Oxidative stress is the imbalance of the redox system in the body, which produces excessive reactive oxygen species, leads to multiple cellular damages, and closely relates to some pathological conditions, such as insulin resistance and inflammation. Meanwhile, exercise as an external stimulus of oxidative stress causes the changes of pathophysiological functions in the tissues and organs, including skeletal muscle. Exercise‐induced oxidative stress is considered to have different effects on the structure and function of skeletal muscle. Long‐term regular or moderate exercise‐induced oxidative stress is closely related to the formation of muscle adaptation, while excessive free radicals produced by strenuous or acute exercise can cause muscle oxidative stress fatigue and damage, which impacts exercise capacity and damages the body’s health. The present review systematically summarizes the relationship between exercise‐induced oxidative stress and the adaptions, damage, and fatigue in skeletal muscle, in order to clarify the effects of exercise‐induced oxidative stress on the pathophysiological functions of skeletal muscle.

Hypoxia-inducible factor-1α (HIF-1α) is widely distributed in human cells, and it can form different signaling pathways with various upstream and downstream proteins, mediate hypoxia signals, regulate cells to produce a series of compensatory responses to hypoxia, and play an important role in the physiological and pathological processes of the body, so it is a focus of biomedical research. In recent years, various types of HIF-1α inhibitors have been designed and synthesized and are expected to become a new class of drugs for the treatment of diseases such as tumors, leukemia, diabetes, and ischemic diseases. This article mainly reviews the structure and functional regulation of HIF-1α, the modes of action of HIF-1α inhibitors, and the application of HIF-1α inhibitors during the treatment of diseases.

Fatty acid amide hydrolase (FAAH) is a central component of the endocannabinoid system (ECS), where it primarily regulates intracellular levels of anandamide (AEA) through enzymatic hydrolysis. Although FAAH has been extensively studied in neural and immune contexts, its involvement in female reproductive physiology is receiving increasing attention. Accumulating evidence indicates that FAAH participates in several important ovarian processes, including follicular development, steroid hormone synthesis, ovulation, and luteal function. In this review, we outline the biochemical properties of FAAH and its spatial distribution in ovarian tissues, with a particular focus on how FAAH-mediated AEA metabolism contributes to intraovarian signaling. Furthermore, we highlight the potential implications of altered FAAH activity in ovarian disorders such as polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), and infertility. By integrating molecular observations with clinical findings, this work provides updated perspectives on FAAH as both a physiological regulator and a potential therapeutic target in reproductive medicine.

To address the challenge of insufficient capture of learnable functional connectivity and discriminative spatial-temporal features in motor imagery EEG (MI-EEG) decoding, this paper proposes a Spatial-Temporal Graph Convolutional Attentional Network (STGCAN-MI). The method first explicitly models the functional connectivity and temporal dependencies among electrodes through a learnable dynamic graph convolution module. It then introduces a lightweight parallel spatial-temporal attention module to perform adaptive recalibration of feature responses across channel and time dimensions, thereby enhancing critical discriminative information and suppressing irrelevant noise. Experiments on the BCI Competition IV dataset 2a (four-class) and 2b (binary-class) show that STGCAN-MI achieves average best classification accuracies of 83.17% and 87.50%, respectively, outperforming several representative baseline models. Ablation studies and visualization analysis validate the effectiveness of each core component. This research provides a new approach for developing high-performance decoding methods for motor imagery EEG.