Comparison of one-year outcomes and quality of life between endovenous microwave ablation and high ligation and stripping of the great saphenous vein.

INTRODUCTION: The objective of this study is to evaluate and compare the effectiveness of endovenous microwave ablation (EMA) and high ligation and strippingn (HLS) of the great saphenous vein (GSV) in the treatment of varicose veins. METHODS: We included 182 patients in each EMA and HLS groups. Follow-up outcomes included AVVQ, VCSS, chronic venous insufficiency questionnaire-14 (CIVIQ14) score, clinical recurrence rate of varicose vein treatment, and patient satisfaction during the 1-year follow-up period. RESULTS: At the 1-year follow-up, no significant difference was found in the clinical recurrence rate of varicose veins between the EMA and HLS groups (p = .75). The duration of the operation and the length of hospital stay for patients in the EMA group was shorter than that for the HLS group (p < .01). The Aberdeen Varicose Vein Questionnaire (AVVQ), Venous Clinical Severity Score (VCSS) score, and ecchymosis were lower for patients who underwent EMA surgery (p < .01). CONCLUSION: Our research results confirm that EMA improves patients' quality of life with lower limb varicose veins, with EMA showing higher patient satisfaction.

Deciphering complex antibiotic resistance patterns in Helicobacter pylori through whole genome sequencing and machine learning.

Introduction: Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments. Investigating the genotype-phenotype connection for Hp using next-generation sequencing could enhance our understanding of this resistance. Methods: In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole-genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the development of support vector machine models. Results: Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively. Discussion: Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections. The application of whole-genome sequencing for Hp presents a promising pathway for advancing personalized medicine in this context.