BACKGROUND: The effects of heat acclimation (HA) on the hypothalamus after exertional heatstroke (EHS) and the specific mechanism have not been fully elucidated, and this study aimed to address these questions.
METHODS: In the present study, rats were randomly assigned to the control, EHS, HA, or HA + EHS groups (n = 9). Hematoxylin and eosin (H&E) staining was used to examine pathology. Tandem mass tag (TMT)-based proteomic analysis was utilized to explore the impact of HA on the protein expression profile of the hypothalamus after EHS. Bioinformatics analysis was used to predict the functions of the differentially expressed proteins. The differential proteins were validated by western blotting. An enzyme-linked immunosorbent assay was used to measure the expression levels of inflammatory cytokines in the serum.
RESULTS: The H&E staining (n = 5) results revealed that there were less structural changes in hypothalamus in the HA + EHS group compared with the EHS group. Proteomic analysis (n = 4) revealed that proinflammatory proteins such as argininosuccinate synthetase (ASS1), high mobility group protein B2 (HMGB2) and vimentin were evidently downregulated in the HA + EHS group. The levels of interleukin (IL)-1β, IL-1, and IL-8 were decreased in the serum samples (n = 3) from HA + EHS rats.
CONCLUSIONS: HA may alleviate hypothalamic damage caused by heat attack by inhibiting inflammatory activities, and ASS1, HMGB2 and vimentin could be candidate factors involved in the exact mechanism.

To determine how lateral shuffling/lateral shuffle (LS) -induced fatigue affects ankle proprioception and countermovement jump (CMJ) performance. Eighteen male college athletes performed 6 modes of a repeated LS protocol with 2 distances (2.5 and 5 m) and 3 speeds (1.6, 1.8, and 2.0 m/s). After LS, ankle inversion proprioception (AIP) was measured using the active movement extent discrimination apparatus (AMEDA). CMJ, blood lactate (BLa), heart rate (HR) and rating of perceived exertion (RPE) were measured before and after LS. The number of changes of direction (CODs) in each protocol was recorded. LS-induced fatigue was evident in BLa, HR and RPE (all p < 0.05), increasing with shorter shuffle distance and faster speed. RM-ANOVA showed a significant distance main effect on both AIP (p < 0.01) and CMJ (p < 0.05), but the speed main effect was only significant for CMJ (p ≤ 0.001), not AIP (p = 0.87). CMJ performance was correlated with BLa, HR and RPE (r values range from -0.62 to -0.32, all p ≤ 0.001). AIP was only correlated with CODs (r = -0.251, p < 0.01). These results suggested that in LS, shorter distance, regardless of speed, was associated with worse AIP, whereas subsequent CMJ performance was affected by both LS distance and speed. Hence, AIP performance was not related to physiological fatigue, but CMJ performance was. Results imply that LS affects processing proprioceptive input and producing muscular output differently, and that these two aspects of neuromuscular control are affected by physiological fatigue to varying degrees. These findings have implications for injury prevention and performance enhancement.

OBJECTIVE: To analyze the effect and molecular mechanism of Gehua Jiejiu Dizhi decoction (, GJDD) on alcoholic fatty live disease (AFLD) by using proteomic methods.
METHODS: The male C57BL/6J mouse were randomly divided into four groups: control group, model group, GJDD group and resveratrol group. After the AFLD model was successfully prepared by intragastric administration of alcohol once on the basis of the Lieber-DeCarli classical method, the GJDD group and resveratrol group were intragastrically administered with GJDD (4900 mg/kg) and resveratrol (400 mg/kg) respectively, once a day for 9 d. The fat deposition of liver tissue was observed and evaluated by oil red O (ORO) staining. 4DLabel-free quantitative proteome method was used to determine and quantify the protein expression in liver tissue of each experimental group. The differentially expressed proteins were screened according to protein expression differential multiples, and then analyzed by Gene ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Finally, expression validation of the differentially co-expressed proteins from control group, model group and GJDD group were verified by targeted proteomics quantification techniques.
RESULTS: In semiquantitative analyses of ORO, all kinds of steatosis (ToS, MaS, and MiS) were evaluated higher in AFLD mice compared to those in GJDD or resveratrol-treated mice. 4DLabel-free proteomics analysis results showed that a total of 4513 proteins were identified, of which 3763 proteins were quantified and 946 differentially expressed proteins were screened. Compared with the control group, 145 proteins were up-regulated and 148 proteins were down-regulated in the liver tissue of model group. In addition, compared with the model group, 92 proteins were up-regulated and 135 proteins were down-regulated in the liver tissue of the GJDD group. 15 differentially co-expressed proteins were found between every two groups (model group vs control group, GJDD group vs model group and GJDD group vs control group), which were involved in many biological processes. Among them, 11 differentially co-expressed key proteins (Aox3, H1-5, Fabp5, Ces3a, Nudt7, Serpinb1a, Fkbp11, Rpl22l1, Keg1, Acss2 and Slco1a1) were further identified by targeted proteomic quantitative technology and their expression patterns were consistent with the results of 4D label-free proteomic analysis.
CONCLUSIONS: Our study provided proteomics-based evidence that GJDD alleviated AFLD by modulating liver protein expression, likely through the modulation of lipid metabolism, bile acid metabolism and with exertion of antioxidant stress.

BACKGROUND: Mammary carcinoma, a pervasive and potentially lethal affliction, is conjectured to be profoundly influenced by physical exercise, both in prophylaxis and therapeutic contexts. This study endeavors to explore the repercussions of exercise training on the progression of mammary carcinoma, particularly the mechanisms by which the amalgamation of an exercise regimen and doxorubicin induces tumor cell apoptosis.
METHODS: Female BALB/c mice were categorized into four distinct groups: A sedentary group (SED), an exercise group (Ex), a doxorubicin group (Dox, 5 mg/kg), and a combined treatment group (Dox + Ex). The exercise training lasted for 21 days and included aerobic rotarod exercise and resistance training. The impact of exercise training on tumor growth, immune cell proportions, inflammatory factor levels, and cell apoptosis pathway was assessed.
RESULTS: Exercise training significantly curtailed tumor growth in a mouse model of breast cancer. Both the Ex and Dox groups exhibited significant reductions in tumor volume and weight (p < 0.01) in comparison to the SED group, while the Dox + Ex group had a significantly lower tumor volume and weight than the Dox group (p < 0.01). Exercise training also significantly increased the proportion of NK and T cells in various parts of the body and tumor tissue, while decreasing tumor blood vessels density. Exercise training also increased IL-6 and IL-15 levels in the blood and altered the expression of apoptosis-related proteins in tumor tissue, with the combined treatment group showing even more significant changes.
CONCLUSIONS: Physical training improves the effectiveness of doxorubicin in treating breast cancer by activating cytotoxic immune cells, releasing tumor suppressor factors, and initiating mt-apoptosis, all while mitigating the adverse effects of chemotherapy.

This study aims to discuss the load intensity monitoring in the training process of sitting volleyball, to help coaches understand the training status of athletes, and to provide a scientific basis for the follow-up training plan. Through big data technology, the physiological changes of athletes can be more accurately grasped. This includes classification and summary of exercise load intensity and experimental study of the relationship between heart rate and rating perceived exertion (RPE). Through monitoring the training process of a provincial women\'s sitting volleyball team, it is found that there is a significant positive correlation between athletes\' RPE and average heart rate. This result shows that by monitoring the change in heart rate and RPE of athletes, athletes\' training state and physical condition can be more accurately understood. The results reveal that through the use of big data technology and monitoring experiments, it is found that heart rate and RPE are effective monitoring indicators, which can scientifically reflect the load intensity during sitting volleyball training. The conclusions provide coaches with a more scientific basis for making training plans and useful references for sports involving people with disabilities.

