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Summary: Background: Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level. Methods: Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001–2010 and 2011–2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain). Findings: An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC = 0.970 [0.947–0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania. Interpretation: This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots. Funding: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.

Abstract Healthcare workers present an increased risk of contagion for the SARS-CoV-2 virus due to their labor exposure. Here, we describe the clinical, laboratory, and immunological characteristics of healthcare workers, before vaccine application, exposed to SARS-CoV-2-infected patients. We collected sociodemographic, clinical, and laboratory information from 50 professionals who worked during the COVID-19 pandemic at the Clinical Hospital of the Northwest in Brazil. The results showed that most workers are women, over 50 years old, and worked as nursing technicians. Approximately 56% of workers were positive for a previous infection by RT-PCR and/or anti-SARS-CoV-2-immunoglobulin tests. Increased levels of hematocrit, neutrophils, NK lymphocytes, and fibrinogen, were found in positive healthcare workers, suggesting a light inflammatory status. The immunological findings showed an increase in IL-17 production and a Th2/Th17/Th22 profile followed by high serology for anti-SARS-CoV-2 IgM and IgG. Those data reveal the importance of studies with healthcare workers to investigate if the continuous exposition to the virus may result in chronic activation of the immune system and/or pulmonary inflammation in this target group.

Although a few studies have addressed the combined effects of climate change and habitat loss on biodiversity at a regional scale, the extent to which climate change will impact species occurrence at the landscape scale remains unclear. We evaluated the potential effects of climate change at the landscape scale for 11 bird species endemic to the Atlantic Forest biodiversity hotspot. We quantified current and future climatic suitability for each species using ecological niche models, and quantified habitat amount and structure using connectivity analyses. Climate and landscape suitability were then combined using two approaches, which differed in the spatial scale at which climate was quantified. First, we incorporated climate at patch-level into the probability of connectivity (PC) index, obtaining a Combined Landscape Suitability (CLS) index. Second, we multiplied climate at landscape-level with PC. Climatic suitability at the landscape scale was predicted to vary little (

ABSTRACT Objectives To assess the evolution to permanent or transient conditions in children with positive neonatal TSH tests in Sergipe, Brazil, from 2004 to 2010. Subjects and methods Out of 193,794 screened newborns, 713 presented a neonatal TSH level higher than the local cutoff (5.2 µU/mL). From the confirmatory serum TSH values, the children were diagnosed with initial congenital hypothyroidism (CH) or suspect CH. From the evolution, they were classified as permanent CH, hyperthyrotropinemia, or transient TSH elevation. The mean incidence of each final condition was calculated for the total period of time. Results The initial diagnosis included 37 CH (18.1%) and 167 suspect CH (81.9%) cases. The final diagnosis included 46 cases of permanent CH (22.5%), 56 of hyperthyrotropinemia (27.5%), and 102 of transient TSH elevation (50.0%). Out of the 37 cases of initial CH, 23 (62.2%) had permanent CH, nine (24.3%) had hyperthyrotropinemia, and five (13.5%) had transient TSH elevation. Out of the 167 suspect CH cases, 23 (13.8%) had permanent CH, 47 (28.1%) had hyperthyrotropinemia and 97 (58.1%) had transient TSH elevation. The mean incidence after the follow up was 1:4,166 for permanent CH, 1:3,448 for hyperthyrotropinemia, and 1:1,887 for transient TSH elevation. Eighty-six percent of the children with an initial diagnosis of CH and 41.9% with suspect CH had a permanent condition (CH or hyperthyrotropinemia). Conclusions The follow-up of children with an initial diagnosis of CH or suspect CH is necessary to determine whether the disorder is permanent because predicting the evolution of the condition is difficult.

Obesity is currently a pandemic of worldwide proportions affecting millions of people. Recent studies have proposed the hypothesis that mechanisms not directly related to the human genome could be involved in the genesis of obesity, due to the fact that, when a population undergoes the same nutritional stress, not all individuals present weight gain related to the diet or become hyperglycemic. The human intestine is colonized by millions of bacteria which form the intestinal flora, known as gut flora. Studies show that lean and overweight human may present a difference in the composition of their intestinal flora; these studies suggest that the intestinal flora could be involved in the development of obesity. Several mechanisms explain the correlation between intestinal flora and obesity. The intestinal flora would increase the energetic extraction of non-digestible polysaccharides. In addition, the lipopolysaccharide from intestinal flora bacteria could trigger a chronic sub-clinical inflammatory process, leading to obesity and diabetes. Another mechanism through which the intestinal flora could lead to obesity would be through the regulation of genes of the host involved in energy storage and expenditure. In the past five years data coming from different sources established causal effects between intestinal microbiota and obesity/insulin resistance, and it is clear that this area will open new avenues of therapeutic to obesity, insulin resistance and DM2.