Summary of the first Brazilian Symposium on Human Biometeorology.

This brief background highlights Brazil as a 'climate-health hotspot', i.e. a country where climate affects local populations negatively through multiple pathways (Di Napoli et al. BMC Public Health 22(1):1-8, 2022). Knowledge gaps still need to be filled concerning the various climaterelated dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers in the country (Krüger et al. Int J Biometeorol 66(7):1297-1315, 2022). Motivated by this, the first Brazilian Symposium on Human Biometeorology (Simpósio Brasileiro de Biometeorologia Humana 2022) was organized and held at the Federal University of Rio Grande do Norte (UFRN) in Natal, northeastern Brazil, between July 4 and 8, 2022. The symposium was organized as a hybrid event by a committee composed of researchers acting in different regions of the country, and who had an ongoing research collaboration on matters related to human biometeorology. The event was partly sponsored by the ISB and partly self-supported by the organizers and institutions involved. The symposium aimed to promote the development of the research area on human biometeorology in Brazil in facing challenges imposed by a globally and locally changing climate. To achieve this, the symposium focused on five main topics of discussion: a) climate-driven diseases; b) thermal comfort, urban and architectural biometeorology; c) atmospheric pollution and health; d) climate change; e) climate, health and climate change. This summary highlights the main findings, future research directions, and policy implications in each topic from the presentations and panel discussions.

A first approach to human biometeorology research in Brazil: a systematic review and meta-analysis.

This systematic review aims to give an overview of the diversity of research areas related to human biometeorology in Brazil. The main focus of this paper addresses research trends, represented by published papers with national and international authorship, main contributions and shortcomings, as well as challenges and prospects of research in this area of study. An extensive literature search was conducted in the Scopus, Web of Science, and Science Direct databases so as to identify relevant publication output up to July 2021 related to the research area. The screening resulted in 96 studies chosen for full-text reading. Overall, results indicated a reduced amount of articles on the subject matter published internationally, with noticeable gaps in research in some regions of the country, such as the Amazon region and in the Brazilian Midwest region. Research gaps in relevant areas have been identified with limited output in the climate dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers. Such gaps should further encourage researchers to engage in research focused on those areas.

Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients.

BACKGROUND: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. METHODS: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. RESULTS: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. CONCLUSIONS: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.

A preliminary study of the concentration of metallic elements in the blood of patients with multiple sclerosis as measured by ICP-MS.

It is estimated that multiple sclerosis (MS) affects 35,000 Brazilians and 2.5 million individuals worldwide. Many studies have suggested a possible role of metallic elements in the etiology of MS, but their concentration in the blood of MS patients is nonetheless little investigated in Brazil. In this work, these elements were studied through Inductively Coupled Plasma Mass Spectrometry (ICP-MS), whose analysis provides a tool to quantify the concentrations of metal elements in the blood samples of individuals with neurodegenerative disorders. This study aimed to compare the concentration of metallic elements in blood samples from patients with MS and healthy individuals. Blood was collected from 30 patients with multiple sclerosis and compared with the control group. Blood samples were digested in closed vessels using a microwave and ICP-MS was used to determine the concentrations of 12 metallic elements (Ba, Be, Ca, Co, Cr, Cu, Fe, Mg, Mo, Ni, Pb and Zn). In MS patients, we observed a reduction in the concentrations of beryllium, copper, chromium, cobalt, nickel, magnesium and iron. The mean concentration of lead in blood was significantly elevated in the MS group. However, no difference was observed in the concentrations of Mo, Ba, Ca and Zn in blood samples from MS patients and the control group. According to our data, there is a possible role for the concentrations of 8 of the 12 evaluated metallic elements in multiple sclerosis. Abnormalities in transition metals levels in biological matrices have been reported in several neurological diseases.

Valuation of the human thermal discomfort index for the five Brazilian regions in the period of El Niño-Southern Oscillation (ENSO).

Brazil is an extensive country with five administrative regions that have different climates, mainly due to their geographic locations. The El Niño-Southern Oscillation influences the regime of temperature and precipitation of the Brazilian regions, which can directly affect the thermal discomfort of the population. The objective of this study was to evaluate the human thermal discomfort index (HDI) in the five regions of Brazil for El Niño, La Niña, and neutral years from 1979 to 2017, as well as the influence of the degree of intensity of the Pacific Ocean anomaly in the thermal conditions of the Brazilian regions. Monthly data on air temperature and dewpoint temperature obtained from the ERA-Interim reanalysis were used. The HDI was calculated using specific equations. The results were analyzed by means of composition fields. From the results, it was possible to conclude that the El Niño and La Niña phenomena influence the HDI of the Brazilian regions. El Niño increases the discomfort due to the heat and the La Niña causes them to decrease. This study is important since these phenomena, by influencing thermal conditions, directly affect the well-being and health of the Brazilian population.

Evaluation of metal removal efficiency and its influence in the physicochemical parameters at two sewage treatment plants.

In sewage treatment plants, physicochemical parameters are highly controlled since treated sewage can be returned to water bodies or reused. In addition, pollutants such as heavy metals also deserve attention due to their potential toxicity. In general, these characteristics of sewage and treated water are evaluated independently, with the support of Brazilian legislation that does not require a routine for the analysis of metals as frequent as for the physicochemical parameters. In this work, 66 samples of raw sewage, treated sewage, and effluents from two treatment plants in the city of Bauru, São Paulo, Brazil, were evaluated to assess the efficiency of the treatment plants in the removal of metals. In addition, the influence of these pollutants on the quantification of physicochemical parameters was evaluated. The quantification of metals was performed using inductively coupled plasma optical spectroscopy (ICP-OES), and Spearman's test was applied to evaluate correlation between physicochemical parameters and metal content. The main metals found in the samples were Ba, Mn, Zn, Cu, Se, Fe, and Al. The results indicate that concentrations of metals in the aquatic environment can significantly affect the physicochemical parameters, since high concentrations of metals can interfere mainly in the pH, chemical oxygen demand, and dissolved oxygen.