Vitamin D3 as adjuvant in the treatment of type 2 diabetes mellitus: modulation of genomic and biochemical instability

Erratum in - Corrigendum: Vitamin D3 as adjuvant in the treatment of type 2 diabetes mellitus: modulation of genomic and biochemical instability. Fagundes GE, Macan TP, Rohr P, Damiani AP, Da Rocha FR, Pereira M, Longaretti LM, Vilela TC, Ceretta LB, Mendes C, Silveira PCL, Teixeira JPF, de Andrade VM. Mutagenesis. 2019 May 29;34(2):215. doi: 10.1093/mutage/gez006.Type 2 diabetes mellitus has undergone a worldwide growth in incidence in the world and has now acquired epidemic status. There is a strong link between type 2 diabetes and vitamin D deficiency. Because vitamin D has beneficial effects on glucose homeostasis, the aim of this study was to evaluate the influence of vitamin D3 supplementation on the modulation of glycaemic control and other metabolic effects, as well as modulation of genomic instability in patients with type 2 diabetes. We evaluated 75 patients with type 2 diabetes, registered in the Integrated Clinics of the University of Southern Santa Catarina. Participants received 4000 IU of vitamin D3 (25(OH)D) supplementation daily for 8 weeks. Blood samples were collected at the beginning and at the end of the supplementation, and 4 weeks after the end of supplementation. The glycidic and lipid profiles [total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein and triglycerides], oxidative stress, DNA damage and 25(OH)D levels were evaluated. Vitamin D3 supplementation for 8 weeks showed enough to significantly increase blood levels of 25(OH)D. A significant difference in lipid profile was observed only in non-HDL cholesterol. Significant changes were observed in glucose homeostasis (fasting glucose and serum insulin) and, in addition, a reduction in the parameters of oxidative stress and DNA damage. There was a significant reduction in the values of 25(OH)D 4 weeks after the end of the supplementation, but levels still remained above baseline. Use of vitamin D supplementation can be an ally in the health modulation of patients with type 2 diabetes mellitusThis work was supported by grants from Conselho Nacional de Pesquisa e Desenvolvimento (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) and Programa de Pós-Graduação em Ciências da Saúde/ Universidade do Extremo Sul Catarinense.info:eu-repo/semantics/publishedVersio

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Staphylococci: colonies morphological caracteristics, coagulase and EEA production colected from cooled raw milk samples

Os relatos de intoxicação estafilocócica, principalmente por S. aureus, são frequentemente associados à ingestão de 100 ng de enterotoxina/g ou mL de alimento. A produção de enterotoxina pelos estafilococos está associada fortemente à produção de coagulase. Neste trabalho foram avaliadas amostras de leite de 51 propriedades leiteiras em Londrina (RS) e 50 em Pelotas. Todas as 101 amostras apresentaram contagens de estafilococos coagulase positivos (ECP). Das 552 colônias avaliadas, 256 foram coagulase positivas e destas cerca de 60% eram típicas no àgar Baird Parker e, em média, 60% do total de 296 estafilococos coagulase negativos (ECN) eram colônias atípicas. Quanto à capacidade enterotoxigênica, testou-se 109 colônias ECP, sendo 5,5% (6) positivas e 18 ECN, sendo 1 (5,5%) positiva. Assim, a freqüência de ECN produtores de EEA (5,5%) foi semelhante a dos ECP. Conclui-se que há extensa contaminação do leite cru por ECP, sendo grande parte (40%) das colônias atípicas produtoras de coagulase.The stories of food poisoning from staphylococci, mainly by S. aureus, are frequently associates to the ingestion of food with 100 ng of enterotoxin/g or mL. The production of enterotoxin using staphylococci is strongly associated with the production of coagulase. In this paper, milk samples of 51 milk properties in Londrina (RS) and 50 in Pelotas had been evaluated. All the 101 samples had presented counts of coagulase positive staphylococci (CPS). From 552 colonies studied, 256 were CPS where 60% of the colonies were typical in Baird Parker agar and, from 296 coagulase negative staphylococcus (CNS), in mean, 60% were atypical colonies. Due to the enterotoxigenic capacity, were tested 109 CPS, being 5,5% (6) positive and 18 CNS where 1 (5,5%) were positive. The frequency of CPS EEA (5,5%) producer was similar of the CNS. There is a wide milk contamination with CPS, where, a big part (40%) of the atypical colonies is coagulase producers

Transient Hearing Loss in Adults Associated with Zika Virus Infection

This work was supported by the National Institutes of Health (grant numbers 1 R01 AI121207 and R24 AI120942). R. K. and L. A. S. were supported by “Programa Ciências sem fronteiras,” Conselho Nacional de Desenvolvimento Cientifico e Tecnológico of Brazil.Hospital Santa Izabel. Salvador, BA, Brazil.Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil.Hospital Santa Izabel. Salvador, BA, Brazil.Hospital Santa Izabel. Salvador, BA, Brazil.Hospital Santa Izabel. Salvador, BA, Brazil.Hospital Santa Izabel. Salvador, BA, Brazil.Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. World Health Organization Collaborating Center for Reference and Research on Arbovirus. Ananindeua, PA, Brasil.University of Texas Medical Branch. Center for Biodefense and Emerging Infectious Diseases. Department of Pathology. Galveston, TX, USA.University of Texas Medical Branch. Center for Biodefense and Emerging Infectious Diseases. Department of Pathology. Galveston, TX, USA.Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil / Yale School of Public Health. Department of Epidemiology of Microbial Diseases. New Haven, CT, USA.Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil / José Silveira Foundation. Research Initiative. Multinational Organization Network Sponsoring Translational and Epidemiological. Salvador, BA, BrazilMinistério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil.Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil / KU Leuven. Rega Institute for Medical Research. Department of Microbiology and Immunology. Laboratory of Clinical and Epidemiological Virology. Leuven, Belgium.Hospital Santa Izabel. Salvador, BA, Brazil / Ministério da Saúde. Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brazil / Universidade Federal da Bahia. Faculdade de Medicina da Bahia. Salvador, BA, Brazil.In 2015, during the outbreak of Zika virus (ZIKV) in Brazil, we identified 3 cases of acute hearing loss after exanthematous illness. Serology yielded finding compatible with ZIKV as the cause of a confirmed (n = 1) and a probable (n = 2) flavivirus infection, indicating an association between ZIKV infection and transient hearing loss

Transient hearing loss in adults associated with Zika virus infection

In 2015, during the outbreak of Zika virus (ZIKV) in Brazil, we identified 3 cases of acute hearing loss after exanthematous illness. Serology yielded finding compatible with ZIKV as the cause of a confirmed (n = 1) and a probable (n = 2) flavivirus infection, indicating an association between ZIKV infection and transient hearing loss.status: publishe