Assessing the Impact of HIV Preexposure Prophylaxis Scale-Up on Gonorrhea Incidence Among Gay and Bisexual Men in Sydney: A Mathematical Modeling Study.

BACKGROUND: The rollout of preexposure prophylaxis (PrEP) for HIV prevention among gay and bisexual men (GBM) is associated with increases in condomless anal intercourse, potentially increasing the incidence of other sexually transmissible infections (STIs). METHODS: We developed an individual-based mathematical model to simulate the transmission of Neisseria gonorrhoeae among GBM in Sydney, accounting for changes in sexual practices, STI testing, and PrEP use. We calibrated and validated the model using reported incidence rates for HIV-positive and HIV-negative GBM from 2010 to 2019. Scenarios were run with varying PrEP uptake, PrEP-related STI testing, and PrEP-related sexual behavior and testing intervals up to 2030 to assess the impact of PrEP use on gonorrhea incidence. RESULTS: Preexposure prophylaxis uptake and associated 3-monthly STI testing from 2015 onward resulted in a predicted increase from 20 to 37 N. gonorrhoeae infections per 100 person-years among HIV-negative GBM by the end of 2020. This is lower than the counterfactual predictions of 45 per 100 person-years if PrEP were not scaled up and 48 per 100 person-years with nonadherence to 3-monthly STI testing. Increasing the time between STI tests for PrEP users by 1 month from 2018 results in the incidence rate among HIV-negative GBM increasing by 8% by 2030. If PrEP coverage doubles from 24% to 53%, incidence among HIV-negative GBM declines by ~25% by 2030. CONCLUSIONS: Behavior change due to widespread PrEP use may lead to significant increases in gonorrhea incidence in GBM, but the recommended quarterly STI testing recommended for PrEP users should reduce incidence by 18% by 2030.

Predicting aqueous solubility by QSPR modeling.

The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R2 greater than 0.7 and Q2 greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R2 and standard deviation.

Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublimation; here we describe several models that build upon the previous models for predicting the enthalpy of sublimation. The most robust and predictive model constructed using multiple linear regression, with the fewest number of descriptors for estimating this property, was obtained with an R2 of the training set of 0.71, an R2 of the test set of 0.62, and a standard deviation of 9.1 kJ mol-1. This model could be improved by training using a neural network, yielding an R2 of the training and test sets of 0.80 and 0.63, respectively, and a standard deviation of 8.9 kJ mol-1.

BioPPSy: An Open-Source Platform for QSAR/QSPR Analysis.

The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from https://sourceforge.net/projects/bioppsy/.

Race Making in a Penal Institution.

This article provides a ground-level investigation into the lives of penal inmates, linking the literature on race making and penal management to provide an understanding of racial formation processes in a modern penal institution. Drawing on 135 days of ethnographic data collected as an inmate in a Southern California county jail system, the author argues that inmates are subjected to two mutually constitutive racial projects--one institutional and the other microinteractional. Operating in symbiosis within a narrative of risk management, these racial projects increase (rather than decrease) incidents of intraracial violence and the potential for interracial violence. These findings have implications for understanding the process of racialization and evaluating the effectiveness of penal management strategies.

The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development.

The nasopharynx (NP) is a reservoir for microbes associated with acute respiratory infections (ARIs). Lung inflammation resulting from ARIs during infancy is linked to asthma development. We examined the NP microbiome during the critical first year of life in a prospective cohort of 234 children, capturing both the viral and bacterial communities and documenting all incidents of ARIs. Most infants were initially colonized with Staphylococcus or Corynebacterium before stable colonization with Alloiococcus or Moraxella. Transient incursions of Streptococcus, Moraxella, or Haemophilus marked virus-associated ARIs. Our data identify the NP microbiome as a determinant for infection spread to the lower airways, severity of accompanying inflammatory symptoms, and risk for future asthma development. Early asymptomatic colonization with Streptococcus was a strong asthma predictor, and antibiotic usage disrupted asymptomatic colonization patterns. In the absence of effective anti-viral therapies, targeting pathogenic bacteria within the NP microbiome could represent a prophylactic approach to asthma.

Elucidation of pathways driving asthma pathogenesis: development of a systems-level analytic strategy.

Asthma is a genetically complex, chronic lung disease defined clinically as episodic airflow limitation and breathlessness that is at least partially reversible, either spontaneously or in response to therapy. Whereas asthma was rare in the late 1800s and early 1900s, the marked increase in its incidence and prevalence since the 1960s points to substantial gene × environment interactions occurring over a period of years, but these interactions are very poorly understood (1-6). It is widely believed that the majority of asthma begins during childhood and manifests first as intermittent wheeze. However, wheeze is also very common in infancy and only a subset of wheezy children progress to persistent asthma for reasons that are largely obscure. Here, we review the current literature regarding causal pathways leading to early asthma development and chronicity. Given the complex interactions of many risk factors over time eventually leading to apparently multiple asthma phenotypes, we suggest that deeply phenotyped cohort studies combined with sophisticated network models will be required to derive the next generation of biological and clinical insights in asthma pathogenesis.