Multicenter clinical comparative evaluation of Alinity m HIV-1 assay performance.

Abstract Background Accurate, rapid detection of HIV-1 RNA is critical for early diagnosis, treatment decision making, and long-term management of HIV-1 infection. Objective We evaluated the diagnostic performance of the Alinity m HIV-1 assay, which uses a dual target/dual probe design against highly conserved target regions of the HIV-1 genome and is run on the fully automated Alinity m platform. Study design This was an international, multisite study that compared the diagnostic performance of the Alinity m HIV-1 assay to four commercially available HIV-1 assays routinely used in nine independent clinical laboratories. Alinity m HIV-1 assay precision, detectability, and reproducibility was compared across four study sites. Results The Alinity m HIV-1 assay produced comparable results to currently available HIV-1 assays (correlation coefficient >0.995), with an overall bias of -0.1 to 0.10 Log10 copies/mL. The Alinity m HIV-1 assay and its predecessor m2000 HIV-1 assay demonstrated comparable detection of 16 different HIV-1 subtypes (R2 = 0.956). A high level of agreement (>88 %) between all HIV-1 assays was seen near clinical decision points of 1.7 Log10 copies/mL (50 copies/mL) and 2.0 Log10 copies/mL (200 copies/mL). Alinity m HIV-1 assay precision was 0.08 and 0.21 Log10 copies/mL at VLs of 1000 and 50 copies/mL, respectively, with a high level of detectability (≥97 % hit rate) and reproducibility across sites. Conclusions The Alinity m HIV-1 assay provides comparable diagnostic accuracy to current HIV-1 assays, and when run on the Alinity m system, has the capacity to shorten the time between diagnosis and treatment

sameerd/DiffusionTensorImaging: Release done for zenodo archive purposes

<p>A release of latest version of code so that it can be archived.</p&gt

Inferring protein fitness landscapes from laboratory evolution experiments.

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds

gymrek-lab/TRTools: v5.1.0

<p>New features:</p> <ul> <li>Added prancSTR for mosaicism detection</li> <li>Added simTR for simulating NGS reads with stutter errors at TRs</li> </ul&gt

gymrek-lab/TRTools: v5.0.2

Toolkit for genome-wide analysis of tandem repeat

gymrek-lab/TRTools: v5.1.1

<p>Bug fixes:</p> <ul> <li>Remove stray files from source distribution (#195)</li> </ul&gt

Characterising HIV-1 transmission in Victoria, Australia: a molecular epidemiological studyResearch in context

Summary: Background: In Australia the incidence of HIV has declined steadily, yet sustained reduction of HIV transmission in this setting requires improved public health responses. As enhanced public health responses and prioritisation of resources may be guided by molecular epidemiological data, here we aimed to assess the applicability of these approaches in Victoria, Australia. Methods: A comprehensive collection of HIV-1 pol sequences from individuals diagnosed with HIV in Victoria, Australia, between January 1st 2000 and December 31st 2020 were deidentified and used as the basis of our assessment. These sequences were subtyped and surveillance drug resistance mutations (SDRMs) identified, before definition of transmission groups was performed using HIV-TRACE (0.4.4). Phylodynamic methods were applied using BEAST (2.6.6), assessing effective reproductive numbers for large groups, and additional demographic data were integrated to provide a high resolution view of HIV transmission in Victoria on a decadal time scale. Findings: Based on standard settings for HIV-TRACE, 70% (2438/3507) of analysed HIV-1 pol sequences were readily assigned to a transmission group. Individuals in transmission groups were more commonly males (aOR 1.50), those born in Australia (aOR 2.13), those with probable place of acquisition as Victoria (aOR 6.73), and/or those reporting injectable drug use (aOR 2.13). SDRMs were identified in 375 patients (10.7%), with sustained transmission of these limited to a subset of smaller groups. Informative patterns of epidemic growth, stabilisation, and decline were observed; many transmission groups showed effective reproductive numbers (Re) values reaching greater than 4.0, representing considerable epidemic growth, while others maintained low Re values. Interpretation: This study provides a high resolution view of HIV transmission in Victoria, Australia, and highlights the potential of molecular epidemiology to guide and enhance public health responses in this setting. This informs ongoing discussions with community groups on the acceptability and place of molecular epidemiological approaches in Australia. Funding: National Health and Medical Research Council, Australian Research Council