The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.

Background: Cancer cachexia (CAC) reduces patient survival and quality of life. Developments of efficient therapeutic strategies are required for the CAC treatments. This long-term process could be shortened by the drug-repositioning approach which exploits old drugs approved for non-cachexia disease. Amiloride, a diuretic drug, is clinically used for treatments of hypertension and edema due to heart failure. Here, we explored the effects of the amiloride treatment for ameliorating muscle wasting in murine models of cancer cachexia.Methods: The CT26 and LLC tumor cells were subcutaneously injected into mice to induce colon cancer cachexia and lung cancer cachexia, respectively. Amiloride was intraperitoneally injected daily once tumors were formed. Cachexia features of the CT26 model and the LLC model were separately characterized by phenotypic, histopathologic and biochemical analyses. Plasma exosomes and muscle atrophy-related proteins were quantitatively analyzed. Integrative NMR-based metabolomic and transcriptomic analyses were conducted to identify significantly altered metabolic pathways and distinctly changed metabolism-related biological processes in gastrocnemius.Results: The CT26 and LLC cachexia models displayed prominent cachexia features including decreases in body weight, skeletal muscle, adipose tissue, and muscle strength. The amiloride treatment in tumor-bearing mice distinctly alleviated muscle atrophy and relieved cachexia-related features without affecting tumor growth. Both the CT26 and LLC cachexia mice showed increased plasma exosome densities which were largely derived from tumors. Significantly, the amiloride treatment inhibited tumor-derived exosome release, which did not obviously affect exosome secretion from non-neoplastic tissues or induce observable systemic toxicities in normal healthy mice. Integrative-omics revealed significant metabolic impairments in cachectic gastrocnemius, including promoted muscular catabolism, inhibited muscular protein synthesis, blocked glycolysis, and impeded ketone body oxidation. The amiloride treatment evidently improved the metabolic impairments in cachectic gastrocnemius.Conclusions: Amiloride ameliorates cachectic muscle wasting and alleviates cancer cachexia progression through inhibiting tumor-derived exosome release. Our results are beneficial to understanding the underlying molecular mechanisms, shedding light on the potentials of amiloride in cachexia therapy.

Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis

Lactate is a general compound fuel serving as the fulcrum of metabolism, which is produced from glycolysis and shuttles between different cells, tissues and organs. Lactate is usually accumulated abundantly in muscles during exercise. It remains unclear whether lactate plays an important role in the metabolism of muscle cells. In this research, we assessed the effects of lactate on myoblasts and clarified the underlying metabolic mechanisms through NMR-based metabonomic profiling. Lactate treatment promoted the proliferation and differentiation of myoblasts, as indicated by significantly enhanced expression levels of the proteins related to cellular proliferation and differentiation, including p-AKT, p-ERK, MyoD and myogenin. Moreover, lactate treatment profoundly regulated metabolisms in myoblasts by promoting the intake and intracellular utilization of lactate, activating the TCA cycle, and thereby increasing energy production. For the first time, we found that lactate treatment evidently promotes AMPK signaling as reflected by the elevated expression levels of p-AMPK and p-ACC. Our results showed that lactate as a metabolic regulator activates AMPK, remodeling the cellular metabolic profile, and thereby promoting the proliferation and differentiation of myoblasts. This study elucidates molecular mechanisms underlying the effects of lactate on skeletal muscle in vitro and may be of benefit to the exploration of lactate acting as a metabolic regulator.

Background: Regular exercise recommendations (ERs) and physical activity (PA) are crucial for the prevention and management of chronic diseases. However, creating effective exercise programs demand substantial time and specialized expertise from both medical and sports professionals. Large language models (LLMs), such as ChatGPT, offer a promising solution by helping create personalized ERs. While LLMs show potential, their use in exercise planning remains in its early stages and requires further exploration. Objectives: This study aims to systematically review and classify the applications of LLMs in ERs and PA. It also seeks to identify existing gaps and provide insights into future research directions for optimizing LLM integration in personalized health interventions. Methods: A scoping review methodology was used to identify studies related to LLM applications in ERs and PA. Literature searches were conducted in Web of Science, PubMed, IEEE, and arXiv for English language papers published up to March 21, 2024. Keywords included LLMs, chatbots, ERs, PA, fitness plan, and related terms. Two independent reviewers (XL and CH) screened and selected studies based on predefined inclusion criteria. Thematic analysis was used to synthesize findings, which were presented narratively. Results: An initial search identified 598 papers, of which 1.8% (11/598) of studies were included after screening and applying selection criteria. Of these, ChatGPT-based models were used in 55% (6/11) of the studies. In addition, 73% (8/11) of the studies used expert evaluations and user feedback to assess model usability, and 45% (5/11) of the studies used experimental designs to evaluate LLM interventions in ERs and PA. Key findings indicated that LLMs can generate tailored ERs, save time in clinical practice, and enhance safety by incorporating patient-specific data. They also increased engagement and supported behavior change. This made PA guidance more accessible, especially in remote or underserved communities. Conclusions: This review highlights the promising applications of LLMs in ERs and PA but emphasizes that they remain a supplement to human expertise. Expert validation is essential to ensure safety and mitigate risks. Future research should prioritize pilot testing, clinician training programs, and large-scale clinical trials to enhance feasibility, transparency, and ethical integration.

Gastric cancer (GC) is one of the deadliest cancers worldwide, and the progression of gastric carcinogenesis (GCG) covers multiple complicated pathological stages. Molecular mechanisms of GCG are still unclear. Here, we undertook NMR‐based metabolomic analysis of aqueous metabolites extracted from gastric tissues in an established rat model of GCG. We showed that the metabolic profiles were clearly distinguished among 5 histologically classified groups: control, gastritis, low‐grade gastric dysplasia, high‐grade gastric dysplasia (HGD), and GC. Furthermore, we carried out metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with the 4 pathological stages, including oxidation stress, choline phosphorylation, amino acid metabolism, Krebs cycle, and glycolysis. Three metabolic pathways were continually disturbed during the progression of GCG, including taurine and hypotaurine metabolism, glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism. Both the Krebs cycle and glycine, serine, and threonine metabolism were profoundly impaired in both the HGD and GC stages, potentially due to abnormal energy supply for tumor cell proliferation and growth. Furthermore, valine, leucine, and isoleucine biosynthesis and glycolysis were significantly disturbed in the GC stage for higher energy requirement of the rapid growth of tumor cells. Additionally, we identified potential gastric tissue biomarkers for metabolically discriminating the 4 pathological stages, which also showed good discriminant capabilities for their serum counterparts. This work sheds light on the molecular mechanisms of GCG and is of benefit to the exploration of potential biomarkers for clinically diagnosing and monitoring the progression of GCG.