model predictions of separated phase under both types of decompression staging correlated with medical imaging. Correlation of models and test data are always sought in real life and diving is an important case.

Duke Chamber Experiments: Bennett and Vann used a linear diffusion (TM variant) model to improve stops in a dive to 500 fsw for 30 min which proved DCS free in chamber tests at Duke [27]. The early TM of Hills however at the time suggested dropout in the shallow zone which was troublesome in tests and was later modified with additional shallow decompression time. BMs today while making necessary model deep stops also require time in the shallow zone (10-30 fsw). Unfortunately, premature dropout in the shallow zone may have discredited deep stop models especially the TM. That doesn’t happen anymore in bubble models.

ZHL and RGBM DCS Computer Statistics: An interesting study by Balestra of DAN Europe (DSL) centered on DCS incidence rates in dissolved gas, shallow stop (ZHL) computers versus bubble model, deep stop (RGBM) computers [28]. In 11,738 recreational dives, a total of 181 DCS cases were recorded and were almost equally divided between the ZHL and RGBM computers, that is, the ZHL incidence rate was 0.0135 and the RGBM incidence rate was 0.0175. Clearly both RGBM and ZHL computers are nominally safe at roughly the 1% DCS level in this wet test. DCS rates for both computers, however, are higher than published DAN recreational rates nearer 0.1% or so.

Pyle Stops: Richard Pyle is a diving fish specimen collector out of the Bishop Laboratory at the University of Hawaii who pioneered the ad hoc practice of making deep stops at multiples of half the bottom depth for a few minutes or so. Stops were interposed on standard deco regimens like the USN, ZHL, VPM and RGBM for a minute or so at the first half stop and up to a few minutes at successively shallower later stops at 1/4, 1/8, etc multiples of the bottom depth. These stops were made on top of any requisite deco stops. Except for recreational diving, nothing has been tested or correlated for this half stop approach, but Pyle apparently uses the protocol safely in his fish collecting ventures. In the recreational arena, the Doppler tests of Bennett and Marroni described above certainly support the Pyle half stop approach. For this reason, technical divers quickly adopted and extended Pyle half stops across mixed gas, OC and RB deco diving. It remains one of the few apparently successful ad hoc deep stop procedures backed up with some Doppler measurements.

Computer downloads

Computer downloaded profiles serve as a global set of diving outcomes across all diving venues and provide statistical data that can never be reproduced in chambers, wet pods and open ocean testing because of cost and diversity. The low incidence rates in these collections suggest that divers on computers are not at high risk, DCS and oxtox spikes are nonexistent, models and algorithms are safe and divers are using them sensibly [29].

LANL DB: With a low prevalence of deep stop DCS hits in the LANL DB (28/3569), some regard the downloaded profiles as a wet test of real OC and RB diving. While low incidence rates are beneficial to divers, low incidence rates make statistical analysis more difficult. With the incidence rate so low in the LANL DB, the (low p) Weibull function [30] is a more economical descriptor of the bends distribution than the canonical binomial distribution. The DCS incidence rate in the LANL DB is 28/3569, less than 1%.

DAN DB: Like the LANL DB the massive DAN DB can also be regarded as an extended wet test for air and nitrox diving. Mixed gas and altitude profiles are also being included at last reading. With a low incidence rate (80/18745) the DAN DB underscores the relative safety of recreational air and nitrox diving. Both GM and BM profiles are stored. The collection obviously grows daily under ambitious collection of computer profile downloads with DCS outcomes by DAN and DSL.


Model correlations and validation

With paucity of DCS outcome data across the full spectrum of diving it is very difficult to validate decompression models. Unlike scientific experiments in a laboratory under controlled conditions diving varies across OC and RB systems, gas mixtures, altitude, physiological and environmental factors, depth and time and each has its own set of subtle impacts on the diver. There will never be enough time and money to characterize diving outcomes across the full spectrum of possibilities, but some testing and model correlations have been useful over limited ranges of diving as described in the foregoing. As seen the only correlated and validated models are USN, ZHL, VPM and RGBM.

Staging pros and cons

The full ascent schedule of any deco strategy is equally as important as the first stop and in fact must be consistently followed after the first stop using models or protocols tuned to global diving data and not just isolated and disjoint experiments or tests. This is the problem with tests that arbitrarily interpose a deep stop some point on a schedule, continue with the rest of the schedule (usually dissolved gas) and get widely varying Doppler counts and outcomes. It is simply a question of staging consistency and not disjoint experiments and ad hoc stop insertions. Of course, to have a consistent ascent strategy (first stop plus ascension levels) you need a correlated model. Not GFs or Pyle stops. Random deep stops inserted into shallow stop schedules are inconsistent and of little use for staging analysis except to say “don’t do this” when something happens. Some of the early and later deep stop tests suffer in this respect (Pyle, French Navy, Spisni, Ljubkovic just for example). It is hit or miss as far as gas transport is concerned and not always consistent further up the ascent schedule. One chamber or wet pod test of a profile is not necessarily definitive against the full spectrum and set of actual mixed gas, OC and RB, altitude and sea level, deco and nonstop diving outcomes and it does not follow that all other diving is the same. One test is not the whole of diving and is thus differential not integral as needed in experimental science (French Navy, NEDU, Ljubkovic, Spisni again just for example). This is why DAN, DSL and LANL use the global approach (as many diving profiles and DCS or Doppler outcomes as possible across all diving) in constructing optimal ascent strategies (models, tables, software). Such requires high powered computers and sophisticated statistical software not always accessible [31,32].

