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Introduction: This study aimed to explore the impact of COVID-19 on strength performance in highly trained athletes. Method: A force plate was employed to measure squat jump height (SJH), counter-movement jump height (CMJH), and drop jump reactive strength index (DJRSI) in 27 highly trained athletes before infection, and at one week, two weeks, and four weeks post-recovery. Additionally, an Isometric Mid-thigh Pull (IMTP) test was conducted to record maximum isometric strength (MIS) and the rate of force development of the initial phase (RFD 0–50; RFD 0–100). Repeated measures analysis of variance was utilized to compare variations in these indicators across different time points. Results: One week post-recovery, SJH (-7.71%, P = 0.005), CMJH (-9.08%, P < 0.001), DJRSI (-28.88%, P < 0.001), MIS (-18.95%, P < 0.001), RFD 0–50 (-64.98%, P < 0.001), and RFD 0–100 (-53.65%, P < 0.001) were significantly lower than pre-infection levels. Four weeks post-recovery, SJH (-2.08%, P = 0.236), CMJH (-3.28%, P = 0.277), and MIS (-3.32%, P = 0.174) did not differ significantly from pre-infection levels. However, DJRSI (-11.24%, P = 0.013), RFD 0–50 (-31.37%, P = 0.002), and RFD 0–100 (-18.99%, P = 0.001) remained significantly lower than pre-infection levels. Conclusion: After COVID-19, highly trained athletes exhibited a significant reduction in maximum strength, explosive strength, reactive strength, and initial phase force generation capability. By four weeks post-recovery, their maximum and explosive strength had returned to near pre-infection levels, yet their reactive strength and initial phase force generation capability remained significantly impaired. [ABSTRACT FROM AUTHOR]

Background: Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved. Methodology/Principal Findings: This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively. Conclusions/Significance: The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior. [ABSTRACT FROM AUTHOR]