To describe the clinical characteristics, laboratory results, imaging findings, and in-hospital outcomes of COVID-19 patients admitted to Brazilian hospitals. Methods: A cohort study of laboratory-confirmed COVID-19 patients who were hospitalized from March 2020 to September 2020 in 25 hospitals. Data were collected from medical records using Research Electronic Data Capture (REDCap) tools. A multivariate Poisson regression model was used to assess the risk factors for in-hospital mortality. Results: For a total of 2,054 patients (52.6% male; median age of 58 years), the in-hospital mortality was 22.0%; this rose to 47.6% for those treated in the intensive care unit (ICU). Hypertension (52.9%), diabetes (29.2%), and obesity (17.2%) were the most prevalent comorbidities. Overall, 32.5% required invasive mechanical ventilation, and 12.1% required kidney replacement therapy. Septic shock was observed in 15.0%, nosocomial infection in 13.1%, thromboembolism in 4.1%, and acute heart failure in 3.6%. Age >= 65 years, chronic kidney disease, hypertension, C-reactive protein ! 100 mg/dL, platelet count < 100 Â 10 9 /L, oxygen saturation < 90%, the need for supplemental oxygen, and invasive mechanical ventilation at admission were independently associated with a higher risk of in-hospital mortality. The overall use of antimicrobials was 87.9%. Conclusions: This study reveals the characteristics and in-hospital outcomes of hospitalized patients with confirmed COVID-19 in Brazil. Certain easily assessed parameters at hospital admission were independently associated with a higher risk of death. The high frequency of antibiotic use points to an over-use of antimicrobials in COVID-19 patients.

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An emerging body of evidence has implicated plasminogen activator inhibitor-1 (PAI-1) in the development of type 2 diabetes (T2D), though findings have not always been consistent. We systematically reviewed epidemiological studies examining the association of PAI-1 with T2D. EMBASE, PubMed, Web of Science, and the Cochrane Library were searched to identify studies for inclusion. Fifty-two studies (44 cross-sectional with 47 unique analytical comparisons and 8 prospective) were included. In pooled random-effects analyses of prospective studies, a comparison of the top third vs. bottom third of baseline PAI-1 values generated a RR of T2D of 1.67 (95% CI 1.28–2.18) with moderate heterogeneity (I2 = 38%). Additionally, of 47 cross-sectional comparisons, 34(72%) reported significantly elevated PAI-1 among diabetes cases versus controls, 2(4%) reported significantly elevated PAI-1 among controls, and 11(24%) reported null effects. Results from pooled analyses of prospective studies did not differ substantially by study design, length of follow-up, adjustment for various putative confounding factors, or study quality, and were robust to sensitivity analyses. Findings from this systematic review of the available epidemiological literature support a link between PAI-1 and T2D, independent of established diabetes risk factors. Given the moderate size of the association and heterogeneity across studies, future prospective studies are warranted.

Aims: The aim of this study was to assess the effects of orlistat on weight lossrelated clinical variables in overweight/obese women with polycystic ovary syndrome (PCOS) and to compare treatment with orlistat vs. metformin in this group. Methods: We conducted a systematic review and meta-analysis of the evidence about the use of orlistat in women with PCOS. We searched the literature published until May 2015 in MEDLINE, Cochrane Central Register of Controlled Trials and LILACS. Results: Of 3951 studies identified, nine were included in the systematic review (three prospective, non-randomised studies and six randomised control trials). Eight studies used the Rotterdam criteria and 1 used NIH criteria to diagnose PCOS. Data suggest that orlistat promotes a significant reduction in BMI/ weight in overweight/obese PCOS women. Eight studies evaluated orlistat impact on testosterone. Seven reported an improvement in testosterone levels. Eight studies evaluated impact on insulin resistance, and five reported improvement. Finally, five studies evaluated impact on lipid profile, and four reported improvement. Three randomised control trials were included in the fixed effects model meta-analysis for a total of 121 women with PCOS. Orlistat and metformin had similar positive effects on BMI (À0.65%, 95% CI: À2.03 to 0.73), HOMA (À3.60%, 95% CI: À16.99 to 9.78), testosterone (À2.08%, 95% CI: À13.08 to 8.93) and insulin (À5.51%, 95% CI: À22.27 to 11.26). Conclusion(s): The present results suggest that orlistat leads to significant reduction in BMI/body weight in PCOS. In addition, the available evidence indicates that orlistat and metformin have similar effects in reducing BMI, HOMA, testosterone and insulin in overweight/obese PCOS women.This study was registered in PROSPERO under number CRD42014012877. Review criteria• We conducted a systematic review and metaanalysis of the evidence about the effect of orlistat on weight, BMI, androgens and insulin resistance in women with polycystic ovary syndrome.• We systematically searched literature published until May 2015 in electronic databases MEDLINE, Cochrane Central Register of Controlled Trials and LILACS.• We conducted a descriptive systematic review and a fixed effects model meta-analysis and evaluated heterogeneity using the I 2 statistics and Cochran's Q test. Message for the clinic• Orlistat leads to significant reduction in BMI/body weight in overweight/obese PCOS.• Orlistat and metformin have similar effects in reducing BMI, testosterone and insulin/HOMA in overweight/obese PCOS women.