Published results of deep stop Doppler scores vary all over the map and are not necessarily indicative of DCS stress [33,34]. Same of course said about shallow stops. Across all staging regimens, Doppler correlates weakly with DCS incidences excepting limb bends (maybe). Thus, DCS outcomes as a final metric appear superior to Doppler counts for developing ascent staging procedures and correlating models. Not that high Doppler scores are being dismissed here. Of course, DCS varies all over the body making things more complicated. But DCS outcomes are the bottom line on staging no matter what disjoint and scattered wet and dry tests claim about diving in general. Such is the approach taken in real operational diving quarters and used to fabricate diving regimens and tables from basic and complete staging models. Shallow stops are basically medical contraindications while deep stops come from laboratory studies and bubble model correlations fitted within medical inventions. Both certainly work safely as witnessed by the plethora of deep stop and shallow stop tables, meters, software and dive protocols utilized by divers at all levels over many years. Here (LANL) we have many thousands of deep stop and shallow stop computer downloaded profiles and DCS outcomes with the overall incidence rates of both below 1%. That is good for divers but not necessarily statistics. To cure some of the statistical limitations, packages that are built on low DCS incidences (low p) are used and helpful. Focus is operational diving and the need to get a job done safely and timely outside and independent of conflicting opinion, tests, Doppler, models and arbitrary rules. Again, such requires high powered computers and attendant statistical software.

To say bubble models have not been tested and validated is false. Differential chamber tests certainly are absent in number but deep stops and bubble models (VPM and RGBM) have been validated and correlated over the past 20 yrs using computer downloaded profiles from DBs and comparative results published [35, 13,14]. Tests support their viability as well as Agency testing for training purposes [25]. Deep stop tests and correlations are fewer in number than shallow stop tests but are growing. And the collective experiences of divers using deep stop tables, meters and software cannot be easily discounted today. Literally millions of deep stop dives across technical and recreational diving pay witness to their safety and utility. Certainly, deep stops and bubble models are under the microscope today and that is a good thing. One interesting issue for bubble models is the question of bubble regeneration and Ostwald bubble growth (broadening) [36-38] and impacts on decompression schedules. Initial estimates suggest that both increase decompression debt.

Some recent test pros and cons

Keeping in mind that single test profiles are differential across all diving that Doppler is not necessarily definitive and that arbitrary insertions of deep stops on shallow stop staging are ad hoc and can be inconsistent (and vice-versa) some further test specific comments are interesting we hope. The following pop up in various Training Agency publications, online blogs and technical diving forums.

Keeping in mind that single test profiles are differential across all diving that Doppler is not necessarily definitive and that arbitrary insertions of deep stops on shallow stop staging are ad hoc and can be inconsistent (and vice-versa) some further test specific comments are interesting we hope. The following pop up in various Training Agency publications, online blogs and technical diving forums.

Ljubkovic VPM Bubble Study: The Ljubkovic test [39] looked at the VPM to assess Doppler bubble formation and noted high bubble incidence using VPM the study returned null results for VPM because it was not comparative against a shallow stop model which may or may not show less bubbling.

Spisni ratio deco test

The Spisni study [40] is another test of R-values in a shallow stop model with arbitrary deep stops imposed. From a bubble model perspective, there is little learned in either case unless a bubble model profile is tested against the modified dissolved gas profile. The same comments hold for GF reductions of Buhlmann critical parameters and tests. Comparing one R-value deep stop profile against another R-value deep stop profile says nothing about deep stops in general especially when they are arbitrarily inserted. Comparing apples to apples is not the same as apples to oranges.

Ratio Deco: Despite new name, ratio deco is nothing more than M-value deco in an equivalent representation, R, namely M-value divided by absolute pressure P, that is R = M/P. This was the original Haldane model with R = M/P = 2 changed to variable M-values later and now back again. Ratio deco is still dissolved gas deco with arbitrary deep stops imparted in manner similar to GFs. Nothing is really new here, but R-values are popular with technical divers. Without too much extra work, ratio deco can be extended to the critical gradients or G-values, G=M-P, with R=G/P thereby connecting to gradients factors (GF).

Gradient factors

It is opportunistic that GFs (ξ) mimic bubble models to some extent but why use GFs that are not correlated with any data when correlated bubble models (VPM and RGBM) are available and consistent across the whole dive. Correlation of GFs with RGBM are underway at LANL as a service to the diving community not familiar or not using full up BMs.

Fraedrich Computer Algorithm Comparisons: This study [41] took a closer look at 4 computer algorithms, namely Suunto RGBM, VPM-B, EMC-20 and ZHL, focusing on first stops and total run time. It was important that the Fraedrich study looked at full up shallow and deep stop staging with dissolved gas and bubble model computers. However, using the results of the NEDU 2008 study [42,43] as a baseline is questionable and not well defined as the NEDU study is controversial. The comparisons have some validity and we are looking at the results across profile data in the LANL DB. Studies like this are headed in the right direction.

Equal risk staging

Deep stops are and remain the norm in technical diving because of a record of safe and sane usage in tables, meters and software and no DCS spiking. At the same risk level (computed from profile data and DCS outcomes) deep stops are always shorter in total decompression time than shallow stops. A comparative example is seen in the appended schematic as reported at the Deep Stops Workshop in Salt Lake City in 2008. Shown is a trimix 12/50 dive to 280 fsw for 10 min with gas switch to 20/40 trimix at 150 fsw and pure oxygen at 20 fsw for both shallow stop (ZHL) and deep stop (RGBM) staging and equal risk. Professional and savvy divers know this from experience and training.

Arbitrary deep stops

Deep stops are mostly arbitrary as seen outside correlated model staging requirements and the question of deep stop semantics is indeed confusing. Real bubble models (TM, TBDM, VPM, RGBM) will all have first stops deeper than traditional USN and ZHL models. With GFs you can get almost anything for stops and nothing about GFs has ever been correlated and validated in the same manner as VPM and RGBM have been correlated and published. See References for details of VPM and RGBM published model correlations and validation [44,35,14,45]. And see comparisons of USN and ZHL correlations just for completeness [37].

Fast Compartments and Middle Compartments: It is false as claimed in some quarters that deep stops only control the fast compartments and that middle compartments are supersaturated in gas content with bubble formation. Bubble models control gas buildup and bubble formation in lockstep across all compartments not just fast ones. Troublesome compartments violating both gas buildup and bubble volume limits are controlled at every point across the whole ascent profile and at the surface within bubble models. It turns out as seen in Table 1 that the control structure of compartments, τ, for the NEDU 170/30 air dive are the same for ZHL and RGBM staging across overlapping segments of the decompression schedules. Nothing much can be said of ZHL controlling tissues in the deep stop region of the RGBM. Calculations were performed with CCPlanner at nominal settings and can easily be checked with most GM and BM diveware packages. Run times are very close when allowing Boyle expansion for bubbles in the shallow zone. Thus, we suggest claims of oversaturated middle compartments in bubble model staging are suspect at best. (Table 1) is also interesting because it clearly shows the staging differences in GM (shallow stop) and BM (deep stop) algorithms. The computed surfacing risks [45] are seen to be larger in the deeper zones for the RGBM and shallower zones for the ZHL. RGBM is conservative in deep zones while ZHL is conservative in shallow zones. The surfacing risks are 0.029 and 0.021 on this profile. If deep stops and BMs are endangering middle compartments, then from Table 1 shallow stops and GMs are doing the same because of the equality of controlling compartments as seen in Table 1.

French navy deep stops tests

The French Navy Tests were air tests at 200 fsw and fall into the category of arbitrary deep stop insertions [46]. Deep stops were inserted into the MN90 shallow stop schedules at 90 fsw. Why not 150 fsw? And why might the impending shallow stop ascension schedule be remotely consistent with the first deep stop? This is a standard question that is raised in deep stop tests with arbitrary deep stop insertions.

Nedu deep stops air trials

The NEDU Deep Stop Air Trials at 170 fsw for 30 min were terminated after some 100+ trials with a 5.5% DCS hit rate using the USN BVM3 (pseudo bubble) model [42, 43]. The profile generated resembled nothing that tec divers seemingly employ and stirred considerable discussion and related counterpoint. Air diving at depths beyond 150 fsw is a seldom occurrence outside Navies and as COMEX data suggests air diving beyond 170 fsw incurs risk 5-7 times greater than at shallower depths. Using the LANL DB at the time a DCS hit rate of 11% was projected. The staging divergences shown following and discussion generated suggest the NEDU test was removed from technical diving, deep or shallow stop. Hopefully USN divers benefitted in ways not clear at the time. In Table 2, looking at the standard USN Extreme Exposure Tables for a 170/30 air dive the NEDU test was longer and outside the USN Table by at least 2. So why would anyone dive or opt for a longer NEDU test schedule over a Standard USN schedule unless risk is very low which it isn’t according to the outcome of the trials. By way of aside, what is going on with the long Haldane test tail in the shallow zone versus the schedule in the USN Extreme Exposure Tables? Run times in the NEDU Test are doubled over run times in the USN Extreme Exposure Tables. The surfacing (EOD) risk is listed after the 10 fsw stop in both cases using the RGBM DB. For the USN Extreme Exposure Table the surfacing risk (EOD) is estimated to be 0.029 while the NEDU Test Schedule has an (updated) estimated surfacing risk of 0.097. Both can be compared to the actual DCS incidence rate in the 170/30 air test of 0.055 and the corresponding ZHL and RGBM schedules and risks are indicated in Table 1. There are some big differences between the test schedule and ZHL, RGBM and USN Extreme Exposure schedules as well as in estimated surfacing risk. Further model differences are seen in the graphic following the Summary. The LANL DB was used for risk estimates in Table 2 with deep stop data for the NEDU Test Schedule and shallow stop data for the USN Extreme Exposure Schedule.

Table 1: Controlling Tissues On 170/30 Air Dive.


Table 2: USN Extreme Exposure Schedule and NEDU Test Schedule.


Modern Developments and Tools

Data Banks and coupled statistical analyses of profile DCS outcomes are a major development in model correlations and validation for safe and sane diving using tables, meters and diveware. Expect their usefulness to grow. In some broad sense, DBs represent an ultimate set of wet tests across the full spectrum of diving in ways that single wet and chamber tests cannot duplicate especially for model correlations and validation. Costs and time are prohibitive for broad scale wet and dry testing. And here is where DBs are useful.

Data banks

Profile Data Banks are extended collections of dive profiles with conditions and outcomes [35,47]. To validate tables, meters, and software within any computational model, profiles and outcomes are necessarily matched to model parameters with statistical (fit) rigor. Profile-outcome information is termed a Data Bank (DB) these days and there are a couple of them worth discussing. Others will surely develop along similar lines. Their importance is growing rapidly in technical and recreational sectors not only for the information they house but also for application to diving risk analysis and model tuning. In a physical world of models DBs are the only way to really validate staging and ascent protocols. Disjoint and scattered tests by themselves fall short in scope of application and validation. The following represent data estimates in the 2010 time frames roughly.
One well known DB is the DAN Project Dive Exploration (PDE) collection [27]. The PDE collection focuses on recreational air and nitrox diving initially but is extending to technical, mixed gas and decompression diving. Approximately 87,000 profiles reside on PDE computers with some 97 cases of DCS across the air and nitrox recreational diving. PDE came online in the 1995 timeframe under the guidance of Peter Bennett, Dick Vann and Petar Denoble. DAN Europe under Alessandro Marroni joined forces with DAN USA in the 2000s extending PDE. Their effort in Europe is termed Dive Safe Lab (DSL). DSL has approximately 50,000 profiles with 18 cases of DCS. For simplicity we group PDE and DSL together as one DB as information is easily exchanged across their computers. In combo, PDE and DSL house some 137,000 profiles with 105 cases of DCS as of 2010 roughly. The incidence rate is 0.0008 or so. This is a massive and important collection. Today it has grown since the early 2000s.
Another more recent DB focused on technical, mixed gas and decompression diving is the Data Bank at Los Alamos National Laboratory (LANL DB). Therein some 3579 profiles with 28 cases of DCS across mixed gas, OC and RB diving reside now. Authors and C&C Dive Team are mainly responsible for bringing the LANL DB online in the early 2000s. Much of the LANL DB rests on data extracted from C&C Dive Team operations over the past 20 yrs or so. Tech diver computer downloads also reside in the DB. Therein the actual incidence rate is 0.0069, roughly 10 times greater than PDE and DSL. Such might be expected as LANL DB houses mixed gas, OC, RB and decompression profiles which are likely a riskier diving activity with more unknowns. For illustration an earlier sample breakdown of LANL DB profile data and outcomes follows. The data is relatively coarse grained making compact statistics difficult. The incidence rate across the whole set is small on the order of 1% and smaller. Fine graining into depths is not meaningful yet so we breakout data into OC and RB gas categories (nitrox, heliox, trimix). (Table 3) tabulates an earlier data compilation Wienke.

Table 3: Profile Data.


Maximum likelihood and USN, ZHL, VPM, RGBM data fits

Maximum likelihood is a general statistical approach to fitting large-scale data to models [32,48-50] and is a useful technique for fitting GMs and BMs to real diving data. The useful models, of course, are the USN and ZHL on the shallow stop side and the VPM and RGBM on the deep stop side. These 4 models have been correlated and safely dived for many years now, forming the bases for worldwide dive tables, meters and desktop software. Millions of dives have been logged using them. Recreational divers tend toward USN and ZHL while technical divers prefer VPM, RGBM and ZHL with GFs. Using deep stop and shallow stop profiles in the LANL DB, maximum likelihood analyses suggested that the USN and ZHL models correlate with shallow stop data very well and the VPM and RGBM models correlate very well with the deep stop data [29]. Opposite cases (GMs against deep stops and BMs against shallow stops) did not correlate in chi squared, Γ, goodness of fit. For the deep stop data,

Γ= 0.717 (VPM)
Γ= 0.081 (RGBM)
and for the shallow stop data,
Γ= 0.934 (USN)
Γ= 0.869 (ZHL)

Clearly both shallow stop and deep stop models correlate well within their corresponding data sets scoring safe and consistent diver utilization of both within model constraints. Chi squared fit, Γ, is a standard numerical test that quantifies how well models track experimental data and ranges,

0 ≤ Γ ≤ 1
in quantifying model correlations for Γ close to 1 or anticorrelations for Γ close to 0.

Computer vendor and training agency deep stop DCS poll

At the UHMS/NAVSEA Workshop [43] deep stop statistics from dive computer Vendors and Training Agencies were presented following polling. In the anecdotal category as far as pure science and medicine they are reproduced below. The reader can take them for whatever worth but the suggested DCS incidence rate is low. That is no surprise as DCS, and oxygen toxicity spikes would likely lead to recalls and replacement units. Training Agencies, decompression computer Manufacturers and dive Software Vendors were queried prior to the Workshop for estimated DCS incidence rates against total dives performed with deep stops. Both recreational and technical diving are lumped together in the estimates below (guesstimates). Keep in mind that polling does not involve controlled testing and only echoes what the Agencies, Manufactures and Vendors glean from their records and accident reports. Both GM and BM algorithms with deep stops were tallied. A rough compendium of the poll is tabulated below as DCS incidences/total dives in the list. Deep Stop Decompression Meters: Suunto, Mares, Dacor, Hydrospace, UTC, Atomic Aquatics, Cressisub report 47/4,000,000 with 950,000 meters marketed. Deep Stop Software Packages: Abyss, GAP, NAUI GAP, ANDI GAP, Free Phase RGBM Simulator, NAUI RGBM Dive Planner, RGBM Simulator and CCPlanner report 68/920,000 with 50,000 CDs marketed. Deep Stop Agency Training Dives: NAUI, ANDI, FDF, IDF report 38/1,020,000 in open water training activities. Commercial Operations: Exxon- Mobil, Chevron tally (trimix only) some 13/43,000 tethered dives.
So, broadly, the tally is 166/6,000,000, probably on the conservative side and slightly limited in participation. The incidence rate is small. Nothing scary is seen as DCS spikes or trends. This again is not science but if alarming DCS statistics were to surface the meter folks (Vendors) would respond very rapidly to the algorithm problem with recalls, new meters and fixes for any perceived liability and safety concerns.

