Centro de Excelência em Bioinformática

Introdução: O transplante renal é o tratamento mais eficaz para pacientes com insuficiência renal crônica terminal, mas complicações urológicas, como obstrução ureteral, podem comprometer o enxerto. O stent ureteral é amplamente utilizado para prevenir essas complicações, embora esteja associado a riscos, como infecção urinária e formação de cálculos, gerando controvérsias quanto ao seu benefício. Objetivo: Avaliar o impacto do uso do stent ureteral no transplante renal e sua influência na redução de complicações urológicas. Métodos: Revisão integrativa realizada nas bases PubMed e Biblioteca Virtual em Saúde (BVS), incluindo artigos em português e inglês publicados de 2015 a 2025 disponíveis em texto completo e envolvendo adultos. Foram selecionados 20 estudos após triagem de 287 registros.

Introdução: A síndrome de Down é causada pela trissomia do cromossomo 21. Indivíduos com SD apresentam envelhecimento precoce e maior risco de Alzheimer (DA). Estudos recentes identificaram 63 genes e 42 marcadores genéticos associados à neuroinflamação em indivíduos com SD, com destaque para a superexpressão de genes triplicados como SOD1, APP, S100B, TREM2. Esses resultados indicam que intervenções com antioxidantes, como a vitamina E, possam ter papel relevante na modulação desses mecanismos patológicos. No entanto, são incertas as implicações clínicas do uso desses compostos nessa população. Objetivo: Revisar as evidências disponíveis sobre o uso da vitamina E e outros antioxidantes em pessoas com SD e demência, com ênfase nos efeitos sobre estresse oxidativo e neurodegeneração, além de identificar perspectivas futuras no uso dessas terapias com base em marcadores genéticos. Métodos: Foi realizada uma revisão narrativa inicial da literatura científica, com buscas nas bases PubMed e Embase, utilizando os descritores: “antioxidants”, “vitamin E”, “Down syndrome”, “dementia”, “oxidative stress”, “SOD1”, “genetics”, “pharmacogenomics”. Foram incluídos artigos originais e revisões que abordassem intervenções antioxidantes em pessoas com SD e demência. A revisão foi orientada por três perguntas norteadoras: quais antioxidantes têm sido estudados em pessoas com SD e demência; quais evidências existem sobre os efeitos da vitamina E em processos associados ao estresse oxidativo e neurodegeneração em SD; e quais são as principais lacunas e perspectivas futuras envolvendo terapias baseadas em farmacogenômica para essa população. Resultados: Foram identificados estudos experimentais e clínicos explorando o uso da vitamina E, vitamina C e ácido alfa-lipóico como agentes antioxidantes em pessoas com SD. A vitamina E foi o composto mais investigado, com dados sugerindo efeitos benéficos na redução de biomarcadores de estresse oxidativo e possível desaceleração do declínio cognitivo. No entanto, os resultados dos estudos clínicos apresentam divergências entre si, além de limitações metodológicas, pequeno tamanho amostral e variabilidade na definição dos desfechos. Há escassez de ensaios clínicos randomizados robustos para esses compostos e quase inexistência de abordagens que estratifiquem os participantes com base em perfis genéticos, como o status de genes relacionados ao processo neuroinflamatório. Conclusões: O uso da vitamina E é uma alternativa promissora para o tratamento da demência na população com SD. No entanto, os dados ainda são preliminares, e faltam estudos que integrem variabilidade da expressão gênica na análise de eficácia e segurança dessas intervenções. Os resultados desta revisão inicial indicam a necessidade de estudos mais completos e sugerem o potencial da farmacogenômica como ferramenta futura para personalização terapêutica nessa população.

Context Aylacostoma species (Gastropoda, Hemisinidae) are sensitive to environmental changes and low dissolved oxygen concentrations, making them particularly vulnerable to disturbances such as dam construction. The ‘Aylacostoma Project’ aims to conserve some species of the genus, maintaining populations in captivity. Phylogeographic studies play a key role in this context, because they provide insights into the genetic diversity of populations, an essential aspect for conservation. Aims The main objective of this study was to investigate the phylogeographic structure and genetic diversity of the following three Aylacostoma species from the Paraná River Basin: A. brunneum, Aylacostoma sp. and A. chloroticum. Methods The analyses were based on 81 partial sequences of the mitochondrial COI region from different localities, obtained from GenBank and the present study. Key results Eight haplotypes were identified across the analysed populations, with A. chloroticum comprising six haplotypes. Some haplotypes were exclusive to specific localities. Conclusion This study has highlighted new information about the phylogeographic pattern of the group, showing a phylogeographic structure among Aylacostoma species from the Paraná River Basin. Implications This is the first phylogeographic study of Aylacostoma to include molecular data from natural populations of A. chloroticum and Aylacostoma sp. from the upper Paraná River Basin, Brazil.

