Male member break Institutional example of Fourteen situations

BACKGROUND KCNE1 loss-of-function variants cause type 5 long QT syndrome (LQT5). However, most alleged LQT5-causative KCNE1 variants were identified before the true rate of background genetic variation was appreciated fully. OBJECTIVE The purpose of this study was to reassess the clinical and electrophysiological (EP) phenotypes associated with KCNE1 variants detected in a single-center LQTS cohort. METHODS Retrospective analysis of 1026 LQTS patients was used to identify those individuals with isolated KCNE1 ultra-rare variants (minor allele frequency [MAF] less then 0.0004 in the Genome Aggregation Database [gnomAD]). After classification according to American College of Medical Genetics (ACMG) guidelines, variants of uncertain significance (VUS) were characterized in vitro using whole-cell patch-clamp technique. Trichostatin A HDAC inhibitor Lastly, the clinical phenotype observed in ACMG pathogenic/likely pathogenic (P/LP) KCNE1-positive individuals was assessed. RESULTS Overall, 6 KCNE1 variants were identified in 38 of 1026 LQTS patients (3.7%). Based on existing data, 2 KCNE1 variants (p.Asp76Asn-KCNE1, p.Arg98Trp-KCNE1) were classified as P/LP. Whereas the p.Ser28Leu-KCNE1 VUS conferred a loss-of-function EP phenotype (72% reduction in IKs current) and was upgraded to an LP variant, the 3 remaining KCNE1 VUS (p.Arg67Cys-KCNE1, p.Arg67His-KCNE1, p.Ser74Leu-KCNE1) were indistinguishable from wild type. Collectively, the phenotype observed in p.Ser28Leu-KCNE1-, p.Asp76Asn-KCNE1-, and p.Arg98Trp-KCNE1-positive individuals (n = 22) was relatively weak (91% asymptomatic; average QTc 444 ± 19 ms; 27% with a maladaptive QTc response during exercise/recovery). CONCLUSION This study indicates that p.Ser28Leu-KCNE1 may be an LQT5-causative substrate analogous to p.Asp76Asn-KCNE1 and p.Arg98Trp-KCNE1. However, the weak phenotype and cumulative gnomAD MAF (42/141,156) associated with these P/LP variants suggest LQT5/KCNE-LQTS may be a more common/weaker form of LQTS than anticipated previously. BACKGROUND Inappropriate therapy is a common adverse effect in patients with an implantable cardioverter-defibrillator (ICD) that may be prevented by appropriate programming. OBJECTIVE The purpose of this study was to assess the outcomes of device programming based on a 2015 HRS/EHRA/APHRS/SOLAECE expert consensus statement and a 2019 focused update on optimal ICD programming and testing. METHODS Consecutive patients who underwent ICD insertion for primary prevention from 2014-2016 at 3 centers were included in the retrospective analysis. Patients were classified into 2 groups based on the tachycardia programming at the time of implant guideline concordant group (GC) and non-guideline concordant group (NGC). Kaplan-Meier analysis and Cox proportional hazard models were used to estimate freedom from ICD therapy (antitachycardia pacing or shock), ICD shock, and death. RESULTS A total of 772 patients were included in the study (mean age 63.3 ± 13.8 years). Of this total, 258 patients (33.4%) were in the GC group and 514 patients (66.6%) were in the NGC group. During mean follow-up of 2.02 ± 0.91 years, guideline concordant programming was associated with a 53% reduction in ICD therapy (P  less then .01) and 50% reduction in ICD shock (P = .02). There were no significant differences in mortality (6% in GC group vs11% in NGC group; P = .22). CONCLUSION Only one-third of the studied population had an ICD device programmed in concordance with current guidelines. ICD programming based on the current guidelines was associated with a significantly lower rate of ICD therapy and shock without changes in mortality during intermediate-term follow-up. Published by Elsevier Inc.This paper evaluates use of the Threshold of Toxicological Concern (TTC) approach to assess safety of botanical preparations that may contain potentially genotoxic constituents, based on estimation of the fraction that may be genotoxic. A database of 107 chemical constituents of botanicals was compiled and their potential for genotoxicity evaluated from published data. Forty-three constituents met the criteria for potential genotoxicity. Concentration data on their occurrence in plants provided 2878 data points; the majority were in the low ppm level (range 0.00001-139,965 ppm, by dry weight). Weibull models of the quantitative distribution data were used to calculate 95th percentile values for chemical concentrations, analysing the dataset according to their presence in botanicals (i) as a single chemical, (ii) as two or more chemicals from the same chemical group, or (iii) as two or more chemicals from different chemical groups. The highest 95th percentile concentration value from these analyses was 1.8%. Using the TTC value of 0.15 μg/person per day for potentially genotoxic substances proposed in 2004, this value of 1.8% was used to derive an adjusted TTC value of 10 μg of plant material on a dry weight basis/person per day for