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Abstract BackgroundSarcopenia is characterized by the loss of skeletal muscle mass and muscle function with increasing age. The skeletal muscle mass of older people who endure sarcopenia may be improved via the practice of strength training and tai chi. However, it remains unclear if the hybridization of strength exercise training and traditional Chinese exercise will have a better effect. ObjectiveWe designed a strength training and tai chi exercise hybrid program to improve sarcopenia in older people. Moreover, explainable artificial intelligence was used to predict postintervention sarcopenic status and quantify the feature contribution. MethodsTo assess the influence of sarcopenia in the older people group, 93 participated as experimental participants in a 24-week randomized controlled trial and were randomized into 3 intervention groups, namely the tai chi exercise and strength training hybrid group (TCSG; n=33), the strength training group (STG; n=30), and the control group (n=30). Abdominal computed tomography was used to evaluate the skeletal muscle mass at the third lumbar (L3) vertebra. Analysis of demographic characteristics of participants at baseline used 1-way ANOVA and χ2 ResultsA significant interaction effect was found in skeletal muscle density at the L3 vertebra, skeletal muscle area at the L3 vertebra (L3 SMA), grip strength, muscle fat infiltration, and relative skeletal muscle mass index (all PPF1 ConclusionsThe skeletal muscle area of older adults with sarcopenia may be improved by a hybrid exercise program composed of strength training and tai chi. In addition, we identified that the LightGBM classification model had the best performance to predict the reversion of sarcopenia.

Abstract Objective Through a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group. Methods Seventy five older adults with sarcopenia aged 60–75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p 0.05). The same was in post-term tests (p > 0.05). Conclusion Compared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia. Trial registration The trial was registered in ClinicalTrials.gov (NCT05767710).

Abstract Addressing the current complexities, costs, and adherence issues in the detection of forward head posture (FHP), our study conducted an exhaustive epidemiologic investigation, incorporating a comprehensive posture screening process for each participant in China. This research introduces an avant-garde, machine learning-based non-contact method for the accurate discernment of FHP. Our approach elevates detection accuracy by leveraging body landmarks identified from human images, followed by the application of a genetic algorithm for precise feature identification and posture estimation. Observational data corroborates the superior efficacy of the Extra Tree Classifier technique in FHP detection, attaining an accuracy of 82.4%, a specificity of 85.5%, and a positive predictive value of 90.2%. Our model affords a rapid, effective solution for FHP identification, spotlighting the transformative potential of the convergence of feature point recognition and genetic algorithms in non-contact posture detection. The expansive potential and paramount importance of these applications in this niche field are therefore underscored.

BackgroundAlzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level.MethodsWe collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes.ResultsWe identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability.ConclusionThe transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.

[Objectives] This study was conducted to explore the action mechanism of chemical components from stems of Isodon suzhouensis on prostate cancer based on UPLC-Q-TOF-MS/MS technique, network pharmacology, and molecular docking validation. #Methods] UPLC-Q-TOF-MS/MS was applied to search active components in stems of I. suzhouensis, and SwissTargetPrediction and PharmaMapper databases were used to predict their potential targets. The GeneCards database was adopted to screen the targets of prostate cancer, and the targets of the active components and the targets of prostate cancer were intersected to obtain common targets. A protein-protein interaction network was constructed, and also, an "active component-target-disease-pathway" network was constructed using Metascape for gene ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Moreover, AutoDock was adopted to perform molecular docking verification on key active components and core targets. #Results] Sixty three active components were screened from stems of I. suzhouensis, and they had 233 common targets with prostate cancer, mainly involving biological processes such as protein phosphorylation, response to hormones, active regulation of cell movement, cell response to lipids, and response to oxidative stress, as well as pathways in cancer, prostate cancer, micro ribonucleic acids in cancer, p53 signaling pathway, NF-KB signaling pathway and other signaling pathways, exerting anti-tumor effects. Molecular docking showed that the top 5 key active components had good binding ability with the top 5 core targets. [Conclusions] Active components from stems of I suzhouensis can exert therapeutic effects on prostate cancer through multiple targets and pathways. [ABSTRACT FROM AUTHOR]

[Objectives] Based on UPLC-Q-TOF-MS/MS, network pharmacology and molecular docking techniques, the mechanism of Euphorbia peplus in the treatment of Alzheimer's disease (AD) was studied. [Methods] The UPLC-Q-TOF-MS/MS technique was used to rapidly analyze the chemical components of E. peplus. Active components and potential targets of E. peplus were retrieved from TCMSP and Swiss Target Prediction database, and AD targets were screened using GeneCards database. The targets of E. peplus in the treatment of AD were obtained. The PPI network was constructed using String platform, and the network topology of Cytoscape software was used to compute and screen key targets, and the GO and KEGG pathway enrichment analysis was carried out in Metascape database to construct the "component-target-pathway-disease" network. Molecular docking was used to predict the binding properties of active ingredients and targets. [Results] The results of UPLC-Q-TOF-MS/MS showed that 83 compounds were identified from E. peplus, including 19 terpenoids, 10 phenolic acids and phenols, 16 flavonoids, 2 phenylpropanoids, 4 coumarins, 1 alkaloid, 1 anthraquinone and 30 other compounds. The results of network pharmacological analysis showed that 82 active ingredients were screened, and 279 common targets were identified for the treatment of AD, among which the key targets were ALB (albumin), GAPDH (glyceraldehyde triphosphate dehydrogenase), TNF (tumor necrosis factor), AKT1 (serine/threonine protein kinase 1), and IL6 (interleukin-6). KEGG enrichment analysis showed that key signaling pathways include cancer pathways, lipid and atherosclerosis, Alzheimer's disease, insulin resistance, serotonergic synapses, calcium signaling pathway, cAMP signaling pathway and other signaling pathways. Molecular docking results showed that 14-deoxyandrographolide, dehydroan-drographolide, licochalcone B, apigenin and naringenin may be the key components of E. peplus in the treatment of AD. [Conclusions] The results suggest that E. peplus can be used to treat Alzheimer's disease through multi-component, multi-target and multi-pathway. [ABSTRACT FROM AUTHOR]