Early diagnosis and prevention play a crucial role in the treatment of patients with ARDS. The definition of ARDS requires an arterial blood gas to define the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO 2 /FiO 2 ratio). However, many patients with ARDS do not have a blood gas measured, which may result in under-diagnosis of the condition. Using data from MIMIC-III Database, we propose an algorithm based on patient non-invasive physiological parameters to estimate P/F levels to aid in the diagnosis of ARDS disease. The machine learning algorithm was combined with the filter feature selection method to study the correlation of various noninvasive parameters from patients to identify the ARDS disease. Cross-validation techniques are used to verify the performance of algorithms for different feature subsets. XGBoost using the optimal feature subset had the best performance of ARDS identification with the sensitivity of 84.03%, the specificity of 87.75% and the AUC of 0.9128. For the four machine learning algorithms, reducing a certain number of features, AUC can still above 0.8. Compared to Rice Linear Model, this method has the advantages of high reliability and continually monitoring the development of patients with ARDS.

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Inhibitory control training (ICT) is a promising method to improve individual performance of inhibitory control (IC). Recent studies have suggested transcutaneous vagus nerve stimulation (tVNS) as a novel approach to affect cognitive function owing to its ability to modulate the locus coeruleus-noradrenaline system. To examine the synergistic effects of combining ICT with tVNS, 58 young males in college were randomly assigned to four groups: ICT + tVNS, ICT + sham tVNS, sham ICT + tVNS, and sham ICT + sham tVNS. Participants were instructed to complete three sessions that comprised pre-training tests, a training session, and post-training tests sequentially. Results showed that the ICT + tVNS group significantly improved training and near-transfer effects on the stop-signal and Go/No-go tasks, and these effects were larger than those of the other groups. However, none of the groups exhibited the far-transfer effect on the color-word Stroop task. These results suggest that tVNS augments the intervention effects of training and similar inhibition tasks to achieve the synergistic effect; however, it does not modulate the effects of non-training tasks and obtain the far-transfer effect. ICT combined with tVNS may be a valuable intervention for improving IC in healthy individuals in certain industries and offers novel research ideas for using tVNS for cognitive improvement.

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