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Objective: To investigate the anatomic risk factors of knee in patients with acute non-contact anterior cruciate ligament (aACL) ruptures to develop ramp lesions. Methods: A total of 202 subjects were retrospectively divided into three groups: (1) aACL ruptures combined with ramp lesions group (n = 76); (2) isolated ACL ruptures group (n = 56) and (3) normal controls group (n = 70). Quantitative morphological parameters on MRI were measured including: diameter of medial femoral condyle (MFC), anterior-posterior length and depth of medial tibial plateau (MTP AP length and depth), lateral posterior tibial slope (LPTS) and medial posterior tibial slope (MTPS), asymmetry of LPTS and MPTS (LMPTS), lateral meniscal slope (LMS), and medial meniscal slope (MMS). Results: The MTP AP length, MTP AP length/MFC diameter ratio, MTP depth, LPTS and the asymmetry of LMPTS showed significant differences among the three groups (p < 0.001). The risk factors associated with the ramp lesions including a longer MTP AP length (OR 1.17, 95% CI 1.00–1.44, p = 0.044), increased MTP depth (OR 1.91, 95% CI 1.22–3.00, p = 0.005) and lager ratio (OR 1.11, 95% CI 1.01–1.22, p = 0.036). The highest AUC was the MTP AP length/MFC diameter ratio (0.74; 95% CI, 0.66–0.82). The combination model increased higher accuracy (0.80; 95% CI, 0.72–0.88). Conclusion: Several bony anatomic characteristics of the knee, especially the morphology of medial tibia plateau, are additional risk factors for aACL ruptures to develop ramp lesions. Critical relevance statement: Predictive anatomic risk factors of the knee for patients with acute non-contact anterior cruciate ligament (aACL) ruptures to develop ramp lesions, especially the morphology of medial tibia plateau, are detectable by MRI. Key Points: Ramp lesion development can complicate aACL ruptures and requires specific treatment. Longer AP length and increased MTP depth are risk factors for concurrent ramp lesions. Identification of ramp lesions allows for the most appropriate treatment. [ABSTRACT FROM AUTHOR]

Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events. [ABSTRACT FROM AUTHOR]