assessment of potentially genotoxic substances in botanicals. A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates. CONTEXT Healthcare professionals (HCP) currently judge pain presence and intensity in patients with delirium despite the lack of a valid, standardized assessment protocol. However, little is known about how they make these judgements. This information is essential to develop a valid and reliable assessment tool. OBJECTIVES to identify pain cues that HCP report utilizing to judge pain in patients with delirium and to examine whether the pain cues differed based on patient cognitive status and delirium subtype. METHODS Mixed qualitative-quantitative design. Doctors and nurses were recruited. All participants provided written informed consent and prior to the recorded interview, demographic information was collected; then participants were asked to describe their practices and beliefs regarding pain assessment and management with older patients who are cognitively intact and patients with delirium. Interviews were transcribed verbatim and coded for pain cues. Coded data were imported into SPSS to conduct bivariate analyses. RESULTS The pain cue self-report was stated more often for intact than for delirium patients χ2 (1, N = 106) = 22.56, p less then 0.001. HCP s stated yelling χ2 (2, N = 159) = 11.14, p=0.004, when describing pain in hyperactive than in hypoactive and mixed delirium patients; and significantly more HCP s stated grimace χ2 (2, N = 159) = 6.88, p=0.03, when describing pain in hypoactive than hyperactive and mixed patients. CONCLUSION This study outlines how HCP conduct pain assessment in patients who are delirious and, also, identifies pain behaviour profiles for the subtypes of delirium. CONTEXT Responding to emotion cues is an essential skill for communicating with patients and families, but many healthcare trainees have difficulty applying this skill within the context of a complex conversation. OBJECTIVES We created an original online module to facilitate deliberate practice of a three-skill framework for responding to emotion cues during complex or non-linear serious illness conversations. METHODS Our original online module employs a gamebook format which prompts trainees to engage in focused, repetitive practice of three well-defined skills for responding to emotion cues in a simulated family conference. We implemented the module as a part of a communication skills curriculum for interns rotating in the intensive care unit. After completing the module, all interns answered an open-ended survey question about their perceived skill acquisition. Results were analyzed by a qualitative method and coded into themes. RESULTS 71% of interns (n=65/92) completed the online module and open-ended survey question. 89% of participants responded that they would use a NURSE statement (naming, understanding, respecting, supporting, exploring) in response to an emotion cue. Nearly two-thirds of participants articulated their rationale for using NURSE statements (eg preparing patients to process complex medical information, eliciting information about patient perspective.) CONCLUSIONS Our online emotion cue module is a novel tool for deliberate practice of advanced skills for responding to emotion cues in serious illness conversations. In future studies, we will investigate whether our module's efficacy is enhanced by utilizing it as a part of a flipped classroom curriculum with an in-person simulation session. ETHNOPHARMACOLOGICAL RELEVANCE Walnut leaf (WL) is a hypoglycemic herbal medication with blood glucose-lowering activity that can affect diabetes mellitus (DM). However, the active components of WL and the mechanisms by which these compounds affect DM are unclear. AIM OF STUDY This study aimed to determine these effective ingredients and elucidate the potential mechanisms by which they affect DM via ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) coupled with network pharmacology analysis. MATERIALS AND METHODS First, UHPLC-Q-Orbitrap HRMS was utilized to identify components of WL. Second, the putative targets of the components were identified and predicted based on chemical similarity and online databases. Third, the key candidate targets and potential active components were identified through topological analysis of a component-disease target interaction network. Finally, interactions between active components and therapeutic targets were confirmed by molecular docking analysis. RESULTS One hundred and thirty components were identified in WL, among which 38 were considered potentially bioactive, as they showed hypoglycemic effects. Among these 38, 8 key active components possessed high similarities and shared 4 targets with approved drugs. These findings were confirmed by molecular docking analysis. CONCLUSION The approach combining UHPLC-Q-Orbitrap HRMS with network pharmacology analysis is a rapid and effective tool to identify potentially bioactive constituents in medicinal plants and prescriptions.