Objective:To screen preoperative microvascular invasion (MVI)-related indicators in patients with hepatocellular carcinoma by machine learning, and to construct a predictive model for predicting MVI and evaluate it.Methods:The clinical data of hepatocellular carcinoma patients who underwent radical resection from January 2018 to March 2023 in General Hospital of Ningxia Medical University were retrospectively analyzed. A total of 437 patients were enrolled, including 325 males and 112 females, aged (56.3±13.6) years. The 437 patients were divided into a training set ( n=305) and a test set ( n=132) by computer-generated random numbers on a 7∶3 basis; the training set was used to construct the predictive model as well as to internally validate it by the five-fold cross-validation method, and the test set was used to externally validate the model. Two machine learning Boruta algorithm and LASSO regression, were used to screen MVI characteristic variables and construct multifactorial logistic regression prediction models. Receiver operating characteristic (ROC) curve, calibration curves, and decision curve were evaluated for predictive modeling, applying Shapley's additive explanatory analysis (SHAP) of the significance of key variables. Results:The intersection (5 variables) of 8 characteristic variables selected by Boruta algorithm and 8 variables selected by LASSO regression were selected: aspartate aminotransferase/lymphocyte ratio (ALR), tumor margin, intratumbral necrosis, tumor number and tumor maximum diameter, and the logistic regression model was constructed. The area under ROC curve for predicting the MVI were 0.77 (95% CI: 0.70-0.82) (training set), 0.76 (95% CI: 0.63-0.87) (validation set), and 0.84 (95% CI: 0.78-0.91) (test set). The prediction results of calibration curve logistic regression model were close to those of reagent, and the analysis of decision curve indicates that the model had good clinical application value. According to the mean absolute SHAP value, the order of importance was tumor margin, tumor maximum diameter, tumor number, ALR, and intratumoral necrosis. Conclusion:Tumor margin, tumor maximum diameter, tumor number, ALR and intratumoral necrosis were independent influencing factors for hepatocellular carcinoma associated with MVI, and the logistic regression model based on these factors was effective in predicting MVI.

BACKGROUND:Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM:To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS:The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.RESULTS:Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION:The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Objective: To explore the surgical effect of three-dimensional (3D) image reconstruction technology in pancreatoduodenectomy. Methods: The clinical records of 47 cases who underwent pancreatoduodenectomy between January 2018 and December 2019 at the department of hepatobiliary surgery of the General Hospital of Ningxia Medical University were retrospectively examined, including 23 males and 24 females, with an average age of 55.00 ± 10.06 years. All patients underwent enhanced computed tomography (CT), and the 3D images were reconstructed by uploading the CT imaging data. The pre-operation evaluation and treatment strategy were planned according to CT imaging and 3D data, respectively. The change of treatment strategy based on 3D evaluation, actual surgical procedure, tumor volume measured by 3D model, actual tumor volume, variants of hepatic artery, operation time, intraoperative blood loss, post-operation hospital stay and post-operation complications was recorded. Results: The treatment strategies were changed after 3D visualization in 10 (21.3%) out of 47 patients because of blood vessel and organ invasion by tumor. The surgical procedure was changed in three cases, and the surgical procedure was optimized and improved in seven cases. All surgical plans based on 3D visualization technology were matched with the actual surgical procedures. Tumor volume measured by 3D model was 19.69 ± 23.47 mL, post-operation actual tumor volume was 17.07 ± 20.29 mL, with no significant difference between them (t = 0.54, p = 0.59). Pearson’s correlation analysis showed statistical significance (r = 0.766, p = 0.00). The average operation time was 4.85 ± 1.75 h, median blood loss volume was 447.05 (50–5000) mL, and post-operation hospital stay was 26.13 ± 11.13 days. Six cases had pancreatic fistula, two cases had biliary leakage, and four cases had delayed gastric emptying. Ascites and pleural effusion was observed in three cases. Conclusions: 3D visualization technology can offer a precise and individualized surgical plan before operation, which might improve the safety of pancreatoduodenectomy, and has application value in preoperative planning.

目的:探讨补肾益精丸干预雷公藤多苷诱导睾丸支持细胞损伤的作用机制.方法:将36只SD大鼠按体质量随机分为无药血清组、雷公藤多苷组和补肾益精丸组,每组各12只,制备含药血清.体外培养小鼠睾丸支持细胞,取对数生长期细胞分为空白对照组、无药血清组、雷公藤多苷组和补肾益精丸组,每组4孔.血清干预后采用MTr法测定细胞增殖率,流式细胞仪测定细胞周期.结果:补肾益精丸组细胞增殖率高于雷公藤多苷组,差异具有统计学意义(P＜0.05),补肾益精丸组细胞增殖率与空白对照组比较,差异无统计学意义(P＞0.05).与空白对照组比较,无药血清组及雷公藤多苷组G1期细胞数增多,S期细胞数减少;补肾益精丸组与雷公藤多苷组比较,S期细胞数增多.结论:雷公藤多苷对睾丸支持细胞的增殖具有抑制作用,主要作用于细胞G1期及S期.补肾益精丸可有效拮抗雷公藤多苷对睾丸支持细胞的损伤.

目的 探索雷公藤多苷(GTW)对睾丸支持细胞增殖的影响机制.方法 制备GTW含药血清,与体外培养的睾丸支持细胞共培养,以观察GTW对睾丸支持细胞增殖的影响机制,同期与无药血清组、补肾中药组作对照;用MTT法测细胞增殖率,用流式细胞仪测细胞周期,用Western blot检测Caspase-9、Caspase-3蛋白表达.结果 (1)与无药血清组相比,GTW组睾丸支持细胞增殖率降低(P均<0.05),补肾中药组细胞增殖率变化差异无统计学意义(P>0.05);(2)细胞周期:与无药血清组相比,GTW组在G1期中细胞占比增多,S期细胞占比减少(P均<0.05);(3)与无药血清组相比,GTW组Caspase-9、Caspase-3蛋白表达增高(P均<0.05).结论 GTW抑制睾丸支持细胞增殖,可能与细胞凋亡有关.

目的 研究儿童体质与儿童过敏性紫癜发病的相关性.方法 依据中医体质学说,参照2015年杨寅编撰的儿童中医体质学量表,采用横断面调查研究的方法,通过对165例健康儿童与165例过敏性紫癜患儿的体质进行分析总结与归纳,探索体质因素在儿童过敏性紫癜的发病中具有何种影响力.结果 在165例健康儿童中,体质类型以平和质及痰湿证居多,分别占39.39％和26.06％;而165例过敏性紫癜患儿中,体质类型以阴虚质和湿热质为主,分别占42.42％和26.67％,两组间差异比较有统计学意义(P＜0.05).结论 健康儿童与过敏性紫癜患儿的中医体质类型分布存在显著性差异,阴虚质及湿热质儿童较其他类型儿童更易发生过敏性紫癜.