PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets.

Motivation: High-dimensional cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with the potential for biological relevance being overlooked. Here, we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments, such as identifying parent populations of interest, filtering of low-frequency populations and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with a local computer or server-based use. Results: In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimensional single-cell data. Availability and implementation: The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Statistical methods used to combine the effective reproduction number, [Formula: see text], and other related measures of COVID-19 in the UK.

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information.

Microcredentials training in pharmacy practice and education: an exploratory study of its viability and pharmacists' professional needs.

BACKGROUND: Microcredentials (MCs) are short courses that certify/recognise an individual's achievement of specific skills or knowledge. Schools of pharmacy could be well-placed to contribute to the continuing professional development (CPD) of pharmacists through the inclusion of MCs training in their programs. This study aimed to explore pharmacy professionals' views on the need and viability of MC courses globally. METHODS: Eleven semi-structured telephone interviews were conducted with pharmacy practitioners, policymakers, and academics across seven countries. The participants were selected using purposive sampling to explore information from varying pharmacy disciplines. Interviews were audio-recorded, transcribed verbatim, and analysed using a general inductive approach. RESULTS: Participants regarded MCs in pharmacy as an innovative idea, well-suited to the increasingly technology-driven world. They believe MCs provide easily accessible means of skills and knowledge acquisition that fulfils the needs of the pharmacy profession. MCs were also perceived as an alternative pathway of meeting the requirements of traditional CPD programmes. Many participants believe universities are well-suited to provide MCs; however, numerous challenges such as recognition, time and resources have been identified as potential barriers to enrolment and implementation. CONCLUSIONS: This study provides an insight into the views of pharmacy practitioners and academics on MCs, and their potential utility in pharmacy education and practice. The findings should help in the development of MCs that could be utilised by pharmacy practitioners around the world for CPD purposes. This study comes at a time when alternative models of teaching and learning are being explored as a direct result of the COVID-19 pandemic.

Echocardiographic changes following active heat acclimation.

Heat adaption through acclimatisation or acclimation improves cardiovascular stability by maintaining cardiac output due to compensatory increases in stroke volume. The main aim of this study was to assess whether 2D transthoracic echocardiography (TTE) could be used to confirm differences in resting echocardiographic parameters, before and after active heat acclimation (HA). Thirteen male endurance trained cyclists underwent a resting blinded TTE before and after randomisation to either 5 consecutive daily exertional heat exposures of controlled hyperthermia at 32°C with 70% relative humidity (RH) (HOT) or 5-days of exercise in temperate (21°C with 36% RH) environmental conditions (TEMP). Measures of HA included heart rate, gastrointestinal temperature, skin temperature, sweat loss, total non-urinary fluid loss (TNUFL), plasma volume and participant's ratings of perceived exertion (RPE). Following HA, the HOT group demonstrated increased sweat loss (p = 0.01) and TNUFL (p = 0.01) in comparison to the TEMP group with a significantly decreased RPE (p = 0.01). On TTE, post exposure, there was a significant comparative increase in the HOT group in left ventricular end diastolic volume (p = 0.029), SV (p = 0.009), left atrial volume (p = 0.005), inferior vena cava diameter (p = 0.041), and a significant difference in mean peak diastolic mitral annular velocity (e') (p = 0.044). Cardiovascular adaptations to HA appear to be predominantly mediated by improvements in increased preload and ventricular compliance. TTE is a useful tool to demonstrate and quantify cardiac HA.