Cardiac Resynchronization Therapy (CRT) is generally indicated for heart failure patients with a left bundle branch block (LBBB). "Strict" LBBB criteria have been proposed as a better predictor of benefit from CRT. Automatic detection of "strict" LBBB criteria may improve outcomes for heart failure patients by reducing high false positive rates in LBBB detection. This study proposes an algorithm to automatically detect "strict" LBBB, developed and tested using ECGs made available via the International Society of Computerized Electrocardiology (ISCE) LBBB initiative. The dataset consists of 12-lead Holter ECGs recorded before the therapy from the MADIT-CRT clinical trial. The algorithm consists of multi-lead QRS complex detection using length transform, a support vector machine (SVM) classifier to identify QS-or rS-configurations and identification of mid-QRS notching and slurring by analyzing the variation of first and second derivatives of the signals respectively. The algorithm achieved an accuracy of 80%, sensitivity of 86%, specificity of 73%, positive predictive value (PPV) of 81% and negative predictive value of 79% on the training set. It achieved accuracy, sensitivity, specificity, PPV and NPV of 81%, 88%, 75%, 79% and 85% on the test set. High sensitivity to minor slurring and errors in QRS detection result in low specificity for LBBB detection.

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Introduction: Selenium (Se) may have a protective effect against some selected cancers. Ovarian cancer is ranked as one of the major killers of all gynecological malignancies worldwide. The objective of this study is to find the relationship between selenium intake and Epithelial Ovarian Cancer risk in women who have not had an oophorectomy. Methods: A comprehensive electronic search was carried out according to the prepared strategy from the starting date of the PubMed/Medline, EMBASE, Scopus, Proquest, and Web of Science databases up to 30th of September 2022 without limitations related to language and publication status. Studies were screened by COVIDENCE. Cohort studies, case-control studies, cross-sectional analytical studies, ecological studies, and randomized control studies were included, and descriptive studies were excluded from the systematic review. The exposure of interest is high selenium intake from either food sources or supplements and also high measures of selenium in blood, toenails, or other biological samples, and high measures of serum selenoproteins. Data extraction will be done. New Castle Ottawa Scale will be used to assess the bias of observational studies. The findings will be synthesized first via a narrative description. If data permits results will be displayed via forest plots. All analyses will be conducted using STATA-17. Discussion: Ovarian cancer is the most fatal gynecological malignancy among women. Due to the lack of recommended screening tools, the identification of modifiable effective risk factors and preventive tools are essential to reduce ovarian cancer burden. Selenium is a powerful antioxidant, therefore it prevents cell damage. It was proven in some studies that selenium protects against the development of some selected cancers. Therefore it is envisaged to find whether there is an inverse relationship between selenium and ovarian cancer for future preventive strategies. Systematic review registration: Registered in the International Prospective Register of Systematic Reviews (PROSPERO)- CRD42022356472

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. Neural oscillations exhibit phase-dependent associations with cognitive functions, and tools to manipulate local oscillatory phases can affect communication across remote brain regions. A recent study demonstrated that multi-channel tACS can generate electric fields with a phase gradient or traveling waves in the brain. Computational simulations using phasor algebra can predict the phase distribution inside the brain and aid in informing parameters in tACS experiments. However, experimental validation of computational models for multi-phase tACS is still lacking. Here, we develop such a framework for phasor simulation and evaluate its accuracy using in vivo recordings in nonhuman primates. We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated conductivity of tissues. Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.

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