Biological activities from extracts of endophytic fungi isolated from Viguiera arenaria and Tithonia diversifolia

A total of 39 endophytic fungi have been isolated from Viguiera arenaria and Tithonia diversifolia, both collected in São Paulo State, Brazil. The isolates were identified based on their ribosomal DNA sequences. The ethyl acetate (EtOAc) extracts of all endophytic fungi were evaluated for their antimicrobial, antiparasitic and antitumoral activity. Antimicrobial screening was conducted using an agar diffusion assay against three pathogenic microorganisms: Staphylococcus aureus, Escherichia coli and Candida albicans. Antiparasitic activity was determined by enzymatic inhibition of gGAPDH of Trypanosoma cruzi and adenine phosphorybosiltransferase (APRT) of Leishmania tarentolae. Antitumoral activity was tested against human T leukemia cells by the Mosmann colorimetric method. All extracts showed activity in at least one assay: 79.5% of the extracts were cytotoxic against leukemia cells, 5.1% of the extracts were active against S. aureus, 25.6% against E. coli and 64.1% against Candida albicans. Only one extract showed promising results in the inhibition of parasitic enzymes gGAPDH (95.0%) and three were found to inhibit APRT activity. The cytotoxic extract produced by the strain VA1 (Glomerella cingulata) was fractionated and yielded nectriapyrone and tyrosol. Nectriapyrone showed relevant cytotoxic activity against both human T leukemia and melanoma tumor cell lines.FAPESP 03/07535-5FAPESP 04/07935-6CAPE

A prediction rule to stratify mortality risk of patients with pulmonary tuberculosis

Tuberculosis imposes high human and economic tolls, including in Europe. This study was conducted to develop a severity assessment tool for stratifying mortality risk in pulmonary tuberculosis (PTB) patients. A derivation cohort of 681 PTB cases was retrospectively reviewed to generate a model based on multiple logistic regression analysis of prognostic variables with 6-month mortality as the outcome measure. A clinical scoring system was developed and tested against a validation cohort of 103 patients. Five risk features were selected for the prediction model: hypoxemic respiratory failure (OR 4.7, 95% CI 2.8-7.9), age >= 50 years (OR 2.9, 95% CI 1.7-4.8), bilateral lung involvement (OR 2.5, 95% CI 1.44.4), >= 1 significant comorbidity-HIV infection, diabetes mellitus, liver failure or cirrhosis, congestive heart failure and chronic respiratory disease-(OR 2.3, 95% CI 1.3-3.8), and hemoglobin = 6) mortality risk. The mortality associated with each group was 2.9%, 22.9% and 53.9%, respectively. The model performed equally well in the validation cohort. We provide a new, easy-to-use clinical scoring system to identify PTB patients with high-mortality risk in settings with good healthcare access, helping clinicians to decide which patients are in need of closer medical care during treatment.This work was supported by Fundacao Amelia de Mello/Jose de Mello Saude and Sociedade Portuguesa de Pneumologia (SPP). This work was developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). NSO is a FCT (Fundacao para a Ciencia e Tecnologia) investigator. MS is an Associate FCT Investigator. The fundershad no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The association of myocardial strain with cardiac magnetic resonance and clinical outcomes in patients with acute myocarditis

IntroductionThe role of myocardial strain in risk prediction for acute myocarditis (AMC) patients, measured by cardiac magnetic resonance (CMR), deserves further investigation. Our objective was to evaluate the association between myocardial strain measured by CMR and clinical events in AMC patients.Material and methodsThis was a prospective single-center study of patients with AMC. We included 100 patients with AMC with CMR confirmation. The primary outcome was the composite of all-cause mortality, heart failure and AMC recurrence in 24 months. A subgroup analysis was performed on a sample of 36 patients who underwent a second CMR between 6 and 18 months. The association between strain measures and clinical events or an increase in left ventricular ejection fraction (LVEF) was explored using Cox regression analysis. Global peak radial, circumferential and longitudinal strain in the left and right ventricles was assessed. ROC curve analysis was performed to identify cutoff points for clinical event prediction.ResultsThe mean follow-up was 18.7 ± 2.3 months, and the composite primary outcome occurred in 26 patients. The median LVEF at CMR at baseline was 57.5% (14.6%). LV radial strain (HR = 0.918, 95% CI: 0.858–0.982, p = 0.012), LV circumferential strain (HR = 1.177, 95% CI: 1.046–1.325, p = 0.007) and LV longitudinal strain (HR = 1.173, 95% CI: 1.031–1.334, p = 0.015) were independently associated with clinical event occurrence. The areas under the ROC curve for clinical event prediction were 0.80, 0.79 and 0.80 for LV radial, circumferential, and longitudinal strain, respectively. LV longitudinal strain was independently correlated with prognosis (HR = 1.282, CI 95%: 1.022–1.524, p = 0.007), even when analyzed together with ejection fraction and delayed enhancement. LV and right ventricle (RV) strain were not associated with an increase in LVEF. Finally, when the initial CMR findings were compared with the follow-up CMR findings, improvements in the measures of LV and RV myocardial strain were observed.ConclusionMeasurement of myocardial strain by CMR can provide prognostic information on AMC patients. LV radial, circumferential and longitudinal strain were associated with long-term clinical events in these patients

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio