PLoS medicine
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Randomized Controlled Trial Multicenter Study
Safety, tolerability, and pharmacokinetics of long-acting injectable cabotegravir in low-risk HIV-uninfected individuals: HPTN 077, a phase 2a randomized controlled trial.
Cabotegravir (CAB) is a novel strand-transfer integrase inhibitor being developed for HIV treatment and prevention. CAB is formulated both as an immediate-release oral tablet for daily administration and as a long-acting injectable suspension (long-acting CAB [CAB LA]) for intramuscular (IM) administration, which delivers prolonged plasma exposure to the drug after IM injection. HIV Prevention Trials Network study 077 (HPTN 077) evaluated the safety, tolerability, and pharmacokinetics of CAB LA in HIV-uninfected males and females at 8 sites in Brazil, Malawi, South Africa, and the United States. ⋯ In this study, CAB LA was well tolerated at the doses and dosing intervals used. ISRs were common, but infrequently led to product discontinuation. CAB LA 600 mg every 8 weeks met pharmacokinetic targets for both male and female study participants. The safety and pharmacokinetic results observed support the further development of CAB LA, and efficacy studies of CAB LA for HIV treatment and prevention are in progress.
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Cigarette smoking is associated with earlier menopause, but the impact of being a former smoker and any dose-response relationships on the degree of smoking and age at menopause have been less clear. If the toxic impact of cigarette smoking on ovarian function is irreversible, we hypothesized that even former smokers might experience earlier menopause, and variations in intensity, duration, cumulative dose, and age at start/quit of smoking might have varying impacts on the risk of experiencing earlier menopause. ⋯ The probability of earlier menopause is positively associated with intensity, duration, cumulative dose, and earlier initiation of smoking. Smoking duration is a much stronger predictor of premature and early menopause than others. Our findings highlight the clear benefits for women of early smoking cessation to lower their excess risk of earlier menopause.
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Multicenter Study
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.
Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. ⋯ Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.
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Multicenter Study
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.
Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. ⋯ ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.
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The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification. ⋯ These results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale.