Research on the Rescue Time of the Taiwan Marathon Emergency Medical Service System Between 2013 and 2021.

Background: While there are several studies on marathon injuries worldwide, there are no related studies on the Taipei Marathon regarding the rescue time of onsite injury cases, the incidence of out-of-hospital cardiac arrest (OHCA) cases, and the success rate of recovery of spontaneous circulation (ROSC). This study aims to fill that gap.

Hypothesis: The rescue time onsite of contact injury cases was in the prime time for lifesaving.

Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.

Purpose: To determine whether a deep learning approach using generative adversarial networks (GANs) is beneficial for the classification of retinal conditions with Optical coherence tomography (OCT) images.

Methods: Our study utilized 84,452 retinal OCT images obtained from a publicly available dataset (Kermany Dataset). Employing GAN, synthetic OCT images are produced to balance classes of retinal disorders.

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).