Objetivo: Analisar alterações histopatológicas em nervos sensitivos de pacientes com espasticidade submetidos à rizotomia dorsal seletiva, comparando perfis inflamatórios e degenerativos entre casos de Paralisia Cerebral e Microcefalia associada ao Zika Vírus, além de investigar correlações com parâmetros clínico-funcionais. Métodos: Estudo observacional, transversal e analítico com 8 pacientes (4 com paralisia cerebral e 4 com microcefalia). Foram coletadas amostras de nervos sensitivos durante o procedimento cirúrgico e realizada análise histopatológica para identificar processos inflamatórios e degenerativos. Os pacientes foram avaliados por meio do GMFCS, GMFM e Escala de Ashworth Modificada. Resultados: Alterações degenerativas foram encontradas em 100% das amostras e inflamatórias em 62,5%. Houve forte correlação positiva entre inflamação e degeneração (rs = 0,78; p = 0,023). Pacientes com paralisia cerebral apresentaram escores GMFM mais altos que os com microcefalia, especialmente nas dimensões A (Deitar e Rolar) e B (Sentar). Não houve diferenças significativas nos achados histopatológicos entre os grupos nem correlação destes com parâmetros funcionais. Conclusão: O estudo evidenciou degeneração universal em nervos sensitivos de pacientes com espasticidade, independentemente da causa, e forte relação com inflamação. Apesar da ausência de associação com função motora, os achados indicam possíveis alvos para terapias neuroprotetoras.

Abstract Background Recognizing sepsis as a critical driver of unexpected mortality in both emergency department and inpatient settings, our study seeks to construct a robust model capable of automatically predicting sepsis throughout the continuum of care.Table 1Impact of Sepsis on Mortality: Sepsis patients exhibited a significantly higher risk of death compared to non-sepsis patients. Sepsis had a devastating impact: 29.4% of sepsis patients died, representing a 44-fold higher relative risk compared with those without sepsisFigure 1Box-Plot showing the impact of sepsis on the length of hospital stays. Despite the enormous variability, the length of hospital stay was significantly higher in sepsis cases, with a median of 14 days, compared with a median of one day for non-sepsis patients Methods Single-center retrospective cohort study (Jan 2024-Mar 2025) of all emergency department or admitted patients at a public hospital in Belo Horizonte, Brazil (∼3M pop.). Sepsis: positive blood culture OR physician-documented clinical diagnosis (ICD-10 A40/A41). Multivariate logistic regression determined independent sepsis predictors, including 59 exam/assay requests (present/absent during care). The model built was made available as an application within the Datamart A.R.G.U.S.—Assistant for Recovery and Guarding of Urgent and Epidemiological Sentinels—a cloud-based platform hosted on AWS and developed by our team that integrates patient Electronic Medical Records.Table 2Independent predictors of sepsis identified by multivariate logistic regression among requested exams, assays, or procedures. The stepwise logistic regression model identified 10 exams, assays, or procedures that, when requested for a patient, demonstrate high predictive ability for sepsis.Figure 2ROC (Receiver Operating Characteristic) curves evaluating the predictive performance of the logistic regression model built to predict sepsis. The stepwise logistic regression identified 10 readily available exams, assays, or procedures that exhibit high predictive ability for sepsis when requested during a patient's continuum of care Results A total of 162,537 patients evaluated in the emergency department or admitted to the hospital: 974 sepsis cases (0.6% risk); 250 cases (26% of sepsis cases) were identified as clinically diagnosed sepsis based solely on ICD-10 codes; 38 cases (4% of sepsis cases) received an ICD-10 sepsis code and had a positive blood culture; and 686 cases (70% of sepsis cases) had a positive blood culture. Sepsis had a devastating impact: 29.4% of sepsis patients died, representing a 44-fold higher relative risk compared with those without sepsis (Table 1). Despite the enormous variability (Fig. 1), the length of hospital stay was significantly higher in sepsis cases, with a median of 14 days, compared with a median of one day for non-sepsis patients. Patients were randomly split into two subsets: 113,424 patients for modeling and 49,113 for validating the logistic model. The stepwise logistic regression model identified 10 exams, assays, or procedures (Table 2) that, when requested for a patient, demonstrate high predictive ability for sepsis (Fig. 2). Conclusion Low 0.6% sepsis incidence in a Brazilian hospital contrasted with catastrophic 29.4% mortality (44x higher RR) and prolonged LOS (14 vs 1 day). Logistic regression identified 10 predictive exams/assays, part of a larger effort to optimize the Hospital Infection Control Service's sepsis protocol. Disclosures All Authors: No reported disclosures

