Association between Use of HMG CoA Reductase Inhibitors and Mortality in HIV-Infected Patients

HIV infection is a disease associated with chronic inflammation and immune activation. Antiretroviral therapy reduces inflammation, but not to levels in comparable HIV-negative individuals. The HMG-coenzyme A reductase inhibitors (statins) inhibit several pro-inflammatory processes and suppress immune activation, and are a logical therapy to assess for a possible salutary effect on HIV disease progression and outcomes.Eligible patients were patients enrolled in the Johns Hopkins HIV Clinical Cohort who achieved virologic suppression within 180 days of starting a new highly active antiretroviral therapy (HAART) regimen after January 1, 1998. Assessment was continued until death in patients who maintained a virologic suppression, with right-censoring of their follow-up time if they had an HIV RNA > 500 copies/ml. Cox proportional hazards regression was used to assess statin use as a time-varying covariate, as well as other demographic and clinical factors.A total of 1538 HIV-infected patients fulfilled eligibility criteria, of whom 238 (15.5%) received a statin while taking HAART. There were 85 deaths (7 in statin users, 78 in non-users). By multivariate Cox regression, statin use was associated with a relative hazard of 0.33 (95% CI: 0.14, 0.76; P =  0.009) after adjusting for CD4, HIV-1 RNA, hemoglobin and cholesterol levels at the start of HAART, age, race, HIV risk group, prior use of ART, year of HAART start, NNRTI vs. PI-based ART, prior AIDS-defining illness, and viral hepatitis coinfection. Malignancy, non-AIDS-defining infection and liver failure were particularly prominent causes of death.Statin use was associated with significantly lower hazard of dying in these HIV-infected patients who were being effectively treated with HAART as determined by virologic suppression. Our results suggest the need for confirmation in other observational cohorts, and if confirmed, the need for a clinical trial of statin use in HIV infection

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Universal automated classification of the acoustic startle reflex using machine learning

The startle reflex (SR), a robust, motor response elicited by an intense auditory, visual, or somatosensory stimulus has been widely used as a tool to assess psychophysiology in humans and animals for almost a century in diverse fields such as schizophrenia, bipolar disorder, hearing loss, and tinnitus. Previously, SR waveforms have been ignored, or assessed with basic statistical techniques and/or simple template matching paradigms. This has led to considerable variability in SR studies from different laboratories, and species. In an effort to standardize SR assessment methods, we developed a machine learning algorithm and workflow to automatically classify SR waveforms in virtually any animal model including mice, rats, guinea pigs, and gerbils obtained with various paradigms and modalities from several laboratories. The universal features common to SR waveforms of various species and paradigms are examined and discussed in the context of each animal model. The procedure describes common results using the SR across species and how to fully implement the open-source R implementation. Since SR is widely used to investigate toxicological or pharmaceutical efficacy, a detailed and universal SR waveform classification protocol should be developed to aid in standardizing SR assessment procedures across different laboratories and species. This machine learning-based method will improve data reliability and translatability between labs that use the startle reflex paradigm. [Abstract copyright: Copyright © 2022. Published by Elsevier B.V.