Training agency testing and standards

Some Agencies have conducted wet tests and implemented deep stop protocols into training regimens formally or optionally (NAUI, PADI, GUE, TDI, ANDI, IANTD). This is described in the Deep Stop Workshop Proceedings in completeness and we only summarize a few other points in addition to the above poll [43]. Prior to the introduction of deep stops Training Agencies relied on GM approaches in training divers and instructors with successful and safe results. The ZHL and USN table and computer implementations were mainstays in their training. When deep stop protocols entered the training scene in the 1990s, some Agencies (rather quickly) adopted a look and see attitude while applying their own testing and modified training regimens to BM algorithms, mostly VPM and RGBM. Without DCS and OT issues, deep stop training standards were then strategically drafted and implemented. As far as training regimens go, the following summarizes training standards for some well know US Agencies:

NAUI: A recreational and technical Training Agency using RGBM tables, meters and linked software

PADI: A recreational and technical Training Agency using DSAT tables, meters and software with deep stop options

SSI: A recreational Training Agency using modified USN tables

ANDI: A technical Training Agency using RGBM table, meters and diveware

SDI/TDI/ERDI: A recreational and technical Training Agency using USN tables, computers and com- mercial diveware

IANTD: A recreational and technical Training Agency employing the ZHL and VPM tables, computers and software

GUE: A technical Training Agency that uses ZHL with GFs and VPM tables, computers and software

Training Agencies using USN and ZHL protocols for technical instructor often couple gradient factor (ξ) modifications into dive planning. Some using tables have modified times and repetitive groups to be more conservative. CMAS affiliated Training Agencies are free to choose their tables, meters and software for training. FDF and IDF use RGBM tables, meters and software [51]. An important thing here to mention is that across standards, tables, meters and software the training record of all Agencies collectively is safe and sane.

Dive computers and diveware

The number of dive computers marketed has grown significantly in the past 20 years or so. Units incorporate both GM and BM protocols. These units are modern and engineered for performance and safety. Most have PC connectivity and dive planning software along with interfaces to DAN and LANL DBs for profile downloading. The record of all is one of safe and extensive real-world diving [52,47,53] under many environmental conditions and altitude. Most dive computers are manufactured by one of 4 companies, namely Seiko, Timex, Citizen and Casio, certainly a storied and well-known group of fine instrument makers. The situation with dive planning software is less transparent and less quantitative for user statistics. It is almost impossible to track DCS statistics from divers using diveware and dive planning software. However, the record seems fairly safe and sane from reports and sales usage information. Diveware is used extensively in the technical sectors.

Isorisk deep stop and shallow stop profiles

To say deep stop schedules are shorter than shallow stop schedules needs a metric and qualification. This is only true at the same risk level. To assess risk DBs are necessary and a mathematical risk function needs be assigned to fit the data. In the case of diving, a supersaturation risk function is easily constructed for shallow stops and a bubble number risk function can similarly be devised for deep stops [54,55,57]. Such risk functions are then useful for dive planning. A comparative example is seen in the following graphic contrasting deep stop and shallow stop equal risk (2.8%) profiles for a trimix dive to 280 fsw for 10 min. The LANL DB of deep stop and shallow stop downloaded computer profiles is used. Clearly, the isorisk comparison in the following slide shows deep stop staging is shorter than shallow stop staging. ZHL was used for the shallow stop calculation and RGBM for the deep stop calculation. To round out discussion and provide a short reference list of popular deep stop and shallow stop dive computers and associated dive planning software the following lists complete our analysis in terms of actual diving and algorithm usage [56-59].

Commercial dive computers

Major dive computers incorporate both GM and BM algorithms with user knobs for conservative to aggressive staging, that is, from nonstop to decompression diving on OC and RB systems for nitrox, heliox and trimix. Well known and popular Vendors and models include:

Suunto: Suunto markets a variety of computers all using the RGBM. The EON Steel and DX can be used in gauge, air, nitrox, trimix, OC and CCR modes. The D6, D4 and Vyper are OC computers in gauge, air and nitrox modes. Zoop and Cobra are recreational computers for gauge, air and nitrox use [60].

Mares: Mares computers use the RGBM. Recreational models include the ICON HD, Matrix, Smart and Puck Pro for OC in gauge, air and nitrox modes

Uwatec: Uwatec computers are marketed by ScubaPro and all use the ZHL algorithm. The M2 and Pro Mantis are targeted for both recreational and technical diving with gauge, air, nitrox, trimix and CCR modes. The Pro Galileo Sol is a technical dive computer with gauge, air, nitrox and trimix capabilities.

UTC: UTC markets a message sending-receiving computer called the UDI for air and nitrox. All UDIs employ the RGBM. The message exchanging capabilities extend out to 2 miles using sonar, GPS and underwater communications systems. Models include the UDI 14 and UDI 28. Underwater special military units, search and recovery teams and exploration operations use the UDIs routinely today. UDIs also have high resolution compasses for extended navigation [61-63].

Huish/Atomic Aquatics/Liquivision: Huish Outdoors owns both Atomic Aquatics and Liquivision. Atomic Aquatics markets a recreational dive computer using the RGBM called the Cobalt for air and nitrox. Liquivision models include the Kaon, Lynx, X1 and Xeo. The Lynx and Kaon are technical and recreational computers for gauge, air and nitrox modes using the ZHL with GFs. The X1 and Xeo are full up technical dive computers for air, nitrox, trimix and CCR using offering both the ZHL with GFs and RGBM.

