A growing understanding of the benefits of exercise over the past few decades has prompted researchers to take an interest in the possibilities of exercise therapy. Because each sport has its own set of characteristics and physiological complications that tend to occur during exercise training, the effects and underlying mechanisms of exercise remain unclear. Thus, the first step in probing the effects of exercise on different diseases is the selection of an optimal exercise protocol. This review summarizes the latest exercise prescription treatments for 26 different diseases: musculoskeletal system diseases (low back pain, tendon injury, osteoporosis, osteoarthritis, and hip fracture), metabolic system diseases (obesity, type 2 diabetes, type 1 diabetes, and nonalcoholic fatty liver disease), cardio-cerebral vascular system diseases (coronary artery disease, stroke, and chronic heart failure), nervous system diseases (Parkinson's disease, Huntington's disease, Alzheimer's disease, depression, and anxiety disorders), respiratory system diseases (chronic obstructive pulmonary disease, interstitial lung disease, and after lung transplantation), urinary system diseases (chronic kidney disease and after kidney transplantation), and cancers (breast cancer, colon cancer, prostate cancer, and lung cancer). Each exercise prescription is displayed in a corresponding table. The recommended type, intensity, and frequency of exercise prescriptions are summarized, and the effects of exercise therapy on the prevention and rehabilitation of different diseases are discussed.

Metabolic diseases, featured with dysregulated energy homeostasis, have become major global health challenges. Patients with metabolic diseases have high probability to manifest multiple complications in lipid metabolism, e.g. obesity, insulin resistance and fatty liver. Therefore, targeting the hub genes in lipid metabolism may systemically ameliorate the metabolic diseases, along with the complications. Stearoyl-CoA desaturase 1(SCD1) is a key enzyme that desaturates the saturated fatty acids (SFAs) derived from de novo lipogenesis or diet to generate monounsaturated fatty acids (MUFAs). SCD1 maintains the metabolic and tissue homeostasis by responding to, and integrating the multiple layers of endogenous stimuli, which is mediated by the synthesized MUFAs. It critically regulates a myriad of physiological processes, including energy homeostasis, development, autophagy, tumorigenesis and inflammation. Aberrant transcriptional and epigenetic activation of SCD1 regulates AMPK/ACC, SIRT1/PGC1α, NcDase/Wnt, etc, and causes aberrant lipid accumulation, thereby promoting the progression of obesity, non-alcoholic fatty liver, diabetes and cancer. This review critically assesses the integrative mechanisms of the (patho)physiological functions of SCD1 in metabolic homeostasis, inflammation and autophagy. For translational perspective, potent SCD1 inhibitors have been developed to treat various types of cancer. We thus discuss the multidisciplinary advances that greatly accelerate the development of SCD1 new inhibitors. In conclusion, besides cancer treatment, SCD1 may serve as the promising target to combat multiple metabolic complications simultaneously.

NLRP3 is an important pattern recognition receptor in the innate immune system, and its activation induces a large number of pro-inflammatory cytokines, IL-1β and IL-18 which are involved in the development of various diseases. In recent years, it has been suggested that mitochondria are the platform for NLRP3 inflammasome activation. Additionally, exercise is considered as an important intervention strategy to mediate the innate immune responses. Generally, chronic moderate-intensity endurance training, resistance training and high-intensity interval training inhibit NLRP3 inflammasome activation in response to various pathological factors. In contrast, acute exercise activates NLRP3 inflammasome. However, the mechanisms by which exercise regulates NLRP3 inflammasome activation are largely unclear. Therefore, the mechanism of NLRP3 inflammasome activation is discussed mainly from the perspective of mitochondria in this review. Moreover, the effect and potential mechanism of exercise on NLRP3 inflammasome are explored, hoping to provide new target for relevant research.

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision.