Arterial blood gases' analysis in elite breath-hold divers at extreme depths.

PURPOSE: To showcase results of arterial blood gases' analysis in elite breath-hold divers sampled at depths where their total lung capacities are below their residual lung volume on surface. METHODS: Three male elite breath-hold divers performed body plethysmographies to determine their lung volumes. Two dives were performed, one on normal inhalation to 60 m of depth and the second on complete exhalation to 10 m of depth. Blood samples were taken on five occasions; before the first dive, at 60 and 10 m of depth and immediately after resurfacing after both dives. RESULTS: Arterial blood gases' analysis at 60 m of depth showed an increase in partial pressures of oxygen and carbon dioxide, a consequent decrease in pH and an increase in concentration of HCO3-. After resurfacing, in two divers, values mostly returned to normal; hypoxemia was observed in one diver. At 10 m of depth, all values showed similar variation, and hypoxemia was observed in the same diver but at depth. Upon resurfacing, all values returned to normal. CONCLUSION: This is the first study performed at depths where the total lung capacities of participants are below their residual lung volumes at the surface. Partial pressure of carbon dioxide increases at depth to higher than normal values causing pH to decrease thus exceeding the buffering potential of the blood. In addition, previous assumptions that maximum depth in breath-hold divers is where total lung capacity is reduced to their residual volume proved wrong as our group of divers had no symptoms after resurfacing.

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm.

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020-3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406-0.9992, 0.9404-0.9998 and 0.9797-0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.