The reward after-effect of effort expenditure refers to the phenomenon that previous effort investment changes the subjective value of rewards when obtained. However, the neural mechanisms underlying the after-effects of effort exertion are still not fully understood. We investigated the modulation of reward after-effects by effort type (cognitive vs. physical) through the lens of neural dynamics. Thirty-two participants performed a physically or cognitively demanding task during an effort phase and then played a simple gambling game during a subsequent reward phase to earn monetary rewards while their electroencephalogram (EEG) was recorded. We found that previous effort expenditure decreased electrocortical activity during feedback evaluation. Importantly, this effort effect occurred in a domain-general manner during the early stage (as indexed by the reward positivity) but in a domain-specific manner during the later and more elaborative stage (as indexed by the P3 and delta oscillation) of reward evaluation. Additionally, effort expenditure enhanced P3 sensitivity to feedback valence regardless of effort type. Our findings suggest that cognitive and physical effort, although bearing some surface resemblance to each other, may have dissociable neural influences on the reward after-effects.

Effort expenditure not only prospectively discounts the reward associated with it but also retrospectively adds the subjective value of reward, which is known as the effort paradox. This study aimed to resolve the effort paradox during reward evaluation through the critical lens of neural dynamics and its potential moderators. In total, 40 participants completed an effort-reward task wherein they exerted varying physical efforts to obtain an opportunity to win monetary rewards by active or passive decision-making. We found that after-effects of physical effort expenditure during reward evaluation unfolded as an effort paradox over time, manifesting itself as an effort discounting effect during the reward positivity (RewP) period but as an effort enhancement effect during the late positive potential (LPP) interval. Then, we found a dynamic balance between the discounting effect and the enhancement effect such that the more effort discounted the RewP at the early stage, the more effort increased the LPP at the late stage. Moreover, we observed that the effort-reward relationship was modulated by perceived control such that it increased the reward sensitivity effect and decreased the effort discounting effect. Together, our findings provide new insights into the neural mechanisms underlying the after-effects of physical effort exertion during reward evaluation.

BACKGROUND: Basketball players\' external and internal training loads have been extensively monitored. However, no study has evaluated if pre-training athlete-reported conditions influence them. Therefore, this study investigated the effects of athlete-reported pre-training well-being and recovery on subsequent external load intensity, perceived exertion scores and their ratio (efficiency index) in youth basketball training.
METHODS: The external load (EL) intensity (EL∙min-1), ratings of perceived exertion (RPE) and efficiency index (EL∙min-1:RPE) of 15 youth basketball players (age: 15.2±0.3 years) were monitored during team-based training sessions. Before each session, players reported their levels of perceived recovery (using a modified 10-point Total Quality Recovery, TQR, scale), fatigue, sleep quality, muscle soreness, mood, and stress. Statistical analyses were performed via linear mixed models.
RESULTS: EL∙min-1 was higher when player reported better pre-training recovery (P= 0.001). Higher RPE scores and lower efficiency indexes were registered in players reporting better pre-training conditions, respectively. Specifically, RPE scores were higher when players reported better TQR, fatigue, muscle soreness and stress scores (all P<0.05), while training efficiency was, conversely, lower in correspondence of better TQR and sleep (all P<0.05).
CONCLUSIONS: This study identified influences of athlete-reported pre-training well-being and recovery on subsequent external intensity, RPE and efficiency index in youth basketball players. Recovery and well-being indicators could be monitored seen their influence on subsequent training loads. Current findings can be considered by basketball sport scientist when selecting athlete monitoring questionnaires and when interpreting training load outputs.

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Rating of perceived exertion (RPE) can serve as a more convenient and economical alternative to heart rate (HR) for exercise intensity control. This study aims to explore the influence of factors, such as indicators of demographic, anthropometric, body composition, cardiovascular function and basic exercise ability on the relationship between HR and RPE, and to develop the model predicting RPE from HR. 48 healthy participants were recruited to perform an incrementally 6-stage pedaling test. HR and RPE were collected during each stage. The influencing factors were identified with the forward selection method to train Gaussian Process regression (GPR), support vector machine (SVM) and linear regression models. Metrics of R2, adjusted R2 and RMSE were calculated to evaluate the performance of the models. The GPR model outperformed the SVM and linear regression models, and achieved an R2 of 0.95, adjusted R2 of 0.89 and RMSE of 0.52. Indicators of age, resting heart rate (RHR), Central arterial pressure (CAP), body fat rate (BFR) and body mass index (BMI) were identified as factors that best predicted the relationship between RPE and HR. It is possible to use GPR model to estimate RPE from HR accurately, after adjusting for age, RHR, CAP, BFR and BMI.