The analysis and prediction of antibody-antigen (Ab-Ag) interactions often overlook critical structural features such as glycosylation and important physicochemical conditions like pH and salt concentration. Additionally, the field lacks standardized criteria for selecting complexes based on structural properties and sequence identity. Common practices in data set construction rely on removing redundancy using sequence identity thresholds, which can inadvertently exclude complexes with alternative binding modes that share identical sequences. To enable more precise Ab-Ag modeling and antibody engineering, it is essential to incorporate richer structural and physical information into both physics-based and machine learning models. To address these limitations, we present ANABAG, a new curated data set of Ab-Ag complexes annotated at the residue level with UniProt sequence information and enriched with a wide range of structural and physicochemical features. The data set allows flexible filtering of complexes using a variety of descriptors available at both the complex and residue levels. Selected features are ready to use in machine learning workflows, while the structural files are compatible with antibody design and docking pipelines like Rosetta or Haddock. The complete data set is available on Zenodo at https://zenodo.org/records/17065788, and all accompanying scripts and usage documentation can be accessed via GitHub at https://github.com/DSIMB/anabag-handler.git.

This experience report describes an investigative didactic sequence carried out with a 10th grade class in a public high school in Espírito Santo, Brazil, focusing on the issue of microplastics in the human body. The proposal was guided by the question “How do microplastics enter our bodies?”, integrating ecology, public health, and environmental education. The methodology comprised three main stages: (i) brainstorming, to raise initial hypotheses; (ii) flipped classroom, in which students conducted research using different information sources and presented their findings in class; and (iii) production of infographics, aimed at systematizing and creatively expressing the knowledge constructed. Qualitative data analysis, based on the Meaning Cores technique, revealed three interpretative axes: perplexity in the face of the invisible, when students expressed surprise at the possibility of internal contamination by microplastics; the body as a space of contamination, as they connected the topic to their daily lives and health; and critical responsibility, with signs of reflective and proactive stances toward the environmental issue. The results show that inquiry-based teaching fostered greater engagement, student protagonism, and critical appropriation of scientific concepts, although gaps were observed in mobilizing previously studied notions such as bioaccumulation. The experience contributed to scientific literacy by establishing meaningful connections between science, environment, and society, reinforcing the importance of active methodologies in Biology teaching for the formation of critical and responsible individuals.

Abstract The analysis and prediction of antibody–antigen (Ab–Ag) interactions often overlook critical structural features such as glycosylation, physical chemical conditions like pH and salt concentration, as well as the lack of standardized criteria for selecting complexes based on structural properties and sequence identity. Common practices in dataset construction rely on removing redundancy using sequence identity thresholds, which can inadvertently exclude complexes with alternative binding modes that share identical sequences. To enable more precise Ab–Ag modeling and antibody engineering, it is essential to incorporate richer structural and physical information into both physics-based and machine learning models. To address these limitations, we present ANABAG, a new curated dataset of Ab–Ag complexes annotated at the residue level with UniProt sequence information and enriched with a wide range of structural and physicochemical features. The dataset allows flexible filtering of complexes using a variety of descriptors available at both the complex and residue levels. Selected features are ready to use in machine learning workflows, while the structural files are compatible with antibody design and docking pipelines like Rosetta or Haddock. The complete dataset is available on Zenodo, and all accompanying scripts and usage documentation can be accessed via GitHub at https://github.com/DSIMB/anabag-handler.git.