Purpose The purpose of this study was to determine the prevalence and correlates of nonprescription hormone use among Brazilian trans women. Methods This study is a cross-sectional survey of trans women in São Paulo, Brazil, recruited by respondent-driven sampling in 2017-2019. Results Of 790 trans women, 36.8% were taking nonprescribed hormones. Nonprescribed hormone use was higher with younger age, lower education, homelessness, and using estrogen plus progesterone. Lower use was associated with accessing health care and having trans-specific health needs met. Conclusion Marginalized Brazilian trans women exhibit high use of nonprescription hormones, which may have health consequences and requires further examination and research.Introduction With the unprecedented expansion of women's roles in the U.S. military during recent (post-9/11) conflicts in Iraq and Afghanistan, the number of women seeking healthcare through the Veterans Health Administration (VHA) has increased substantially. Women Veterans often present as medically complex due to multiple medical, mental health, and psychosocial comorbidities, and consequently may be underserved. Thus, we conducted the nationwide Women Veterans Cohort Study (WVCS) to examine post-9/11 Veterans' unique healthcare needs and to identify potential disparities in health outcomes and care. Methods We present baseline data from a comprehensive questionnaire battery that was administered from 2016 to 2019 to a national sample of post-9/11 men and women Veterans who enrolled in Veterans Affairs care (WVCS2). Data were analyzed for descriptives and to compare characteristics by gender, including demographics; health risk factors and symptoms of cardiovascular disease, chronic pain, and mental healtre system.Purpose We sought to evaluate the effect of antiglaucoma ophthalmic solutions on the cornea with a corneal resistance device (CRD), and to compare the results with those by fluorescein staining. Methods In 6 rabbit groups (n = 7 each), right eyes were administered latanoprost ophthalmic solution containing 0.02% benzalkonium chloride (BAK); dorzolamide/timolol (1%/0.5%) containing 0.005% BAK; dorzolamide/timolol without BAK; dorzolamide/timolol+latanoprost with 0.02% BAK; 0.005% BAK; or 0.02% BAK to the conjunctival sac 3 × at 15-min intervals. Left (control) eyes were administered saline. Baseline and post-treatment corneal resistance (CR) were measured. The CR ratio = CR before versus after treatment.  https://www.selleckchem.com/JAK.html  We evaluated superficial punctate keratitis by fluorescein staining using area and density (AD) grades. Results In the dorzolamide/timolol-without BAK group, there were no significant difference in the CR ratio between the control and treatment eyes at any time point. In the 0.005%-BAK group at 30 min and the other 4 groups at all time points, the CR ratio differed significantly between the control and treatment eyes (P  less then  0.05). AD grades were 0 in all control eyes and the dorzolamide/timolol-without BAK and 0.005% BAK treatment eyes. Conclusions Nonpreservative ophthalmic solutions (and those with low BAK concentrations) do not significantly affect corneal electrical resistance. Eye drop ingredients other than BAK may be involved in altering corneal electrical resistance. CRDs may detect corneal epithelium changes not revealed by fluorescein staining.Insulin pump training has traditionally been performed in-person. The coronavirus disease 2019 (COVID-19) pandemic necessitated vast increases in the number of virtual pump trainings for Tandem tslim X2 insulin pump starts. A customized structured pump training curriculum specifically tailored to virtual learning was deployed in early 2020, and included (1) preparation for training with use of the tsimulator app, (2) use of the teach-back method during video training, and (3) automating data uploads for follow-up. Retrospective analysis from >23,000 pump training sessions performed from January 1, 2020 to July 28, 2020 showed sensor time-in-range for up to 6 months after training was 72% (60%-81%) for virtual training versus 67% (54%-78%) for in-person training. Higher user satisfaction (4.78 ± 0.52 vs. 4.64 ± 0.68; P  less then  0.01) and higher user confidence (4.61 ± 0.75 vs. 4.47 ± 0.0.85; P  less then  0.01) were reported after the virtual sessions. Virtual pump training was well received and proved safe and effective with the new virtual training curriculum.Essential proteins possess critical functions for cell survival. Identifying essential proteins improves our understanding of how a cell works and also plays a vital role in the research fields of disease treatment and drug development. Recently, some machine-learning methods and ensemble learning methods have been proposed to identify essential proteins by introducing effective protein features. However, the ensemble learning method only used to focus on the choice of base classifiers. In this article, we propose a novel ensemble learning framework called multi-ensemble to integrate different base classifiers. The multi-ensemble method adopts the idea of multi-view learning and selects multiple base classifiers and trains those classifiers by continually adding the samples that are predicted correctly by the other base classifiers. We applied multi-ensemble to Yeast data and Escherichia coli data. The results show that our approach achieved better performance than both individual classifiers and the other ensemble learning methods.Background Excess gestational weight gain (GWG) is common and adversely affects both mothers and offspring, including increasing the risk of maternal and childhood obesity. GWG is typically examined categorically, with women grouped into categories of those who gain above, within, and below guideline recommendations. Examining GWG as a continuous variable, rather than categorically, allows for a consideration of GWG at a finer level of detail, increasing precision. Methods We collected exposure data among 970 pregnant women in early gestation using a standardized questionnaire in Ontario, Canada, from 2015 to 2017. Maternal weight and height were extracted from antenatal records. Continuous GWG was calculated using four methods percentage of ideal weight gain, excess GWG, GWG adequacy ratio, and GWG z-score. We used the stepwise linear regression analyses to select variables associated with GWG. Results We found that a common set of variables (parity, prepregnancy body mass index, planned pregnancy weight gain, smoking, pregnancy-related food cravings, and fast food intake) significantly predicted GWG in a manner consistent across the four GWG outcomes.