Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.
Background To determine the necessity of tourniquet use in arthroscopic meniscal repair by comparing outcomes including arthroscopic visibility, operative time, postoperative pain and subjective function of the knee joint. Methods This was a retrospective, single-centre, single-surgeon study. A total of 148 patients who underwent arthroscopic meniscal repair were allocated to the tourniquet group (n=82) or the nontourniquet group (n=66). The primary outcome measures were arthroscopic visibility and operative time. The secondary outcomes were postoperative pain measured by a visual analogue scale and subjective function of the knee joint measured by The International Knee Documentation Committee (IKDC) and Lysholm scores. Results The 2 groups did not differ in terms of age, male‒female ratio, body mass index, or operative side. There was no significant difference between the 2 groups regarding arthroscopic visibility and operative time. At 1 week postoperatively, the VAS score and Lysholm score of the nontourniquet group were better than those of the tourniquet group (P<0.05). The VAS score, Lysholm score, and IKDC score at 6 weeks and 3 months postoperatively were significantly improved compared to the preoperative status in both groups (P < 0.05). However, there was no significant difference in these indexes between the two groups at 6 weeks and 3 months postoperatively (P > 0.05). Conclusions Tourniquet use for arthroscopic meniscal repair does not affect primary outcome or secondary outcomes. Based on the results of the analysis, the use of a tourniquet is no longer advisable for routine arthroscopic meniscal repair. Level of Evidence:LEVEL III
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