Establishment and application of three predictive models of anastomotic leakage after rectal cancer sphincter-preserving surgery.

BACKGROUND: Anastomotic leakage (AL) occurs frequently after sphincter-preserving surgery for rectal cancer and has a significant mortality rate. There are many factors that influence the incidence of AL, and each patient's unique circumstances add to this diversity. The early identification and prediction of AL after sphincter-preserving surgery are of great significance for the application of clinically targeted preventive measures. Developing an AL predictive model coincides with the aim of personalised healthcare, enhances clinical management techniques, and advances the medical industry along a more precise and intelligent path. AIM: To develop nomogram, decision tree, and random forest prediction models for AL following sphincter-preserving surgery for rectal cancer and to evaluate the predictive efficacy of the three models. METHODS: The clinical information of 497 patients with rectal cancer who underwent sphincter-preserving surgery at Jincheng People's Hospital of Shanxi Province between January 2017 and September 2022 was analyzed in this study. Patients were divided into two groups: AL and no AL. Using univariate and multivariate analyses, we identified factors influencing postoperative AL. These factors were used to establish nomogram, decision tree, and random forest models. The sensitivity, specificity, recall, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the three models. RESULTS: AL occurred in 10.26% of the 497 patients with rectal cancer. The nomogram model had an AUC of 0.922, sensitivity of 0.745, specificity of 0.966, accuracy of 0.936, recall of 0.987, and accuracy of 0.946. The above indices in the decision tree model were 0.919, 0.833, 0.862, 0.951, 0.994, and 0.955, respectively and in the random forest model were 1.000, 1.000, 1.000, 0.951, 0.994, and 0.955, respectively. The DeLong test revealed that the AUC value of the decision-tree model was lower than that of the random forest model (P < 0.05). CONCLUSION: The random forest model may be used to identify patients at high risk of AL after sphincter-preserving surgery for rectal cancer owing to its strong predictive effect and stability.

Electrochemical Sulfoxidation of Thiols and Alkyl Halides.

Sulfoxides are actively engaged as versatile synthetic building blocks, chiral ligands, bioactive molecules, and function materials. However, their oxidative syntheses from thioethers are inevitably impeded by overoxidation, excess oxidants, and the tedious preparation of thioethers. To address these shortcomings, we report herein a highly selective electrochemical sulfoxidation reaction featuring the use of simple starting materials, i.e., thiols and alkyl halides, in a single operation.

Electrochemical fluorosulfonylation of styrenes.

An environmentally friendly and efficient electrochemical fluorosulfonylation of styrenes has been developed. With the use of sulfonylhydrazides and triethylamine trihydrofluoride, a diverse array of ß-fluorosulfones could be readily obtained. This reaction features mild conditions and a broad substrate scope, which could also be conveniently extended to a gram-scale preparation.