While smoking is widely acknowledged as a risk factor for rheumatoid arthritis (RA), the connection between secondhand smoke (SHS) exposure and RA in never-smoking adults remains limited and inconsistent. This study aims to explore and quantify this association using serum cotinine levels. We conducted a cross-sectional study with 14,940 adults who self-report as never smokers, using National Health and Nutrition Examination Survey data from 1999 to 2018. Based on previous literature, SHS exposure was categorized into four groups according to serum cotinine levels. Compared to individuals in the unexposed group (serum cotinine < 0.05 ng/mL), the adjusted odds ratio (OR) for RA was 1.37 (95% CI 1.14–1.64, p = 0.001) in the low exposure group (serum cotinine at 0.05 to 0.99 ng/mL) after adjusting for covariates. However, no significant association was found in the moderate exposure group (serum cotinine at 1 to 10 ng/mL) or the heavy exposure group (serum cotinine ≥ 10 ng/mL). Furthermore, we detected a non-linear, positively saturated correlation between the cotinine levels after log2 transformation and RA, with a turning point at approximately − 2.756 ng/mL (OR = 1.163, 95% CI 1.073–1.261, p = 0.0002). The stability of the results was confirmed by subgroup analysis.

A new strategy to enhance molecular sonosensitivity is disclosed via tuning molecular assembly behavior, achieving highly efficient sonodynamic therapy of septic arthritis in a rat model.

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making.Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the “feature_importances” method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model.Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction.Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

In cancer research, the extracellular matrix (ECM) of the tumor microenvironment (TME) was once regarded as a mere passive structural scaffold for tumor tissue, while the biomechanical cues generated by its dynamic physical remodeling have now emerged as crucial active regulators that drive tumor initiation, progression and metastasis. By sensing and transducing these abnormal mechanical signals (e.g., matrix stiffness, fluid shear stress) through four core mechanotransduction mechanisms (mechanosensitive ion channels, adhesive complexes, nuclear mechanotransducers, and cytoskeletal dynamics), tumor cells convert them into intracellular biochemical responses. This review begins with the ECM within the TME, exploring mechanosensing and mechanotransduction in the TME, to construct a multi-dimensional research system integrating ECM abnormal remodeling, core mechanotransduction mechanisms and the multi-faceted regulatory effects of mechanical signals on tumor progression. Furthermore, it discusses the application of biomechanical cues in clinical tumor diagnosis, as well as attempts at anti-tumor therapies involving artificial intervention in ECM components to alter its physical properties, thereby providing reference ideas for biomechanical research.