The safety and efficacy of five surgical treatments in prostate enucleation: a network meta-analysis.

PURPOSE: The aim of our study was to investigate the comparative outcomes of five different energy types on surgical efficacy and postoperative recovery in patients with benign prostate hyperplasia. METHODS: The literature was systematically reviewed on December 1st, 2023, encompassing studies retrieved from PubMed, Embase, Web of Science, and The Cochrane Library databases that incorporated clinical studies of holmium laser enucleation of the prostate (HoLEP), Thulium:YAG laser enucleation of the prostate (ThuLEP), transurethral plasmakinetic enucleation of prostate (PKEP), diode laser enucleation of the prostate (DiLEP) and thulium fiber laser enucleation of the prostate (ThuFLEP) in the treatment of prostatic hyperplasia. Two independent reviewers extracted study data and conducted quality assessments using the Cochrane Collaboration's Risk of Bias tool and Newcastle-Ottawa Scale (NOS). Network meta-analysis (NMA) was employed to indirectly analyze the outcomes of endoscopic enucleation of the prostate (EEP) techniques. RESULTS: The study included a total of 38 studies, comprising 21 non-randomized controlled trials (nRCTs) and 17 randomized controlled trials (RCTs), incorporating five distinct techniques: holmium laser, Thulium:YAG laser, bipolar plasma, diode laser and thulium fiber laser. In comparing treatment durations, ThuLEP and HoLEP had shorter overall hospital stays than PKEP, while the enucleation time of ThuLEP and HoLEP was shorter than that of ThuFLEP. Moreover, the enucleation tissue weight of both thulium fiber laser and holmium laser was heavier than bipolar plasma. However, the analysis did not reveal any statistically significant variation in complications among the various types of enucleation. In postoperative follow-up, the IPSS at 3 months post-operation was superior in the Thulium:YAG laser group compared to the holmium laser group. The thulium fiber laser technique demonstrated significant advantages over other enucleation methods in terms of QoL and PVR at 12 months after surgery. CONCLUSION: Theoretical properties may vary among different energy sources; however, there are no discernible clinical differences in operation-related parameters, postoperative complications, and postoperative follow-up. Therefore, the choice of laser does not significantly impact the outcome. However, due to the limited number of included studies, future research should focus on larger sample sizes and multicenter investigations to further validate the findings of this study.

Associations between MTHFR gene polymorphisms (C677T and A1298C) and genetic susceptibility to prostate cancer: a systematic review and meta-analysis.

Background: The association between MTHFR gene polymorphisms (C677T and A1298C) and prostate cancer risk remains controversial. Methods: Two independent researchers searched the PubMed, Embase, Cochrane and Web of Science databases for all papers published up to 12/19/2023 and used various genetic models to evaluate the relationship between MTHFR polymorphisms and prostate cancer risk. Results: The meta-analysis included 26 case‒control studies with a total of 12,455 cases and 13,900 controls with the C677T polymorphism and 6,396 cases and 8,913 controls with the A1298C polymorphism. Overall, no significant association was found between the MTHFR gene polymorphisms and prostate cancer risk. However, the C677T polymorphism was associated with reduced prostate cancer risk in the Asian population (T allele vs. C allele: OR = 0.759, 95% CI 0.669-0.861, p < 0.001; TT + CT vs. CC: OR = 0.720, 95% CI 0.638-0.812, p < 0.001; TT vs. CC + CT: OR = 0.719, 95% CI 0.617-0.838, p < 0.001; TT vs. CC: OR = 0.620, 95% CI 0.522-0.737, p < 0.001); however, the A1298C polymorphism was associated with an increased risk in the mixed race group from the United States (CC + AC vs. AA: OR = 1.464, 95% CI 1.052-2.037, p = 0.024; AC vs. AA: OR = 1.615, 95% CI 1.037-2.514, p = 0.034). Conclusion: The meta-analysis suggested that MTHFR gene polymorphisms (C677T and A1298C) may have different effects on prostate cancer risk in specific populations.

Metabolic Syndrome Predicts Uric Acid Stones in the Upper Urinary Tract: Development and Validation of a Nomogram Model.

BACKGROUND: Accurately identifying uric acid stones is pivotal in determining the appropriate treatment strategy for patients. This study aimed to design an innovative nomogram to predict the occurrence of uric acid stones in the upper urinary tract. METHODS: This retrospective study examined 680 patients with urinary stones from October 2019 to September 2022. Risk factors were identified through univariate and multivariate logistic regression, leading to the development of a nomogram. This model's validity was then assessed internally using receiver operating characteristic (ROC) curves, the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). RESULTS: Our findings revealed that metabolic syndrome (odds ratio (OR) = 4.347, 95% confidence interval (CI) 1.306-14.466, p = 0.017), serum urea levels (OR = 1.004, 95% CI 1.143-2.002, p = 0.004), urinary pH (OR = 0.185, 95% CI 0.059-0.583, p = 0.004), urinary potassium (OR = 0.926, 95% CI 0.875-0.981, p = 0.009), and urinary calcium (OR = 0.693, 95% CI 0.492-0.977, p = 0.037) are independent factors for upper urinary tract uric acid stones. Utilizing the five variables, we developed a predictive nomogram. The AUC of the training cohort and the validation cohort were 0.917 (95% CI 0.871-0.963) and 0.914 (95% CI 0.850-0.978), respectively. Calibration curves indicated strong consistency in both cohorts, and the DCA revealed the model's clinical utility. CONCLUSIONS: We devised a reliable and user-friendly nomogram to predict uric acid stones in the upper urinary tract. It is based on metabolic syndrome, serum biochemical markers, and 24-hour urinary parameters. Key determinants include metabolic syndrome, serum urea, urinary pH, urinary potassium and urinary calcium.

Metabolic Syndrome Predicts Uric Acid Stones in the Upper Urinary Tract: Development and Validation of a Nomogram Model

Background: Accurately identifying uric acid stones is pivotal in determining the appropriate treatment strategy for patients. This study aimed to design an innovative nomogram to predict the occurrence of uric acid stones in the upper urinary tract. Methods: This retrospective study examined 680 patients with urinary stones from October 2019 to September 2022. Risk factors were identified through univariate and multivariate logistic regression, leading to the development of a nomogram. This model’s validity was then assessed internally using receiver operating characteristic (ROC) curves, the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: Our findings revealed that metabolic syndrome (odds ratio (OR) = 4.347, 95% confidence interval (CI) 1.306–14.466, p = 0.017), serum urea levels (OR = 1.004, 95% CI 1.143–2.002, p = 0.004), urinary pH (OR = 0.185, 95% CI 0.059–0.583, p = 0.004), urinary potassium (OR = 0.926, 95% CI 0.875–0.981, p = 0.009), and urinary calcium (OR = 0.693, 95% CI 0.492–0.977, p = 0.037) are independent factors for upper urinary tract uric acid stones. Utilizing the five variables, we developed a predictive nomogram. The AUC of the training cohort and the validation cohort were 0.917 (95% CI 0.871–0.963) and 0.914 (95% CI 0.850–0.978), respectively. Calibration curves indicated strong consistency in both cohorts, and the DCA revealed the model’s clinical utility. Conclusions: We devised a reliable and user-friendly nomogram to predict uric acid stones in the upper urinary tract. It is based on metabolic syndrome, serum biochemical markers, and 24-hour urinary parameters. Key determinants include metabolic syndrome, serum urea, urinary pH, urinary potassium and urinary calcium (AU)