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Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen's kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy. [ABSTRACT FROM AUTHOR]

Soil pathogens play important roles in shaping soil microbial diversity and controlling ecosystem functions. Though climate and local environmental factors and their influences on fungal pathogen communities have been examined separately, few studies explore the relative contributions of these factors. This is particularly crucial in eco‐fragile regions, which are more sensitive to environmental changes. Herein we investigated the diversity and community structure of putative soil fungal pathogens in cold and dry grasslands on the Tibetan Plateau, using high‐throughput sequencing. The results showed that steppe soils had the highest diversity of all pathogens and plant pathogens; contrastingly, meadow soils had the highest animal pathogen diversity. Structural equation modelling revealed that climate, plant, and soil had similar levels of influence on putative soil fungal pathogen diversity, with total effects ranging from 52% to 59% (all p < 0.001), with precipitation exhibiting a stronger direct effect than plant and soil factors. Putative soil fungal pathogen community structure gradually changed with desert, steppe, and meadow, and was primarily controlled by the interactions of climate, plant, and soil factors rather than by distinct factors individually. This finding contrasts with most studies of soil bacterial and fungal community structure, which generally report dominant roles of individual environmental factors. [ABSTRACT FROM AUTHOR]