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MXenes, a rapidly growing family of two-dimensional carbides and nitrides, have attracted attention for their high electrical conductivity and highly tunable surface chemistry. The recent synthesis of MXenes featuring triatomic borate (BO ) terminations via a molten route further expanded the range of achievable surface functionalities. Here, we employ density functional theory calculations to systematically investigate a selection of BO -terminated MXenes, including Ti N, Ti C, V C, Nb C, Ta C, Ti C , Ti N , Ti C , V C , Nb C , and Ta C . Our calculations reveal that such BO polyanionic terminations significantly distort the MXene lattice, increasing the thickness of each M X layer compared to the corresponding parent MAX phases. These structural changes are accompanied by pronounced near-surface charge transfer, indicative of strong bonding interactions between the MXene and BO functional groups. Electronic structure analysis further demonstrates that surface BO units introduce additional electronic states near the Fermi level, potentially enhancing transport properties relative to Cl-terminated MXenes. Thermodynamic modeling confirms that triatomic borate terminations are energetically favorable under realistic experimental conditions, explaining why these groups can dominate over chlorine terminations during the reported synthesis route. Collectively, our results elucidate how borate functionalization reshapes the structural and electronic properties of MXenes, offering valuable insights into the strategic engineering of advanced two-dimensional materials tailored for multifunctional applications.

Leptospirosis is an important public health problem affecting vulnerable urban slum populations in developing country settings. However, the complex interaction of meteorological factors driving the temporal trends of leptospirosis remain incompletely understood. From March 1996-March 2010, we investigated the association between the weekly incidence of leptospirosis and meteorological anomalies in the city of Salvador, Brazil by using a dynamic generalized linear model that accounted for time lags, overall trend, and seasonal variation. Our model showed an increase of leptospirosis cases associated with higher than expected rainfall, lower than expected temperature and higher than expected humidity. There was a lag of one-to-two weeks between weekly values for significant meteorological variables and leptospirosis incidence. Independent of the season, a weekly cumulative rainfall anomaly of 20 mm increased the risk of leptospirosis by 12% compared to a week following the expected seasonal pattern. Finally, over the 14-year study period, the annual incidence of leptospirosis decreased significantly by a factor of 2.7 (8.3 versus 3.0 per 100,000 people), independently of variations in climate. Strategies to control leptospirosis should focus on avoiding contact with contaminated sources of Leptospira as well as on increasing awareness in the population and health professionals within the short time window after low-level or extreme high-level rainfall events. Increased leptospirosis incidence was restricted to one-to-two weeks after those events suggesting that infectious Leptospira survival may be limited to short time intervals.

The clinical outcomes associated with Zika virus (ZIKV) in the Americas have been well documented, but other aspects of the pandemic, such as attack rates and risk factors, are poorly understood. We prospectively followed a cohort of 1453 urban residents in Salvador, Brazil, and, using an assay that measured immunoglobulin G3 (IgG3) responses against ZIKV NS1 antigen, we estimated that 73% of individuals were infected during the 2015 outbreak. Attack rates were spatially heterogeneous, varying by a factor of 3 within a community spanning 0.17 square kilometers. Preexisting high antibody titers to dengue virus were associated with reduced risk of ZIKV infection and symptoms. The landscape of ZIKV immunity that now exists may affect the risk for future transmission.

Vaccination is a major strategy to prevent the coronavirus disease 2019 (COVID-19). However, information about factors associated with men and women intention to be vaccinated are scarce. To determine COVID-19 vaccine acceptance and identify factors associated vaccine hesitancy according to sex, we performed a cross-sectional population-based random survey in Salvador, Brazil between Nov/2020-Jan/2021. Participants were interviewed to obtain data on intention to receive and pay for a COVID-19 vaccine, as well as on demographics, comorbidities, influenza vaccination history, previous diagnosis of COVID-19, and exposures and perception of COVID-19 risk. Among 2,521 participants, 2,053 (81.4%) reported willingness to use a COVID-19 vaccine and 468 (18.6%) hesitated to take it. Among those intending to get vaccinated, 1,400 (68.2%) would pay for the vaccine if necessary. Sex-stratified multivariable analysis found that men who were working and who had comorbidities were less likely to hesitate about using the vaccine. Among women, higher educational level and high perception of COVID-19 risk were associated with less vaccine hesitancy. In both groups, reporting influenza vaccination in 2020 reduced the chance of COVID-19 vaccine hesitancy. COVID-19 vaccine campaigns targeting to reduce vaccine hesitancy are urgently needed. These campaigns should consider gender differences in order to be successful.

Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing. Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment chest organ Sample Characteristic - Location Brazil

Leptospirosis has emerged as an urban health problem as slum settlements have rapidly spread worldwide and created conditions for rat-borne transmission. Prospective studies have not been performed to determine the disease burden, identify risk factors for infection and provide information needed to guide interventions in these marginalized communities. We enrolled and followed a cohort of 2,003 residents from a slum community in the city of Salvador, Brazil. Baseline and one-year serosurveys were performed to identify primary and secondary Leptospira infections, defined as respectively, seroconversion and four-fold rise in microscopic agglutination titers. We used multinomial logistic regression models to evaluate risk exposures for acquiring primary and secondary infection. A total of 51 Leptospira infections were identified among 1,585 (79%) participants who completed the one-year follow-up protocol. The crude infection rate was 37.8 per 1,000 person-years. The secondary infection rate was 2.3 times higher than that of primary infection rate (71.7 and 31.1 infections per 1,000 person-years, respectively). Male gender (OR 2.88; 95% CI 1.40-5.91) and lower per capita household income (OR 0.54; 95% CI, 0.30-0.98 for an increase of $1 per person per day) were independent risk factors for primary infection. In contrast, the 15-34 year age group (OR 10.82, 95% CI 1.38-85.08), and proximity of residence to an open sewer (OR 0.95; 0.91-0.99 for an increase of 1 m distance) were significant risk factors for secondary infection. This study found that slum residents had high risk (>3% per year) for acquiring a Leptospira infection. Re-infection is a frequent event and occurs in regions of slum settlements that are in proximity to open sewers. Effective prevention of leptospirosis will therefore require interventions that address the infrastructure deficiencies that contribute to repeated exposures among slum inhabitants.

Ex situ seed conservation is an effective strategy to help safeguarding plants from extinction. The updated Global Strategy for Plant Conservation’s (GSPC) Target 8 aims to include 75% of threatened plant species in ex situ collections by 2020, preferably in the country of origin. Halfway through the updated GSPC program, we evaluate the current state of knowledge and practice of ex situ seed conservation of threatened species from megadiverse Brazilian flora, contributing to this Target. We identify knowledge gaps and costs to achieve Target 8 through seed banking in Brazil within the time constraints of the GSPC and in a scenario of recent science budget funding cuts. Knowledge on seed storage behavior is available only for 24 Brazilian species (1.3%). Seed desiccation tolerance was concluded for 175 of 228 species, feasibly allowing safe storage of most Brazilian species at sub-zero temperatures. However, only 26 species (1.3%) are effectively banked in research institutions. Surprisingly, the percentage of banked threatened species hardly increased in the first 5 years since the update of the Target (0.55%, 2011–2015), and Brazil now faces the challenge of banking almost 1500 species during 2016–2020. Despite a major lack of commitment of Brazilian institutions and of knowledge to achieve the Target, the costs for banking the remaining species were estimated to be only US$3.9 million. We call for a nationwide coordinated effort of government agencies, policy makers and research institutions to include ex situ seed conservation in the environmental agenda to pursue achievement of the Target by 2020.

Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures. The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums, which vary at fine spatial scales and influence transmission of the bacterial agent. We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003-2007) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources. We completed annual serosurveys to ascertain leptospiral infection based on serological evidence. Among the 1,730 (86%) individuals who completed at least one year of follow-up, the infection rate was 35.4 (95% CI, 30.7-40.6) per 1,000 annual follow-up events. Male gender, illiteracy, and age were independently associated with infection risk. Environmental risk factors included rat infestation (OR 1.46, 95% CI, 1.00-2.16), contact with mud (OR 1.57, 95% CI 1.17-2.17) and lower household elevation (OR 0.92 per 10m increase in elevation, 95% CI 0.82-1.04). The spatial distribution of infection risk was highly heterogeneous and varied across small scales. Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk, but there was a significant residual component that was best explained by the spatial random effect. Although infection risk varied between years, the spatial distribution of risk associated with fixed and random effects did not vary temporally. Specific \"hot-spots\" consistently had higher transmission risk during study years. The risk for leptospiral infection in urban slums is determined in large part by structural features, both social and environmental. Our findings indicate that topographic factors such as household elevation and inadequate drainage increase risk by promoting contact with mud and suggest that the soil-water interface serves as the environmental reservoir for spillover transmission. The use of a spatiotemporal approach allowed the identification of geographic outliers with unexplained risk patterns. This approach, in addition to guiding targeted community-based interventions and identifying new hypotheses, may have general applicability towards addressing environmentally-transmitted diseases that have emerged in complex urban slum settings.

This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V 2 and then a recurrent neural network long short-term memory model. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model’s generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes.

Domestic rats are the principal reservoir for urban leptospirosis. However, few studies have identified infestation markers in slums and evaluated their predictivity for leptospirosis risk. We compared households with leptospirosis cases in Salvador, Brazil between 2007 and 2009 and their neighbors using a case control design, surveying for rodent infestation signs and environmental characteristics. With the 2007–2008 data, a conditional logistic regression modeling identified the peridomiciliar presence of rodent burrows (OR, 3.30; 95% CI, 1.50–7.26), rat feces (2.86; 1.24–6.59), runs (2.57; 1.06–6.22), households bordering abandoned houses (2.48; 1.04–6.02), and unplastered walls (2.22; 1.02–6.02) as risk factors and developed a predictive score for leptospirosis. With an independent data set from 2009, a receiver operating characteristic (ROC) curve analysis evaluated the prediction score performance, with the area under the curve being 0.70 (95% CI, 0.64–0.76) for score development and 0.71 (0.65–0.79) for validation. Results indicate that high proportions of urban slum households are infested with R . norvegicus . The score performed well when identifying high-risk households within slums. These findings need confirmation in other urban centers, but suggest that community-based screening for rodent infestation can allow to target rodent and environmental control measures in populations at highest risk for leptospirosis.