Implementing Preexposure Prophylaxis for HIV Prevention in a Statewide Correctional System in the United States.

HIV disproportionately affects populations experiencing incarceration. Preexposure prophylaxis (PrEP) is an effective approach to preventing HIV acquisition among populations at increased risk of acquiring HIV. Yet few, if any, efforts have been made to offer PrEP in correctional settings. Beginning in November 2019, the Rhode Island Department of Corrections (RIDOC) implemented a systemwide PrEP initiation program with linkage to PrEP care in the community upon reentry. Incarcerated individuals identified as being potentially at increased risk of HIV acquisition during standard clinical screenings and medical care were referred to a PrEP care provider for potential PrEP initiation. Of the 309 people who met with a PrEP care provider, 35% (n = 109; 88 men, 21 women) agreed to initiate PrEP while incarcerated. Clinical testing and evaluation were completed for 82% (n = 89; 69 men, 20 women) of those who agreed to initiate PrEP. Of those, 54% (n = 48; 29 men, 19 women) completed the necessary clinical evaluation to initiate PrEP, were determined to be appropriate candidates for PrEP use, and had the medication delivered to a RIDOC facility for initiation. Only 8 people (4 men, 4 women) were successfully linked to a PrEP care provider in the community after release. The RIDOC experience demonstrates notable levels of PrEP interest and moderate levels of PrEP uptake among this population. However, PrEP engagement in care after release and persistence in taking PrEP when in the community were relatively poor, indicating a need to better understand approaches to overcoming barriers to PrEP care in this unique setting.

Py-Feat: Python Facial Expression Analysis Toolbox.

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state-of-the-art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00191-4.

HRMAn 2.0: Next-generation artificial intelligence-driven analysis for broad host-pathogen interactions.

To study the dynamics of infection processes, it is common to manually enumerate imaging-based infection assays. However, manual counting of events from imaging data is biased, error-prone and a laborious task. We recently presented HRMAn (Host Response to Microbe Analysis), an automated image analysis program using state-of-the-art machine learning and artificial intelligence algorithms to analyse pathogen growth and host defence behaviour. With HRMAn, we can quantify intracellular infection by pathogens such as Toxoplasma gondii and Salmonella in a variety of cell types in an unbiased and highly reproducible manner, measuring multiple parameters including pathogen growth, pathogen killing and activation of host cell defences. Since HRMAn is based on the KNIME Analytics platform, it can easily be adapted to work with other pathogens and produce more readouts from quantitative imaging data. Here we showcase improvements to HRMAn resulting in the release of HRMAn 2.0 and new applications of HRMAn 2.0 for the analysis of host-pathogen interactions using the established pathogen T. gondii and further extend it for use with the bacterial pathogen Chlamydia trachomatis and the fungal pathogen Cryptococcus neoformans.