Is mechanical bowel preparation mandatory for elective colon surgery? A systematic review and meta-analysis.

BACKGROUND: Growing evidence demonstrates minimal impact of mechanical bowel preparation (MBP) on reducing postoperative complications following elective colectomy. This study investigated the necessity of MBP prior to elective colonic resection. METHOD: A systematic literature review was conducted across PubMed, Ovid, and the Cochrane Library to identify studies comparing the effects of MBP with no preparation before elective colectomy, up until May 26, 2023. Surgical-related outcomes were compiled and subsequently analyzed. The primary outcomes included the incidence of anastomosis leakage (AL) and surgical site infection (SSI), analyzed using Review Manager Software (v 5.3). RESULTS: The analysis included 14 studies, comprising seven RCTs with 5146 participants. Demographic information was consistent across groups. No significant differences were found between the groups in terms of AL ((P = 0.43, OR = 1.16, 95% CI (0.80, 1.68), I2 = 0%) or SSI (P = 0.47, OR = 1.20, 95% CI (0.73, 1.96), I2 = 0%), nor were there significant differences in other outcomes. Subgroup analysis on oral antibiotic use showed no significant changes in results. However, in cases of right colectomy, the group without preparation showed a significantly lower incidence of SSI (P = 0.01, OR = 0.52, 95% CI (0.31, 0.86), I2 = 1%). No significant differences were found in other subgroup analyses. CONCLUSION: The current evidence robustly indicates that MBP before elective colectomy does not confer significant benefits in reducing postoperative complications. Therefore, it is justified to forego MBP prior to elective colectomy, irrespective of tumor location.

Single-target detection of Oncomelania hupensis based on improved YOLOv5s.

To address the issues of low detection accuracy and poor effect caused by small Oncomelania hupensis data samples and small target sizes. This article proposes the O. hupensis snails detection algorithm, the YOLOv5s-ECA-vfnet based on improved YOLOv5s, by using YOLOv5s as the basic target detection model and optimizing the loss function to improve target learning ability for specific regions. The experimental findings show that the snail detection method of the YOLOv5s-ECA-vfnet, the precision (P), the recall (R) and the mean Average Precision (mAP) of the algorithm are improved by 1.3%, 1.26%, and 0.87%, respectively. It shows that this algorithm has a good effect on snail detection. The algorithm is capable of accurately and rapidly identifying O. hupensis snails on different conditions of lighting, sizes, and densities, and further providing a new technology for precise and intelligent investigation of O. hupensiss snails for schistosomiasis prevention institutions.