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Introduction: People living with HIV (PLWHIV) often face barriers that can impede their ability to achieve optimal HIV prevention and care outcomes. Implementation strategies have been developed to address these barriers and improve the delivery and uptake of evidence-based interventions for PLWHIV. Despite the availability of implementation strategies, it remains unclear which strategies are most effective in improving HIV prevention and care outcomes among PLWHIV, and how they can be tailored to different populations and settings. This scoping review aims to explore the current state of knowledge on implementation strategies for addressing treatment barriers among people living with HIV (PLWHIV). Methods and analysis: The review will examine studies published in the past ten years that have evaluated implementation strategies for improving HIV prevention and care outcomes among PLWHIV. The review will include randomized controlled trials, quasi-experimental studies, and observational studies that have assessed the effectiveness of implementation strategies for addressing treatment barriers, such as medication adherence, retention in care, and engagement in HIV prevention and care services. The review will be conducted using the Joanna Briggs Institute (JBI) scoping review methodology, which involves a comprehensive search of multiple databases, including PubMed, Embase, and PsycINFO, as well as a manual search of the reference lists of relevant articles. Data extraction will be performed by two reviewers independently, and a narrative synthesis of the findings will be conducted. The scoping review will provide a comprehensive overview of the current state of knowledge on implementation strategies for addressing treatment barriers among PLWHIV, and identify gaps in the literature that may warrant further research. The findings of this scoping review will be useful for researchers, clinicians, and policy-makers in developing and implementing effective interventions to improve HIV prevention and care outcomes among PLWHIV. Ethics and dissemination: Ethical review is not required for scoping reviews since they involve the analysis of secondary data collected from publicly available sources. The synthesized data from the review will be shared with researchers, public health authorities, and other relevant stakeholders through peer-reviewed publications and conference presentations, ensuring proper dissemination of the findings.

Additional file 1: Figure S1. Illustration of 3’UTR-seq method and quality control. A. Overview of the QuantSeq data analysis pipeline. B. Genome browser screenshot of extended 3’UTRs for genes Pou1f1 (left) and Ghrhr (right). Gene name and gene model are shown on the bottom of each panel. Each track represents overlapping signal from 5-6 biological replicates. C. Summary of qPCR vs. 3’UTR-seq comparisons across samples in all samples. Bottom panel: bar plots showing the numbers of genes detected using qPCR (white bars) and 3’UTR seq (red bars). The Spearman correlation coefficients between two experiments are shown for each sample in the dot plot (middle panel) and as well as in a density plot (top panel). Figure S2. Correlation heatmaps for pituitary gland samples. Pearson correlation between samples is calculated based on (A) gene expression and (B) miRNA expression. Hierarchical clustering is performed based on pairwise Pearson correlation coefficients (PCCs). Heatmap color intensity represents PCCs (orange: lower correlation; purple: higher correlation). Sample conditions are shown in top bars. Green shades: different ages, blue: male samples; red: female samples. Figure S3. Characterization of sex-biased mRNAs and miRNAs. A. Gene expression heatmap of sex-biased genes at PD12 and PD22, and genes with significant sex-by-age effect between PD12 and PD22. Each row represents a gene and each column represents a sample. Column annotation bars indicate sample age and sex. Colors represent row-scaled log2 (normalized counts). Whether a gene is female-biased (red) or male-biased (blue) at each corresponding age is summarized by the row annotation bars on the left. Sex chromosome-linked genes are labeled with asterisks. Genes with significant sex-by-age interaction effect between PD12 and PD22 are bolded. B. Expression plots of all sex-biased miRNAs. Log2(normCounts) are plotted for each miRNA across ages. Large filled points represent median expression at each age and unfilled points represent each biological replicate. Red: female samples; blue: male samples. C. novel46 is a mirtron of Cacna1g. The precursor of novel46 is expressed from the last intron of Cacna1g (highlighted in blue). The canonical seed region (nucleotide positions 2-8) in the mature sequence of novel46 is bolded. Figure S4. Summary of gene co-expression modules. Left panel. Module expression profile (log2-transformed normalized counts (log2(normCounts)) scaled per gene across all samples) is plotted for genes within each co-expression module across profiled postnatal ages. Dotted lines: individual gene profiles, solid line: median expression profile of module genes. Number of genes within each module is labeled at the top of the plots. Red: female samples; blue: male samples. Right panel. Expression profile of top five hub genes for each module. log2(normCounts) is plotted across profiled postnatal ages. Large, filled points represent median expression at each age and unfilled points represent each biological replicate. Blue: male samples; red: female samples. Figure S5. Characterization of co-expression gene modules. A. Correlation heatmap based on module eigengene (first PC, obtained using “mod_summary()” function from “CEMitool”). Color scale represents pairwise Pearson correlation coefficients. B. Barplots showing the eigengene for each sample in each module. Samples are grouped by age and colored by sex (blue: male; red: female). C. Barplots showing pathway enrichment results for genes in each module. Each bar represents a pathway. Color represents pathway categories (BP: Biological Process; CC: Cellular Component; MF: Molecular Function). Number of module genes in the pathway is labeled. X axis: -log10(P-value) of each pathway. Figure S6. Co-expression module gene enrichment in single-nuclei RNA-seq data. Uniform Manifold Approximation and Projection (UMAP) dimension reduction representation of adult female and male mouse pituitary gland single-nuclei transcriptome integrated by sex from Ruf-Zamojski et al. 2021 (A) colored by cell type and (B) colored by sample sex. Samples derived from snap-frozen pituitaries were first merged between replicates for each sex (n=3/sex). Merged samples were then integrated between sexes. C. Enrichment heatmap of co-expression module genes within cell types. One-sided Kolmogorov–Smirnov (KS) test was performed to test for enrichment of each co-expression module gene within a given cell type compared to all other cell types. Color gradient represents KS test FDR-adjusted P-value for each gene (dark purple: high enrichment; gray: low enrichment). Only genes with FDR ≤ 0.05 in at least one cell type are plotted and only FDR < 0.05 are shown. Each column represents a cell type as labeled at the bottom of the heatmap. Each row represents a co-expression module gene and the genes are grouped by the co-expression module in which the gene was identified (labeled on the right). Breaks were added in the heatmap between co-expression modules. Colored boxes represent the level of significance based on a one-sided hypergeometric test to determine if a group of module genes with KS test FDR ≤ 0.05 was significantly enriched for a given cell type. D. Estimated cell-type proportions by RNA-seq deconvolution using Proportions in Admixture (WGCNA) changes across profiled ages of pituitary cell types without known sex differences in their proportions. Estimated cell-type proportions are plotted across postnatal ages along the x-axis. Large circles and triangles represent the mean cell-type proportion at each age and small circles and triangles represent each biological replicate. Lighter color, solid line, circle points: female samples; dark color, dotted line, triangle points: male samples. Wilcoxon test was performed to compare cell proportions between both sexes at each age (*P

Cystic Fibrosis (CF) is a genetic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The F508del and G542X are the most common mutations found in US patients, accounting for 86.4% and 4.6% of all mutations, respectively. The F508del causes deletion of the phenylalanine residue at position 508 and is associated with impaired CFTR protein folding. The G542X is a nonsense mutation that introduces a stop codon into the mRNA, thus preventing normal CFTR protein synthesis. Here, we describe the generation of CFTRF508del/F508del and CFTRG542X/G542X lambs using CRISPR/Cas9 and somatic cell nuclear transfer (SCNT). First, we introduced either F508del or G542X mutations into sheep fetal fibroblasts that were subsequently used as nuclear donors for SCNT. The newborn CF lambs develop pathology similar to CFTR−/− sheep and CF patients. Moreover, tracheal epithelial cells from the CFTRF508del/F508del lambs responded to a human CFTR (hCFTR) potentiator and correctors, and those from CFTRG542X/G542X lambs showed modest restoration of CFTR function following inhibition of nonsense-mediated decay (NMD) and aminoglycoside antibiotic treatments. Thus, the phenotype and electrophysiology of these novel models represent an important advance for testing new CF therapeutics and gene therapy to improve the health of patients with this life-limiting disorder.

Malaria Genomic Epidemiology Network The high prevalence of sickle haemoglobin in Africa shows that malaria has been a major force for human evolutionary selection, but surprisingly few other polymorphisms have been proven to confer resistance to malaria in large epidemiological studies. To address this problem, we conducted a multi-centre genome-wide association study (GWAS) of life-threatening Plasmodium falciparum infection (severe malaria) in over 11,000 African children, with replication data in a further 14,000 individuals. Here we report a novel malaria resistance locus close to a cluster of genes encoding glycophorins that are receptors for erythrocyte invasion by P. falciparum. We identify a haplotype at this locus that provides 33% protection against severe malaria (odds ratio= 0.67, 95% confidence interval= 0.60-0.76, P value= 9.5× 10-11) and is linked to polymorphisms that have previously been shown to have features of ancient balancing selection, on the basis of haplotype sharing between humans and chimpanzees. Taken together with previous observations on the malaria-protective role of blood group O, these data reveal that two of the strongest GWAS signals for severe malaria lie in or close to genes encoding the glycosylated surface coat of the erythrocyte cell membrane, both within regions of the genome where it appears that evolution has maintained diversity for millions of years. These findings provide new insights into the host-parasite interactions that are critical in determining the outcome of malaria infection.

RNA polymerase III (Pol III) transcription of tRNA genes is essential for generating the tRNA adaptor molecules that link genetic sequence and protein translation. By mapping Pol III occupancy genome-wide in mouse, rat, human, macaque, dog and opossum livers, we found that Pol III binding to individual tRNA genes varies substantially in strength and location. However, when we took into account tRNA redundancies by grouping Pol III occupancy into 46 anticodon isoacceptor families or 21 amino acid-based isotype classes, we discovered strong conservation. Similarly, Pol III occupancy of amino acid isotypes is almost invariant among transcriptionally and evolutionarily diverse tissues in mouse. Thus, synthesis of functional tRNA isotypes has been highly constrained, although the usage of individual tRNA genes has evolved rapidly.