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BackgroundSurgical ablation (SA) is a key treatment for atrial fibrillation (AF) patients undergoing heart surgery. However, direct comparisons between SA and non-mitral valve (non-MV) surgery alone are lacking. We performed a systematic review and meta-analysis comparing concomitant SA to isolated non-MV surgery in AF patients.MethodsMEDLINE, Embase and Cochrane were searched. Outcomes of interest were: (1) postoperative AF (POAF); (2) early all-cause mortality; (3) postoperative pacemaker implantation and (4) stroke. Additionally, a subgroup analysis comparing randomised controlled trials (RCTs) and propensity score-matched studies (PSM) was conducted. Risk ratios (RRs) and their respective 95% CI were calculated using a random effects model.ResultsAfter screening 6423 citations, we included 2 RCTs and 5 PSM studies encompassing 39 348 AF patients undergoing non-MV surgery, of whom 18 394 (46.7%) underwent SA. Compared with isolated non-MV surgery, SA was associated with significant POAF reduction (RR: 0.73; 95% CI: 0.67 to 0.79; I2=0%) and higher risk of postoperative pacemaker implantation (RR: 1.34; 95% CI: 1.14 to 1.57, I2=0%) compared with surgery alone. No differences were found in early all-cause mortality (RR: 0.96; 95% CI: 0.76 to 1.22; I2=65%) and postoperative stroke (RR: 1.06; 95% CI: 0.89 to 1.26; I2=0%). The subgroup analysis comparing RCTs and PSM showed significant consistency among the different designs.ConclusionsIn this meta-analysis, SA was associated with POAF reduction in non-MV surgery. In terms of safety, it was suggested that although no difference in early mortality and postoperative stroke was observed, SA had a higher risk of pacemaker implantation than isolated non-MV surgery.

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a \"fingerprint\" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.