Tamsulosin combined with solifenacin versus tamsulosin monotherapy for male lower urinary tract symptoms: a meta-analysis

<p>To evaluate the efficacy and safety of tamsulosin and solifenacin combination therapy compared with tamsulosin monotherapy for male lower urinary tract symptoms (LUTS).</p> <p>We identified all eligible studies that compared tamsulosin and solifenacin combination therapy with tamsulosin monotherapy for male LUTS (up to January 2015). The fixed- or random-effects model was selected depending on the proportion of heterogeneity.</p> <p>Seven articles were identified as eligible for this meta-analysis, with a total of 3063 participants. Synthetic data showed combination therapy had significant improvements in Storage International Prostate Symptom Score (WMD = −0.60; 95% CI: −0.81 to −0.38, <i>P</i> < 0.0001), quality of life (WMD = −0.23; 95% CI: −0.34 to −0.11, <i>P</i> < 0.0001), micturitions per 24 hours (WMD = −0.70; 95% CI: −0.86 to −0.55, <i>P</i> < 0.0001) and urgency episodes per 24 hours (WMD = −0.26; 95% CI: −0.48 to −0.05, <i>P</i> = 0.018). The incidence of adverse effects in the tamsulosin and solifenacin combined therapy group (30.82%) was similar to the tamsulosin monotherapy group (25.75%). Acute urinary retention was seldom reported in the studies and no clinically significant changes regarding <i>Qmax</i> were showed in our meta-analysis.</p> <p>Tamsulosin and solifenacin combination therapy may be a reasonable option for male LUTS patients, especially for those who have significant storage symptoms. However, PVR should be measured during treatment to assess the increase in PVR or the incidence of AUR.</p

DataSheet_1_CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer.docx

ObjectivesAlthough the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa.MethodsA retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non–muscle–invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model.ResultsAccording to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis.ConclusionsA deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer.</p

Characteristics and results of the present study compared with the former five meta-analyses.

<p>Azoo-azoospermia; OAT-oligoasthenoteratozoospermia; Oligo-oligozoospermia</p><p>HB, hospital-based controls; PB, population-based controls</p><p>+, positive result</p><p>−, negative result</p><p>NA, not available</p><p>Characteristics and results of the present study compared with the former five meta-analyses.</p

Flow diagram of study selection.

<p>Flow diagram of study selection.</p

Funnel plot for publication bias of <i>MTHFR</i> 677C>T and infertility risk (CT+TT vs. CC) in overall populations.

<p>The horizontal line in the funnel plot indicates the random-effects summary estimate. Sloping lines indicate the expected 95% CI for a given SE.</p

Quality assessment based on the Newcastle-Ottawa Scale of studies included in this meta-analysis^a.

<p>^aA study can be awarded a maximum of one star for each numbered item except for the item Control for important factor or additional factor.</p><p>^bA maximum of two stars can be awarded for Control for important factor or additional factor.</p><p>Quality assessment based on the Newcastle-Ottawa Scale of studies included in this meta-analysis<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121147#t002fn001" target="_blank">^a</a>.</p

Characteristics of eligible studies in the meta-analysis of <i>MTHFR</i> 677C>T polymorphism and male infertility.

<p>Azoo-azoospermia; OAT-oligoasthenoteratozoospermia; oligo-oligozoospermia</p><p>HB, hospital-based controls; PB, population-based controls</p><p>HWE, Hardy Weinberg Equilibrium</p><p>NA, not available</p><p>Characteristics of eligible studies in the meta-analysis of <i>MTHFR</i> 677C>T polymorphism and male infertility.</p

Forest plots of <i>MTHFR</i> 677C>T polymorphism and male infertility risk (CT+TT vs. CC).

<p>[<b>A</b> for overall populations; <b>B</b> for ethnicity subgroup; <b>C</b> for sperm concentration subgroup; <b>D</b> for control sources subgroup]. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the study-specific weight (inverse of the variance). Diamonds represent the pooled OR and 95% CI. ^athe 37^th reference, ^bthe 49^th reference</p