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Rabies is an ancient neuroinvasive viral (genus Lyssavirus, family Rhabdoviridae) disease affecting approximately 59,000 people worldwide. The central nervous system (CNS) is targeted, and rabies has a case fatality rate of almost 100% in humans and animals. Rabies is entirely preventable through proper vaccination, and thus, the highest incidence is typically observed in developing countries, mainly in Africa and Asia. However, there are still cases in European countries and the United States. Recently, demographic, increasing income levels, and the coronavirus disease 2019 (COVID-19) pandemic have caused a massive raising in the animal population, enhancing the need for preventive measures (e.g., vaccination, surveillance, and animal control programs), postexposure prophylaxis, and a better understanding of rabies pathophysiology to identify therapeutic targets, since there is no effective treatment after the onset of clinical manifestations. Here, we review the neuroimmune biology and mechanisms of rabies. Its pathogenesis involves a complex and poorly understood modulation of immune and brain functions associated with metabolic, synaptic, and neuronal impairments, resulting in fatal outcomes without significant histopathological lesions in the CNS. In this context, the neuroimmunological and neurochemical aspects of excitatory/inhibitory signaling (e.g., GABA/glutamate crosstalk) are likely related to the clinical manifestations of rabies infection. Uncovering new links between immunopathological mechanisms and neurochemical imbalance will be essential to identify novel potential therapeutic targets to reduce rabies morbidity and mortality.

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In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. This study applied the techniques in two existing response surface method (RSM) experiments involving MICP technique. Random forest-based models and artificial neural network-based models were submitted through the optimization of hyperparameters via cross-validation technique and grid search, to select the best-optimized model. For this study, the random forest-based algorithm is aimed at having the best performance of 0.9381 and 0.9463 in comparison to the original r2 of 0.9021 and 0.8530, respectively. This study is aimed at exploring the capability of using machine learning-based models in small datasets for the purpose of optimization of experimental variables in MICP technique and the meaningfulness of the models by their specificities in the small experimental datasets applied to experimental designs. This study is aimed at exploring the capability of using machine learning-based models in small datasets for experimental variable optimization in MICP technique. The use of these techniques can create prerogatives to scale and mitigate costs in future experiments associated to the field.

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Background: We evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort. Methods: This prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan-Meier curves and Cox proportional-hazard models were performed. Findings: 1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9-32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8-29.2)] died during hospitalization (median time 14 (IQR,9-24) days). Older age [vs<40 years; age=60-69 years-aHR=1.89 (95%CI,1.08-3.32); age=70-79 years-aHR=2.52 (95%CI,1.42-4.45); age≥80-aHR=2.90 (95%CI 1.54-5.47)]; noninvasive or mechanical ventilation at admission [vs facial-mask or none; aHR=1.69 (95%CI 1.30-2.19)]; SAPS-III score≥57 [vs<57; aHR=1.47 (95%CI 1.13-1.92)] and SOFA score≥10 [vs <10; aHR=1.51 (95%CI 1.08-2.10)] were independently associated with in-hospital mortality. A total of 65 individuals [6.7% (95%CI 5.3-8.4)] had a rehospitalization or death [rate=323 (95%CI 250-417) per 1000 person-years] in a median time of 52 (range 1-280) days post-hospital discharge. Age ≥ 60 years [vs <60, aHR=2.13 (95%CI 1.15-3.94)] and SAPS-III ≥57 at admission [vs <57, aHR=2.37 (95%CI 1.22-4.59)] were independently associated with rehospitalization or death after hospital discharge. Interpretation: High in-hospital mortality rates due to COVID-19 were observed and elderly people remained at high risk of rehospitalization and death after hospital discharge. Funding: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Programa INOVA-FIOCRUZ.

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Xylem vulnerability to embolism represents an important trait to determine species distribution patterns and drought resistance. However, estimating embolism resistance frequently requires time-consuming and ambiguous hydraulic lab measurements. Based on a recently developed pneumatic method, we present and test the "Pneumatron", a device that generates high time-resolution and fully automated vulnerability curves. Embolism resistance is estimated by applying a partial vacuum to extract air from an excised xylem sample, while monitoring the pressure change over time. Although the amount of gas extracted is strongly correlated with the percentage loss of xylem conductivity, validation of the Pneumatron was performed by comparison with the optical method for Eucalyptus camaldulensis leaves. The Pneumatron improved the precision of the pneumatic method considerably, facilitating the detection of small differences in the (percentage of air discharged [PAD] < 0.47%). Hence, the Pneumatron can directly measure the 50% PAD without any fitting of vulnerability curves. PAD and embolism frequency based on the optical method were strongly correlated (r2 = 0.93) for E. camaldulensis. By providing an open source platform, the Pneumatron represents an easy, low-cost, and powerful tool for field measurements, which can significantly improve our understanding of plant-water relations and the mechanisms behind embolism.

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