RESUMEN

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

Asunto(s)

RESUMEN

Benign prostatic hyperplasia (BPH) is the most common benign disease of the prostate gland and is caused by benign hyperplasia of the smooth muscle cells and stromal cells in this important gland. BPH is also the most common disease underlying lower urinary tract symptoms (LUTS). The incidence of BPH increases with age and affects more than half of all men 50 years or older. BPH mainly exerts effects on urinary function and can seriously reduce a patient's quality of life. At present, treatment for BPH aims primarily to improve the quality of life and reduce the risk of BPH-related complications. Pharmacological therapy is recommended for moderate-to-severe cases of LUTS that are suggestive of BPH. A range of drugs is currently available to treat this condition, including α1-adrenoceptor antagonists, 5α-reductase inhibitors (5-ARIs), phosphodiesterase type 5 inhibitors (PDE5Is), muscarinic receptor antagonists (MRAs), ß3-adrenoceptor agonists, and plant extracts. Of these, the most commonly used drugs in the clinic are α1-adrenoceptor antagonists, 5-ARIs, and combination therapy. However, these drugs exert their effects via various mechanisms and are associated with adverse reactions. The purpose of this review is to provide current comprehensive perspectives on the mechanisms of action, efficacy, and adverse reactions associated with the drugs most commonly used for the treatment of BPH.

RESUMEN

Objective To investigate the expression and clinical significance of late endosomal/lysosomal adaptor,mitogen-activated protein kinase and mammalian target of rapamycin activator 3(LAMTOR3)in bladder carcinoma.Methods Oncomine and Expression Atlas were used to extract the useful mining gene chip database for analyzing the expression of LAMTOR3 in bladder carcinoma tissues and cell lines,and the correlation of LAMTOR3 with the clinicopathological features were analyzed.RT-PCR,Western blot,and immunohistochemistry were performed to detect the expression of LAMTOR3 in bladder carcinoma cell lines,specimens,and adjacent normal tissues for verifying the results exploited from the above databases.Results The Expression Atlas showed that LAMTOR3 had high expressions in Hs172.T,HT-1376,RT4,JMSU-1,and T24 cell lines among 20 bladder carcinoma cell lines,among which the LAMTOR3 expression was different.Oncomine reported that LAMTOR3 expression in bladder carcinoma,including invasive(t=2.857,P=0.005)and non-invasive carcinoma(t=3.105,P=0.003),was significantly higher than that in adjacent normal tissues.The expression of LAMTOR3 was positively correlated with pathological grade(P<0.05).The expressions of LAMTOR3 mRNA in bladder carcinoma cell lines,including UMUC3(t=10.84,P=0.0084),J82(t=21.75,P=0.0021),5637(t=45.88,P=0.0005),and T24(t=87.58,P=0.0001)were significantly higher than that in normal bladder cell line SV-HUC-1,while its expression in bladder carcinoma tissues was significantly higher than that in adjacent normal tissues(P<0.05),so was its protein level in tissues(P<0.05).Immunohistochemistry showed that LAMTOR3 protein was over-expressed in bladder carcinoma tissues;its level in invasive carcinoma tissues was higher than that in no-invasive carcinoma tissues and was related closely with the clinical stages(χ 2=9.189,P=0.002),pathological grades(χ 2=4.746,P=0.029),and lymphatic metastasis(χ 2=6.210,P=0.013)but had no significant correlation to sex(χ 2=0.965,P=0.326),age(χ 2=2.126,P=0.145),and distant metastasis(χ 2=1.261,P=0.261).Conclusion LAMTOR3 is highly expressed in bladder carcinoma cell lines and tissues and plays a key role in the development and progression of bladder carcinoma.

Asunto(s)

RESUMEN

Exosomes are 30-100 nm vesicles secreted from almost all types of cells.They contain various molecular constituents,including proteins,lipids,and RNA.As important mediators of cell-to-cell communication,exosomes are involved in a variety of physiological and pathological processes such as inflammatory reaction,cell proliferation and differentiation,tissue repair,immune signal transduction,and stress response.Exosomes can regulate and maintain the initiation and progression of many autoimmune diseases,especially rheumatoid arthritis.Meanwhile,exosomes may be a new biomarker for the diagnosis of rheumatoid arthritis and a potential treatment vector for this disease.

Asunto(s)