Using machine learning to determine the correlation between physiological and environmental parameters and the induction of acute mountain sickness.

BACKGROUND: Recent studies on acute mountain sickness (AMS) have used fixed-location and fixed-time measurements of environmental and physiological variable to determine the influence of AMS-associated factors in the human body. This study aims to measure, in real time, environmental conditions and physiological variables of participants in high-altitude regions to develop an AMS risk evaluation model to forecast prospective development of AMS so its onset can be prevented. RESULTS: Thirty-two participants were recruited, namely 25 men and 7 women, and they hiked from Cuifeng Mountain Forest Park parking lot (altitude: 2300 m) to Wuling (altitude: 3275 m). Regression and classification machine learning analyses were performed on physiological and environmental data, and Lake Louise Acute Mountain Sickness Scores (LLS) to establish an algorithm for AMS risk analysis. The individual R2 coefficients of determination between the LLS and the measured altitude, ambient temperature, atmospheric pressure, relative humidity, climbing speed, heart rate, blood oxygen saturation (SpO2), heart rate variability (HRV), were 0.1, 0.23, 0, 0.24, 0, 0.24, 0.27, and 0.35 respectively; incorporating all aforementioned variables, the R2 coefficient is 0.62. The bagged trees classifier achieved favorable classification results, yielding a model sensitivity, specificity, accuracy, and area under receiver operating characteristic curve of 0.999, 0.994, 0.998, and 1, respectively. CONCLUSION: The experiment results indicate the use of machine learning multivariate analysis have higher AMS prediction accuracies than analyses utilizing single varieties. The developed AMS evaluation model can serve as a reference for the future development of wearable devices capable of providing timely warnings of AMS risks to hikers.

RNAi knock-down of the Litopenaeus vannamei Toll gene (LvToll) significantly increases mortality and reduces bacterial clearance after challenge with Vibrio harveyi.

In this study, we used real-time PCR to simultaneously monitor the responses of 12 key genes of the shrimp innate immune system in Litopenaeus vannamei after challenge with Vibrio harveyi. In the proPO activating system, we found that proPO was up-regulated (3.3x control at 36hpi). The hemolymph clotting genes transglutaminase (TGase) and clotting protein were also up-regulated, as were 5 genes in the antimicrobial peptide system (ALF, Crustin, Lyz, PEN2 and PEN4), with only PEN3 showing no significant changes. In the antioxidant defense system, SOD was slightly elevated while GPx was substantially down-regulated. In the pattern recognition receptor system, at 24hpi, the Toll gene (LvToll) showed the highest relative increase in expression level of all the investigated genes (15x greater than the sterile seawater control). In the second part of this study, when LvToll was knocked down by RNAi silencing, there was no effect on either survival rates or bacterial number in unchallenged shrimp. There was also no difference in mortality rates between control shrimp and LvToll-silenced shrimp when these two groups were challenged with a viral pathogen (white spot syndrome virus; WSSV). However, when LvToll-silenced shrimp were challenged by V. harveyi, there was a significant increase in mortality and bacterial CFU counts. We note that the increase in bacterial CFU count occurred even though treatment with EGFP dsRNA had the opposite effect of reducing the CFU counts. We conclude that LvToll is an important factor in the shrimp innate immune response to acute V. harveyi infection, but not to WSSV.