Cressisub: Cressisub computers use the RGBM in recreational gauge, air and nitrox modes. The Newton Titanium, Goa, Giotta and Leonardo are Cressisub models. Cressisub markets a complete line of diving gear in addition to dive computers.

Sherwood: Sherwood computers all use the ZHL. Recreational models for air and nitrox include the Amphos and Wisdom computers.

Oceanic: Oceanic computers use the DSAT and ZHL algorithms for recreational diving. Many models are marketed for gauge, air and nitrox diving and include the VTX, Datamax, Geo, Pro Plus. OCi, Atom, Veo and F10.

Shearwater: Shearwater computers are targeted for technical diving. All use the ZHL with GFs and VPM may be downloaded as an option, The Petrel, Perdex and Nerd2 models address air, nirox, trimix and CCR. Some RB Manufacturers are integrating Shearwater computers into their RB units.

Ratio: Ratio computers employ the ZHL and VPM algorithms for technical and recreational diving. Models include the iX3M Pro and IX3M GPS (Easy, Deep, Tech+, Reb versions) plus the iDive Sport and iDive Avantgarde (Easy, Deep, Tech+ versions) series with air, nitrox, helium and CCR capabilities and GPS and wireless connectivity. The model list is impressive and complete with a strong offering of technical and professional diving units [64,65].

Cochrane: Cochrane computers are marketed for recreational and technical diving using the USN LEM (VVAL18). The EMC16 a is recreational air and nitrox computer. The EMC20H is a technical air, nitrox and helium unit. Military units include the EODIII for USN EOD operations and the NSWIII for USN Special Warfare (SEAL) evolutions.

Aeris: Aeris computers are directed at recreational divers using (modified USN) DSAT algorithms for air and nitrox. Models include the A100, A300, A300AI, XR1, NXXR2, Elite T3, Epic and Manta.

Commercial dive planning software

Online and commercially available software packages span GM and BM algorithms along with OT estimation and include:

Free Phase RGBM Simulator: Free Phase RGBM Simulator is a software package offered by Free Phase Diving incorporating the ZHL and RGBM algorithms. Both the ZHL and RGBM algorithms are user validated and correlated with actual diving data and tests as mentioned. The Free Phase RGBM Simulator for nominal settings is one-to-one with the published and released NAUI Technical Diving Tables used to train mixed gas OC and RB divers. As such, it is a valuable training and diving tool for deep and decompression diving. No other diveware packages, excepting NAUI GAP and ANDI GAP, provide such correlation with published and user validated Dive Tables. It is also keyed to the Liquivision RGBM implementation plus others under construction in the Far East.

Abyss: Abyss in 90s first introduced the full RGBM into its diveware packages. The Buhlmann ZHL model was also included as the dissolved gas package. It has seen extensive use over the past 20 yrs or so in the technical diving area. A variety of user knobs on bubble parameters and M-values permit aggressive to conservative staging in both models. Both the ZHL and RGBM have been published and formally correlated with diving data. Later, the modified RGBM with M-value reduction factors, χ, was incorporated into Abyss. Modified RGBM with χ was published and correlated with data in the late 90s and also served as the basis for Suunto, Mares, Dacor, ConnXion, Cressisub, UTC, Mycenae, Aqwary, Hydrospace, ANO, Artisan and other RGBM computers. Full RGBM was first incorporated into Hydrospace computers and today in Suunto, Atomic Aquatics, Liquivision and ANO computers. ABYSS was a ground breaker.

VPlanner: VPlanner first introduced the VPM in the late 90s. Based on the original work of Yount and Hoffman, the software has seen extensive use by the technical diving community. Formal LANL DB correlations of the VPM and thus VPlanner have been published. User knobs allow adjustment of bubble parameters for aggressive to conservative staging. VPlanner is also used in Liquivision and Advanced Diving Corporation computers for technical diving.

ProPlanner: ProPlanner is a software package using modified Z-values for diver staging. Buhlmann Z-values with GFs are employed with user knobs for conservancy. The model is called the VGM (variable gradient model) ProPlanner. Some GFs claim to mimic the VPM. Correlations have not been published about VGM and ProPlanner.

GAP: GAP is a software package similar to Abyss offering the full RGBM, modified RGBM with χ and Buhlmann ZHL with GFs. Introduced in the mid-90s, it has seen extensive usage in recreational and technical sectors. Apart from user GFs, the models and parameters in GAP have been published and correlated with diving data and profiles tested over years. Adjustable conservancy settings for all models can be selected. GAP has been keyed to Atomic Aquatics and Liquivision dive computers. Training Agency spinoffs also include ANDI GAP and NAUI GAP.

DecoPlanner: DecoPlanner is a diveware package offered by GUE. Both VPM and Buhlmann ZHL with GFs are available in DecoPlanner. Evolving over the past 10 - 15 yrs, DecoPlanner also incorporates GUE ratio deco approaches which are just another modification of the original Haldane 2/1 law applied to M-value ratios, M/P. This is just another representation of M-values for diver staging. Nothing is published about ratio deco data correlations but both the ZHL and VPM have been correlated. It has seen extensive use in the technical diving community and GUE diver training.

