Publications by authors named "V I Almeida"

Traumatic brain injury (TBI) is a global public health condition that causes cognitive and behavioral deficits. This protocol assesses the potential of quantitative electroencephalogram (EEG) biomarkers, associated with inflammatory indicators, to predict mortality and functional recovery in patients with severe TBI. Through continuous monitoring and analysis of abnormal brain activity patterns, the protocol aims to personalize therapeutic interventions and improve patient quality of life.

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In critically ill patients, the occurrence of multidrug-resistant infection is a significant concern, given its ability to acquire multidrug-resistant, form biofilms and secrete toxic effectors. In Brazil, limited data are available regarding the prevalence of dissemination, and the impact of the type III secretion system (T3SS) on toxin production and biofilm formation in clinical isolates of . This study investigates the dissemination of virulent harbouring the and genes, the presence of T3SS genes and their biofilm-forming capability.

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Background: Proficiency in endotracheal intubation (ETI) is essential for medical professionals and its training should start at medical schools; however, large caseload may be required before achieving an acceptable success rate with direct laryngoscopy. Video laryngoscopy has proven to be an easier alternative for intubation with a faster learning curve, but its availability in medical training may be an issue due to its high market prices. We devised a low-cost 3-dimensionally printed video laryngoscope (3DVL) and performed a randomized trial to evaluate if the intubation success rate on the first attempt with this device is noninferior to a standard commercially available video laryngoscope (STVL).

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Traditional testing methods in pharmaceutical development can be time-consuming and costly, but in silico evaluation tools can offer a solution. Our in-house Active-IT system, a Ligand-Based Virtual Screening (LBVS) tool, was developed to predict the biological and pharmacological activities of small organic molecules. It includes four independent modules for generating molecular descriptors (3D-Pharma), machine learning modeling (ExCVBA), a database of bioactivity models, and a prediction module.

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Objectives: to characterize the sociodemographic and psychological aspects of university students who sought psychiatric care at a Student Support Center of a Federal University and to analyze associations between mental health issues and predisposing factors.

Methods: a retrospective analysis of 103 medical records was conducted. The statistical analysis consisted of two steps: a descriptive analysis and a predictive analysis using the Logistic Regression Model.

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