This study was conducted to explore the effect of hyperbaric oxygen (HBO) therapy combined with virtual reality (VR) training on oxidative stress indicators (OSIs) and inflammatory factors (IFs) in swimming athletes with depression. 88 swimming athletes suffering from depression were grouped into a control group (group C) and a research group (group R). The patients in group C were given HBO therapy, and the group R was given HBO therapy combined with VR training. The Physical Health Questionnaire (PHQ-9) and the Symptom Checklist (SCL-90) were adopted to assess the depression status of patients. The differences between the two groups of serum OSIs and IFs before and after the intervention were compared and analyzed. The results disclosed that the PHQ-9 score and SCL-90 score in group R were not different from those in group C before the intervention, but those in group R were greatly decreased in contrast to group C after the intervention ( P < 0.05 ). Before the intervention, there was no obvious difference in the OSIs and the IFs between the two groups. The levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α) in the two groups were decreased greatly after intervention, and those in the group R were much lower than those in group C ( P < 0.05 ). Compared with the preintervention, the levels of malondialdehyde (MDA) in both groups were reduced observably, and the levels of superoxide dismutase (SOD), nitric oxide (NO), and glutathione peroxidase (GSH-Px) were dramatically increased. The MDA in group R was much lower, while the SOD, NO, and GSH-Px were much higher in contrast to group C ( P < 0.05 ). It indicated that HBO combined with VR training had a good clinical effect for swimming athletes suffering from depression, and it could reduce the oxidative stress and inflammation, thereby helping patients recover quickly.

We aimed to investigate the impact of a single bout of endurance exercise on the brain-derived neurotrophic factor (BDNF) in humans and analyze how a single bout of endurance exercise impacts the peripheral BDNF types by age group. We performed a systematic literature review by searching PubMed, Elsevier, and Web of Science for studies that included a single bout of endurance exercise in the experimental group and other exercise types in the control group. Eight interventions were included in the study. Overall, a single bout of endurance exercise significantly increased BDNF expression (SMD = 0.30; 95% CI = [0.08, 0.52]; p = 0.001), which was confirmed in the serum BDNF (SMD = 0.30; 95% CI = [0.04, 0.55]; p < 0.001). A non-significant trend was observed in the plasma BDNF (SMD = 0.31; 95% CI = [−0.13, 0.76]; p = 0.017). The serum and plasma BDNF levels significantly increased regardless of age (SMD = 0.35; 95% CI = [0.11, 0.58]; p = 0.004; I2 = 0%). In conclusion, a single bout of endurance exercise significantly elevates BDNF levels in humans without neurological disorders, regardless of age. The serum BDNF is a more sensitive index than the plasma BDNF in evaluating the impact of a single bout of endurance exercise on the BDNF.

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.