The results provide quantitative evidence for an association between HPV infection and colorectal cancer risk.

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine disorder affecting women of reproductive age. PCOS has been associated with distinct metabolic and cardiovascular diseases and with autoimmune conditions, predominantly autoimmune thyroid disease (AITD). AITD has been reported in 18–40% of PCOS women, depending on PCOS diagnostic criteria and ethnicity. The aim of this systematic review and meta-analysis was to summarize the available evidence regarding the likelihood of women with PCOS also having AITD in comparison to a reference group of non-PCOS women. We systematically searched EMBASE and MEDLINE for non-interventional case control, cross-sectional or cohort studies published until August 2017. The Ottawa–Newcastle Scale was used to assess the methodological quality of studies. Statistical meta-analysis was performed with R. Thirteen studies were selected for the present analysis, including 1210 women diagnosed with PCOS and 987 healthy controls. AITD was observed in 26.03 and 9.72% of PCOS and control groups respectively. A significant association was detected between PCOS and chance of AITD (OR = 3.27, 95% CI 2.32–4.63). Notably, after geographical stratification, the higher risk of AITD in PCOS women persisted for Asians (OR = 4.56, 95% CI 2.47–8.43), Europeans (OR = 3.27, 95% CI 2.07–5.15) and South Americans (OR = 1.86, 95% CI 1.05–3.29). AIDT is a frequent condition in PCOS patients and might affect thyroid function. Thus, screening for thyroid function and thyroid-specific autoantibodies should be considered in patients with PCOS even in the absence of overt symptoms. This systematic review and meta-analysis is registered in PROSPERO under number CRD42017079676.

Testosterone is the main hormonal agent used for cross-sex hormone therapy in female-to-male transgender persons. Our aim was to systematically review the literature concerning the effects of testosterone on body mass index (BMI), blood pressure, hematocrit, hemoglobin, lipid profile, and liver enzymes in transgender men. PUBMED and EMBASE were searched for studies published until March 2017. Studies were included if they reported interventions with any dose of testosterone and comparison of variables before and during treatment. Of 455 potentially eligible articles, 13 were reviewed. Study duration ranged from 6 to 60 months, sample size ranged from 12 to 97 patients, and the most common treatment was parenteral testosterone undecanoate 1000 mg/12 weeks. Slight but significant increases in BMI were reported (from 1.3 to 11.4%). Three out of seven studies assessing the impact of different testosterone formulations on blood pressure detected modest increases or clinically irrelevant changes in this variable. In another study, however, two patients developed hypertension, which was resolved after cessation of testosterone therapy. Decreases in HDL-cholesterol and increases in LDL-cholesterol were consistently observed. Eight studies observed a relationship between testosterone and increased hemoglobin (range: 4.9-12.5%) and hematocrit (range: 4.4-17.6%), but discontinuation of androgen therapy was not necessary. In one study, two patients developed erythrocytosis (hematocrit >52%) after 9 and 12 months of treatment. One study analyzing testosterone formulations observed smaller increases in hemoglobin and hematocrit with testosterone gel. Six studies assessing liver function showed slight or no changes. Overall, the quality of evidence was low, given the lack of randomized clinical/controlled trials and the small sample sizes. In conclusion, exogenous testosterone administration to transgender men was associated with modest increases in BMI, hemoglobin/hematocrit, and LDL-cholesterol, and with decreases in HDL-cholesterol. Long-term studies are needed to assess the long-term risks of testosterone therapy, particularly as they relate to cardiometabolic risks such as diabetes, dyslipidemia and the metabolic syndrome.

Meta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k=231) and regression (k=210) analysis for the Y-S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher's transformation of the Pearson's correlation coefficient (Zr) and the intercept (β0) and slope (β1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (70%, moderate=>40 and ≤70%, and low=≤40% S the check treatment), and growing season. The overall mean for r- (back-transformed Z-r) was -0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in Z-r. Stronger associations (r-=-0.87 and -0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP>70%) and earliest rust onset (DOT0.73 pp/%(-1)) were estimated for studies with DOT70%; the latter possibly due to high fungicide efficacy when DP is low, thus leading to higher yield differences between fungicide-protected and nontreated plots. The critical-point meta-analytic models can provide general estimates of yield loss based on a composite measure of disease severity. They can also be useful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development.