Notes

Predictors involving practical mitral vomiting repeat after percutaneous mitral device restoration.
26, 95% CI 0.83-1.91) or R-/D- (HR 0.98, 95% CI 0.64-1.5).

CMV non-controllers had worse OS at one-year post-HCT. CMV controllers had similar OS as elite-controllers or R-/D-. Future studies are needed to validate our AAUC cutoff across different cohorts and CMV management strategies.
CMV non-controllers had worse OS at one-year post-HCT. CMV controllers had similar OS as elite-controllers or R-/D-. Future studies are needed to validate our AAUC cutoff across different cohorts and CMV management strategies.
Daily oral tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) is effective for reducing HIV acquisition among cisgender women. We report results from the first United States observational open-label demonstration project of PrEP among at-risk cisgender women.

Adherence Enhancement Guided by Individualized Texting and Drug Levels (AEGiS) was a 48-week single-arm open-label demonstration study of daily oral TDF/FTC in cisgender women ≥18 years old at-risk for HIV. Adherence was supported using two-way text messaging and titrated adherence counseling based on rapid-turnaround tenofovir diphosphate concentrations from dried blood spots. Study visits occurred at baseline, and at weeks 4, 12, and quarterly through week 48. Outcomes included TDF/FTC adherence, retention and persistence.

From June 2016 to October 2018, 136 cisgender women enrolled [mean age 40 (SD 11); 38% non-Hispanic (NH) Black and 19% Latina]. At 48 weeks, 84 (62%) participants were retained and 62 (46%) remained on PrEP. Over one-third (12/31) of those on study but off PrEP throughout study discontinued TDF/FTC due to side effects, and one adverse event led to study discontinuation. Of 120 participants with drug concentrations measured, 67 (56%) had at least one concentration consistent with ≥6 doses/week (d/w); 22 (18%) had consistent ≥6 d/w across all study visits attended. There were no incident HIV infections and 4 incident bacterial STIs.

Adequate PrEP adherence for protective drug concentrations was not achieved for most study participants. More work needs to be done to fully explicate the reasons for non-adherence and low retention in cisgender women.
Adequate PrEP adherence for protective drug concentrations was not achieved for most study participants. More work needs to be done to fully explicate the reasons for non-adherence and low retention in cisgender women.
Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual's average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics.

Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity 'scrambling' the order of days; daily vs. Zunsemetinib chemical structure weekly variation; naps; awakenings; 'all-nighters'; and length of study.

SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences.

Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.
Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~ 340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (p  less then  5 × 10-8) two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534, and milk type [dairy vs. other] with 4131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.
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