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To evaluate the use of biofeedback intervention in the levels of depression. The main hypothesis tested if the use of biofeedback improves depression levels compared to the control group. A randomised clinical trial. The final sample was composed of 36 participants (18 in the experimental group, receiving 6 training, once a week, with biofeedback; and 18 in the control group, who received conventional treatment in the service).Outcome measures were assessed in two stages: pre-test and post-test. The research used the following instruments: demographic survey data, Mini International Neuropsychiatric Interview 5.0.0 and Beck Depression Inventory (BDI). The factors and variables were presented in terms of descriptive and inferential statistics. Fisher's exact test (p < 0.05) was used to verify the existence of an association between the counting variables. The multinomial logistic regression model was adopted, and the Logit link function was used, as the software RStudio version 3.6.2. The factors that remained in the final model were group, sex, partner, atypical antidepressant, benzodiazepines, mood stabiliser, antiepileptic and antihistamine, according to the levels of depression based on the BDI. The group that did not receive biofeedback intervention had 16 times more chances of increasing the depression levels compared to participants in the experimental group. The use of biofeedback reduces depression, thus, representing a complementary alternative for the treatment of moderate and severe depression, and dysthymia.

The expression of immune response appears to be associated with morbidity in Chagas disease. However, the studies in this field have usually employed small samples of patients and statistical analyses that do not consider the wide dispersion of cytokine production observed in these patients. The aim of this study was to evaluate the plasma cytokine levels in well-defined clinical polar groups of chagasic patients divided into categories that better reflect the wide cytokine profile and its relationship with morbidity. Patients infected with Trypanosoma cruzi (T. cruzi) were grouped as indeterminate (IND) and cardiac (CARD) forms ranging from 23 to 69 years of age (mean of 45.6±11.25). The IND group included 82 individuals, ranging from 24 to 66 years of age (mean of 39.6±10.3). The CARD group included 94 patients ranging from 23 to 69 years of age (mean of 48±12.52) presenting dilated cardiomyopathy. None of the patients have undergone chemotherapeutic treatment, nor had been previously treated for T. cruzi infection. Healthy non-chagasic individuals, ranging from 29 to 55 years of age (mean of 42.6±8.8) were included as a control group (NI). IND patients have a higher intensity of interleukin 10 (IL-10) expression when compared with individuals in the other groups. By contrast, inflammatory cytokine expression, such as interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interleukin 1 beta (IL-1β), proved to be the highest in the CARD group. Correlation analysis showed that higher IL-10 expression was associated with better cardiac function, as determined by left ventricular ejection fraction and left ventricular diastolic diameter values. Altogether, these findings reinforce the concept that a fine balance between regulatory and inflammatory cytokines represents a key element in the establishment of distinct forms of chronic Chagas disease.

Candida species are frequently implicated in the development of both superficial and invasive fungal infections, which can impact vital organs. In the quest for novel strategies to combat fungal infections, there has been growing interest in exploring synthetic and semi-synthetic products, particularly chromone derivatives, renowned for their antimicrobial properties. In the analysis of the antifungal activity of the compound (E)-benzylidene-chroman-4-one against Candida, in silico and laboratory tests were performed to predict possible mechanisms of action pathways, and in vitro tests were performed to determine antifungal activity (MIC and MFC), to verify potential modes of action on the fungal cell membrane and wall, and to assess cytotoxicity in human keratinocytes. The tested compound exhibited predicted affinity for all fungal targets, with the highest predicted affinity observed for thymidylate synthase (−102.589 kJ/mol). MIC and CFM values ranged from 264.52 μM (62.5 μg/mL) to 4232.44 μM (1000 μg/mL). The antifungal effect likely occurs due to the action of the compound on the plasma membrane. Therefore, (E)-benzylidene-chroman-4-one showed fungicidal-like activity against Candida spp., possibly targeting the plasma membrane.

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations 1–6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories 7 , we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.

This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series.