Notes

An revise about medications for the menopause signs.
The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements.

Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared 1) Unadjusted AgD network meta-analysis (NMA) - the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and modure work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.
Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.
Bacteriophages play important roles in the evolution of bacteria and in the emergence of new pathogenic strains by mediating the horizontal transfer of virulence genes. Pasteurella multocida is responsible for different disease syndromes in a wide range of domesticated animal species. However, very little is known about the influence of bacteriophages on disease pathogenesis in this species.

Temperate bacteriophage diversity was assessed in 47 P. multocida isolates of avian (9), bovine (8), ovine (10) and porcine (20) origin. Induction of phage particles with mitomycin C identified a diverse range of morphological types representing both Siphoviridae and Myoviridae family-types in 29 isolates. Phage of both morphological types were identified in three isolates indicating that a single bacterial host may harbour multiple prophages. DNA was isolated from bacteriophages recovered from 18 P. multocida isolates and its characterization by restriction endonuclease (RE) analysis identified 10 different RE types. Phage of identical RE types were identified in certain closely-related strains but phage having different RE types were present in other closely-related isolates suggesting possible recent acquisition. The host range of the induced phage particles was explored using plaque assay but only 11 (38%) phage lysates produced signs of infection in a panel of indicator strains comprising all 47 isolates. Notably, the majority (9/11) of phage lysates which caused infection originated from two groups of phylogenetically unrelated ovine and porcine strains that uniquely possessed the toxA gene.

Pasteurella multocida possesses a wide range of Siphoviridae- and Myoviridae-type bacteriophages which likely play key roles in the evolution and virulence of this pathogen.
Pasteurella multocida possesses a wide range of Siphoviridae- and Myoviridae-type bacteriophages which likely play key roles in the evolution and virulence of this pathogen.
Comparative phylogeographic studies on rainforest species that are widespread in Central Africa often reveal genetic discontinuities within and between biogeographic regions, indicating (historical) barriers to gene flow, possibly due to repeated and/or long-lasting population fragmentation during glacial periods according to the forest refuge hypothesis. The impact of forest fragmentation seems to be modulated by the ecological amplitude and dispersal capacities of each species, resulting in different demographic histories. EED226 Moreover, while multiple studies investigated the western part of Central Africa (Lower Guinea), few have sufficiently sampled the heart of the Congo Basin (Congolia). In this study, we look for genetic discontinuities between populations of the widespread tropical tree Scorodophloeus zenkeri Harms (Fabaceae, Detarioideae) in Central Africa. Additionally, we characterize genetic diversity, selfing rate and fine-scale spatial genetic structure within populations to estimate the gene disp resulting from past forest fragmentation can persist for a long time before being erased by gene flow.
Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding, dyad binding, groove binding, and gyre spanning. However, there are substantial experimental challenges in measuring nucleosome binding modes for thousands of TFs in different species.

We present a computational prediction of the binding modes based on TF protein sequences. With a nested cross-validation procedure, our model outperforms several fine-tuned off-the-shelf machine learning (ML) methods in the multi-label classification task. Our binary classifier for the EB mode performs better than these ML methods with the area under precision-recall curve achieving 75%. The end preference of most TFs is consistent with low nucleosome occupancy around their binding site in GM12878 cells. The nucleosome occupancy data is used as an alternative dataset to confirm the superiority of our EB classifier.

We develop the first ML-based approach for efficient and comprehensive analysis of nucleosome binding modes of TFs.
We develop the first ML-based approach for efficient and comprehensive analysis of nucleosome binding modes of TFs.
Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio.

Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.
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