Vanderbilt University School of Medicine is a graduate medical school of Vanderbilt University located in Nashville, Tennessee. Located in the Vanderbilt University Medical Center on the southeastern side of the Vanderbilt University campus, the School of Medicine claims several Nobel laureates in the field of medicine. Through the Vanderbilt Health Affiliated Network, VUSM is affiliated with over 60 hospitals and 5,000 clinicians across Tennessee and five neighboring states, managing more than 2 million patient visits each year. It is considered one of the largest academic medical centers in the United States and is the primary resource for specialty and primary care in hundreds of adult and pediatric specialties for patients throughout the Mid-South.
Fluorescence recovery after photobleaching (FRAP) is a powerful, versatile, and widely accessible tool to monitor molecular dynamics in living cells that can be performed using modern confocal microscopes. Although the basic principles of FRAP are simple, quantitative FRAP analysis requires careful experimental design, data collection, and analysis. In this article, we discuss the theoretical basis for confocal FRAP, followed by step-by-step protocols for FRAP data acquisition using a laser-scanning confocal microscope for (1) measuring the diffusion of a membrane protein, (2) measuring the diffusion of a soluble protein, and (3) analyzing intracellular trafficking. Finally, data analysis procedures are discussed, and an equation for determining the diffusion coefficient of a molecular species undergoing pure diffusion is presented. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: How to set up a FRAP experiment Basic Protocol 2: Confocal FRAP measurements of the lateral diffusion of plasma membrane proteins and lipids Alternate Protocol 1: Lateral diffusion measurements for a rapidly diffusing soluble protein Alternate Protocol 2: FRAP analysis of intracellular trafficking kinetics Basic Protocol 3: Working with FRAP data Basic Protocol 4: Further analysis of FRAP data to obtain diffusion coefficients.
Surgical site infections (SSIs) after hernia repair are associated with longer hospital stays, increased readmission rates, and higher hospital costs. Patients undergoing colorectal and orthopedic surgeries during warmer months have been shown to have higher SSI risk. We investigated the relationship between season and SSI risk after hernia repair, hypothesizing that SSI risk is higher during warmer months. This retrospective cohort study used the American College of Surgeons (ACS) – National Surgical Quality Improvement Program (NSQIP) database to identify hernia repair patients from 2006 to 2021. We compared rates of any SSI between warm and cold seasons, defined based on admission quarter. Multi-variable and binomial logistic regression models were used to determine independent predictors of outcomes. Of the 826,636 patients in the final cohort, 400,329 (48.4
Membership in the Alpha Omega Alpha (AOA) honor society has long-term professional benefits, including improved match outcomes, yet racial and socioeconomic disparities in membership are well-documented. AOA selection processes across schools lack standardization, complicating understanding of the meaning of membership. To analyze transparency and methodology of AOA selection processes nationally and describe chapters’ acknowledgement of potential for bias. Researchers performed a mixed methods cross-sectional study of medical school websites describing AOA chapter eligibility and selection in the United States (US) from October 2023 to April 2024. Descriptive statistics were generated to characterize quantitative data, and thematic analysis was performed to identify patterns within extracted quotes. LCME-accredited, MD-granting medical schools in the US. Researchers iteratively refined a data extraction form focused on AOA chapter status, eligibility and selection criteria, and quotes acknowledging potential for bias. Both thematic and content analyses were used with extracted quotes, while descriptive statistics were calculated for quantitative data. Of 157 medical schools nationally, 131 (83
Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in theC. eleganshermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes inC. elegans. We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.
We have studied 21 435 unique randomized controlled trials (RCTs) from the Cochrane Database of Systematic Reviews (CDSR). Of these trials, 7224 (34%) have a continuous (numerical) outcome and 14 211 (66%) have a binary outcome. We find that trials with a binary outcome have larger sample sizes on average, but also larger standard errors and fewer statistically significant results. We conclude that researchers tend to increase the sample size to compensate for the low information content of binary outcomes, but not sufficiently. In many cases, the binary outcome is the result of dichotomization of a continuous outcome, which is sometimes referred to as "responder analysis". In those cases, the loss of information is avoidable. Burdening more participants than necessary is wasteful, costly, and unethical. We provide a method to convert a sample size calculation for the comparison of two proportions into one for the comparison of the means of the underlying continuous outcomes. This demonstrates how much the sample size may be reduced if the outcome were not dichotomized. We also provide a method to calculate the loss of information after a dichotomization. We apply this method to all the trials from the CDSR with a binary outcome, and estimate that on average, only about 60% of the information is retained after dichotomization. We provide R code and a shiny app at: https://vanzwet.shinyapps.io/info_loss/ to do these calculations. We hope that quantifying the loss of information will discourage researchers from dichotomizing continuous outcomes. Instead, we recommend they "model continuously but interpret dichotomously". For example, they might present "percentage achieving clinically meaningful improvement" derived from a continuous analysis rather than by dichotomizing raw data.
