Comparison of HPV DNA testing in cervical exfoliated cells and tissue biopsies among HIV-positive women in Kenya

HIV-positive women are infected with human papillomavirus (HPV) (especially with multiple types), and develop cervical intraepithelial neoplasia (CIN) and cervical cancer more frequently than HIV-negative women. We compared HPV DNA prevalence obtained using a GP5+/6+ PCR assay in cervical exfoliated cells to that in biopsies among 468 HIV-positive women from Nairobi, Kenya. HPV prevalence was higher in cells than biopsies and the difference was greatest in 94 women with a combination normal cytology/normal biopsy (prevalence ratio, PR = 3.7; 95% confidence interval, CI: 2.4-5.7). PR diminished with the increase in lesion severity (PR in 58 women with high-grade squamous intraepithelial lesions (HSIL)/CIN2-3 = 1.1; 95% CI: 1.0-1.2). When HPV-positive, cells contained 2.0- to 4.6-fold more multiple infections than biopsies. Complete or partial agreement between cells and biopsies in the detection of individual HPV types was found in 91% of double HPV-positive pairs. The attribution of CIN2/3 to HPV16 and/or 18 would decrease from 37.6%, when the presence of these types in either cells or biopsies was counted, to 20.2% when it was based on the presence of HPV16 and/or 18 (and no other types) in biopsies. In conclusion, testing HPV on biopsies instead of cells results in decreased detection but not elimination of multiple infections in HIV-positive women. The proportion of CIN2/3 attributable to HPV16 and/or 18 among HIV-positive women, which already appeared to be lower than that in HIV-negative, would then further decrease. The meaning of HPV detection in cells and random biopsy from HIV-positive women with no cervical abnormalities remains unclear. What\u27s new? Assignment of human papillomavirus (HPV) types to individual cervical lesions is essential for the understanding of the biology of different HPV types, efficacy of HPV vaccines, and design of detection assays. Such attribution is however hampered in HIV-positive women by the high proportion of multiple HPV infections. This study is the first to systematically compare HPV detection in paired cervical exfoliated cells and cervical tissue biopsies. HPV testing using biopsies instead of cells results in decreased detection of multiple infections in HIV-positive women. Exclusive reliance on biopsies also decreased the proportion of CIN2/3 attributable to vaccine-preventable HPV16 and/or 18 infection

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement

hugovk/pypistats 0.4.0

Python interface to PyPI Stats AP

hugovk/top-pypi-packages: Release 2024.01

A regular dump of the most-downloaded packages from PyP

hugovk/top-pypi-packages: Release 2023.12

A regular dump of the most-downloaded packages from PyP

Pooch: A friend to fetch your data files

Scientific software is usually created to acquire, analyze, model, and visualize data. As such, many software libraries include sample datasets in their distributions for use in documentation, tests, benchmarks, and workshops. A common approach is to include smaller datasets in the GitHub repository directly and package them with the source and binary distributions (e.g., scikit-learn (Pedregosa et al., 2011) and scikit-image (Van der Walt et al., 2014) do this). As data files increase in size, it becomes unfeasible to store them in GitHub repositories. Thus, larger datasets require writing code to download the files from a remote server to the user’s computer. The same problem is faced by scientists using version control to manage their research projects. While downloading a data file over HTTPS can be done easily with modern Python libraries, it is not trivial to manage a set of files, keep them updated, and check for corruption. For example, scikit-learn (Pedregosa et al., 2011), Cartopy (Met Office, n.d.), and PyVista (Sullivan & Kaszynski, 2019) all include code dedicated to this particular task. Instead of scientists and library authors recreating the same code, it would be best to have a minimalistic and easy to set up tool for fetching and maintaining data files.Fil: Uieda, Leonardo. University of Liverpool; Reino UnidoFil: Soler, Santiago Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto Geofísico Sismológico Volponi; ArgentinaFil: Rampin, Rémi. University of New York; Estados UnidosFil: Kemenade, Hugo van. No especifíca;Fil: Turk, Matthew. University of Illinois. Urbana - Champaign; Estados UnidosFil: Shapero, Daniel. University of Washington; Estados UnidosFil: Banihirwe, Anderson. National Center for Atmospheric Research; Estados UnidosFil: Leeman, John. Leeman Geophysical; Estados Unido

cta-observatory/pyirf: v0.10.1 – 2023-09-15

*pyirf* is a python3-based library for the generation of Instrument Response Functions (IRFs) and sensitivities for the Cherenkov Telescope Array (CTA

python-pillow/Pillow: 10.0.1

https://pillow.readthedocs.io/en/stable/releasenotes/10.0.1.htm