Analyst: Analyst is a software package marketed by Cochrane Undersea Technology for PCs. It is keyed to Cochrane computers as a dive planner and profile downloader. The Cochrane family of computers use the USN LEM for recreational, technical and military applications. The LEM is a neo-Haldanian model with exponential uptake and linear elimination of inert gases and imbedded in the USN VVAL18 project.

DiveLogger: DiveLogger is linked to Ratio technical and recreational computers. Ratio computers provide GPS and wireless connectivity and offer the ZHL and VPM algorithms to divers. Dive planning and profile downloading capabilities are included in the diveware package. As mentioned, both VPM and ZHL have been correlated with data.

DiveSim: DiveSim is a UDI software package for dive planning and profile downloading. UDI computers and diveware employ the correlated RGBM for air and nitrox. The software packages also include diver to diver, diver to surface, GPS, compass and related communications capabilties. UDIs are highly technical and useful underwater tools used by military, search and rescue and exploration teams but are readily accessible to recreational divers needing underwater communications and boat connectivity.

DSG: A similar development from Dan Europe (DSL) is the Diver Safety Guardian (DSG) software package providing the diver with feedback from an online Deco Risk Analyzer (DRA). Based on permissible supersaturation it is under testing and development. As just an end of dive (EOD) risk estimator now plans are in the works to make it a wet (OTF) risk estimator.

CCPlanner: CCPlanner is a LANL software package offering full RGBM, modified RGBM, USN M-value and Buhlmann Z-value algorithms for dive planning. It is used by the C&C Team and is not distributed commercially but is obtainable under written contract. Also encoded is the Hills TM. It is also provided with licensed LANL RGBM codes. A risk analysis routine using the LANL DB is encoded in CCPlanner and imbedded in licensed RGBM OC and RB codes.

Output is typically extensive from modern diveware. Platforms range from PCs to Droid devices as well as Workstations to Mainframes. Languages employed in codes include VIZ, BASIC, FORTRAN, C, and derivatives. Meter Vendors (Suunto, Mares, Liquivision, UTC, Atomic Aquatics, Cressisub, Sherwood, Oceanic, Genesis, Shearwater, Uwatec, Cochrane, Ratio and Aeris to name a few) often supply proprietary software packages keyed to their meter algorithms for coupled dive planning. These are useful diving tools (Figure 1).

Figure 1:



The issues of deep stops versus shallow stops are not real issues today for those of us involved in operational diving across diverse venues. Both stop schemes work and have been shown to be safe and useful. In common parlance, deep stops control the bubble while shallow stops treat the bubble. Shallow stops have seen more testing than deep stops but real world DCS incidence rates for both are small and DCS spikes are not seen in either case. Technical diving camps use both deep and shallow stops plus hybrids in between. Recreational camps tend toward shallow stops. Commercial diving is in a transitional mode between both. Basic and correlated models employed are the USN, ZHL, VPM and RGBM. These algorithms alone exhibit extensive and safe diving utilization. In closing, we hope the material presented is and will be useful in making safe and sane diving decisions about deep and shallow stops with tables, meters, software and ad hoc protocols. Happy diving.


Thanks to Professional Colleagues, Dive Testers, Trainers, Data Collectors, Table Fabricators, USN, USAF, USCG, Scientists, Laboratory Experimenters, Chamber Techs and Dive Physicians in the Living Laboratory of real diving including but not limited to LANL, C&C Dive Team, Exxon-Mobil, Chevron, Brazilian Mergubhador de Combate, Trondheim Laboratory, UW BIOTRON and Tiny Bubble Group Hawaii plus Meter Vendors Suunto, Mares, Atomic Aquatics, Aqwary, Cressisub, Free Phase, ConneXon, Artisan, Plexus and Hy drospace plus Software Purveyors Abysmal Diving, GAP, RGBM Simulator and CCPlanner plus Training Agencies NAUI, IANTD, ANDI, FDF, IDF and PADI to mention a few. Special thanks to Spouses, Luzanne Coburn and Diane O’Leary, for their support (Figure 2).

Figure 2:


Acronyms and Nomenclature: These are standard and extensively employed by the diving community at large. For brevity we also pen them in the following paper and analysis:

ANDI: Association of Nitrox Diving Instructors.

BM: Bubble phase model dividing the body into tissue compartments with halftimes that are coupled to inert gas diffusion across bubble film surfaces of exponential size distribution constrained in cumulative growth by a bubble volume limit point.

Bubble broadening: Noted laboratory effect that small bubbles increase, and large bubbles decrease in number in liquid and solid systems due to concentration gradients that drive material from smaller bubbles to larger bubbles over time spans of hours to days.

Bubble regeneration: Noted laboratory effect that pressurized distributions of bubbles in aqueous systems return to their original non pressurized distributions in time spans of hours to days.

CCR: Closed circuit rebreather, a special RB system that allows the diver to fix the oxygen partial pressure in the breathing loop (setpoint).

CMAS: Confederation Mondial des Activites Subaquatiques.

Critical radius: Temporary bubble radius at equilibrium, that is, pressure inside the bubble just equals the sum of external ambient pressure and film surface tension.

DB: Data bank, stores downloaded computer profiles in 5-10 sec time-depth intervals.

DCS: Decompression sickness, crippling malady resulting from bubble formation and tissue damage in divers breathing compressed gases at depth and ascending too rapidly.

Decompression stop: Necessary pause in a diver ascent strategy to eliminate dissolved gas and/or bubbles safely and is model based with stops usually made in 10 fsw increments.

