RESUMEN
Purpose: Hemorrhoids (HEM) are the most common perianal disease, but current observational studies have yielded inconsistent results in investigating the risk factors. Our further exploration of the risk factors will help prevent the disease. Patients and Methods: We conducted a two-sample bidirectional Mendelian randomization (MR) analysis using publicly available genome-wide association studies (GWAS) statistics from multiple consortia. The inverse-variance weighted (IVW) method was used for the primary analysis. We applied four complementary methods, including weighted median, weighted mode, MR-Egger regression, and Cochrane's Q value, to detect and correct the effects of horizontal pleiotropy. Results: Genetically determined constipation (OR = 0.97, 95% CI: 0.91-1.03, P = 0.28) and diarrhea (OR = 1.00, 95% CI: 0.99-1.01, P = 0.90) did not have a causal effect on HEM but stool frequency (OR = 1.28, 95% CI: 1.05-1.55, P = 0.01), waist-to-hip ratio adjusted for BMI (OR = 1.11, 95% CI: 1.06-1.64, P = 1.59×10-5), and order Burkholderiales (OR = 1.09, 95% CI = 1.04-1.14, p = 1.63×10-4) had a causal effect on. Furthermore, we found a significant causal effect of constipation on HEM in the reverse MR analysis (OR = 1.21, 95% CI: 1.13-1.28, P = 3.72×10-9). The results of MR-Egger regression, Weighted Median, and Weighted Mode methods were consistent with those of the IVW method. Horizontal pleiotropy was unlikely to distort the causal estimates, as indicated by the sensitivity analysis. Conclusion: Our MR analysis reveals a causal association between stool frequency and waist-to-hip ratio with HEM, despite variations in results reported by observational studies. Unexpectedly, we found a relationship between the order Burkholderiales in the gut flora and HEM, although the mechanism is unclear.
RESUMEN
Objective: This study aimed to optimize the CT images of anal fistula patients using a convolutional neural network (CNN) algorithm to investigate the anal function recovery. Methods: 57 patients with complex anal fistulas admitted to our hospital from January 2020 to February 2021 were selected as research subjects. Of them, CT images of 34 cases were processed using the deep learning neural network, defined as the experimental group, and the remaining unprocessed 23 cases were in the control group. Whether to process CT images depended on the patient's own wish. The imaging results were compared with the results observed during the surgery. Results: It was found that, in the experimental group, the images were clearer, with DSC = 0.89, precision = 0.98, and recall = 0.87, indicating that the processing effects were good; that the CT imaging results in the experimental group were more consistent with those observed during the surgery, and the difference was notable (P < 0.05). Furthermore, the experimental group had lower RP (mmHg), AMCP (mmHg) scores, and postoperative recurrence rate, with notable differences noted (P < 0.05). Conclusion: CT images processed by deep learning are clearer, leading to higher accuracy of preoperative diagnosis, which is suggested in clinics.