Deep stop: Decompression stop made in the deep zone to control bubble growth.

DAN: Divers Alert Network.

Diveware: Diver staging software package usually based on USN, ZHL, VPM and RGBM algorithms.

Diving algorithm: Combination of gas transport and/or bubble model with coupled diver ascent strategy.

Diluent: Any mixed gas combination used with pure oxygen in the breathing loop of RBs.

DOD: Department of Defense.

DOE: Department of Energy.

Doppler: A device for counting bubbles in flowing blood that bounces acoustical signals off bubbles and measures change in frequency.

DSAT: Diving Science and Technology, a research arm of PADI.

DSL: Diving Safety Laboratory, the European arm of DAN.

EAHx: Enriched air helium breathing mixture with oxygen fraction, x, above 21% often called helitrox.

EANx: Enriched air nitrox breathing mixture with oxygen fraction, x, above 21%.

EOD: End of dive risk estimator computed after finishing dive and surfacing.

ERDI: Emergency Response Diving International.

FDF: Finnish Diving Federation.

GF: Gradient factor, multiplier, ξ, of USN and ZHL critical gradients, G and H, that can mimic BMs.

GM: Dissolved gas model dividing the body into tissue compartments with arbitrary half times for uptake and elimination of inert gases with tissue tensions constrained by limit points.

GUE: Global Underwater Explorers.

G-values: A set of critical gradients obtained by subtracting absolute pressure, P, from M-values.

Heliox: Breathing gas mixture of helium and oxygen used in deep and decompression diving.

IANTD: International Association of Nitrox and Technical Divers.

ICD: Isobaric counter diffusion, inert dissolved gases (helium, nitrogen) moving in opposite directions in tissue and blood. IDF: Irish Diving Federation.

LEM: Linear exponential model, a dissolved gas model with exponential gas uptake and linear elimination by Thalmann

LSW theory: Lifschitz-Slyasov-Wagner Ostwald bubble ripening theory and model.

M-values: Set of limiting tensions for dissolved gas buildup in tissue compartments at depth.

Mirroring: The gas switching strategy on OC ascents of reducing the helium fraction and increasing the oxygen fraction in the same amount thereby keeping nitrogen constant.

Mixed Gases: Any combination of oxygen, nitrogen and helium gas breathed underwater.

NAUI: National Association of Underwater Instructors.

NDL: No decompression limit, maximum allowable time at given depth permitting direct ascent to the surface.

NEDU: Naval Experimental Diving Unit, diver testing arm of the USN in Panama City.

Nitrox: Breathing gas mixture of nitrogen and oxygen used in recreational diving.

OC: Open circuit, underwater breathing system using mixed gases exhausted upon exhalation.

Ostwald ripening: Large bubble growth at the expense of small bubbles in liquid and solid systems.

OT: Oxtox, pulmonary and/or central nervous system oxygen toxicity resulting from over exposure to oxygen at depth or high pressure.

PADI: Professional Association of Diving Instructors.

PDE: Project Dive Exploration, a computer dive profile collection project at DAN.

Phase Volume: Surfacing limit point for bubble growth under decompression.

Ratio Deco: R-value deco, a simple modification of M-value (dissolved gas) staging using M-values divided by absolute pressure, R=M/P.

Pyle Stop: Deep ad hoc decompression stops made on ascent in successive half, quarter, eight multiples of bottom depth.

RB: Rebreather, underwater breathing system using mixed gases from a cannister that are recirculated after carbon dioxide is scrubbed with oxygen from another cannister injected into the breathing loop. recreational diving: air and nitrox nonstop diving.

RF: Reduction factor, one of a set of published M-value multipliers, χ, that reduce diving risk.

RGBM Algorithm: An American bubble staging model correlated with DCS computer outcomes by Wienke.

RN: Royal Navy.

R-Values: A set of critical ratios obtained by dividing M-values or G-values by absolute pressure

SDI: Scuba Diving International.

Shallow Stop: Decompression stop made in the shallow zone to eliminate dissolved gas.

SI: Surface interval, time between dives.

SSI: Scuba Schools International.

TDI: Technical Diving International.

Technical Diving: mixed gas (nitrogen, helium, oxygen), OC and RB, deep and decompression diving.

TM: Thermodynamic model, a phase staging model introduced by Hills in 1965 that first consistently coupled dissolved gas and phase separation in divers.

TMX x/y: Trimix with oxygen fraction, x, helium fraction, y, and the rest nitrogen.

Trimix: Breathing gas mixture of helium, nitrogen and oxygen used in deep and decompression diving.

USAF: United States Air Force.

USCG: United States Coast Guard.

USN: United States Navy.

USN Algorithm: An American dissolved gas staging model developed by Workman of the US Navy.

UTC: United Technologies Center, an Israeli company marketing a message sending-receiving underwater system (UDI) using sonar, GPS and underwater communications with range 2 miles.

VPM Algorithm: An American bubble staging model based on gels by Yount.

Z-Values: another set of Swiss limiting tension extended to altitude and similar to M-values.

ZHL Algorithm: A Swiss dissolved gas staging model developed and tested at altitude by Buhlmann.

χ: Set of correlated and published M-value reduction factors (RF) for deeper than previous, short surface interval and multiday dives.

ξ: Set of unpublished and uncorrelated critical G-value multipliers (GF) that try to mimic BMs or extend stop time in the shallow zone.

τ: Controlling tissue halftime at a decompression stop on ascent restricted by dissolved gas buildup and/or separated bubble volume.

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