Biology subjects

Felix, A.

Publications and source records attributed to Felix, A..

3 recordsLinked to original sources

bioRxiv2025

THE HUMAN NEUROBLASTOMA SH-SY5Y CELL LINE AS A MODEL TO ASSESS β-AMYLOID NEUROTOXICITY: A SYSTEMATIC REVIEW AND META-ANALYSIS

The SH-SY5Y human neuroblastoma cell line is widely used as an in vitro model of {beta}-amyloid (A{beta}) neurotoxicity in Alzheimers disease (AD). However, the lack of standardized protocols for assessing A{beta} toxicity - including differentiation strategies for SH-SY5Y cells - limits the comparability of results across studies. To address these issues, we conducted a systematic review and meta-analysis to evaluate how methodological factors influence A{beta}-induced toxicity in SH-SY5Y cells. We included 359 eligible studies encompassing 1,192 MTT-based comparisons of cell viability between A{beta}-treated and control SH-SY5Y cells. A three-level meta-analysis estimated mean cell viability after A{beta} exposure at 63% of control levels (95% CI [61.6; 64.3]), with very high heterogeneity (I{superscript 2}=99.6%). Meta-regression identified significant associations between increased toxicity and higher A{beta} concentrations, longer exposure durations, and the use of peptide preparations described as fibrils. Conversely, differentiation protocols, duration, and cell density did not significantly influence toxicity outcomes. Reporting quality was often poor, with frequent omissions regarding cell line origin, authentication, contamination testing, A{beta} preparation details and nature of the experimental unit. Overall, our findings show robust A{beta} toxicity in SH-SY5Y cells, primarily driven by dose, exposure time, and A{beta} aggregation state, but not cell differentiation status. Our conclusions highlight the critical need for better reporting of A{beta} exposure parameters to enhance reproducibility and translational potential in AD research.

neuroscience↗

bioRxiv2023

Phylogenetics, Epidemiology and Temporal Patterns of Dengue Virus type 1 in Araraquara, Sao Paulo State

Dengue virus is a prominent arbovirus with global spread, causing approximately 390 million infections each year. In Brazil, yearly epidemics follow a well-documented pattern of serotype replacement every three to four years on average. Araraquara, located in the state of Sao Paulo, has faced significant impacts from DENV epidemics since the emergence of DENV-1 in 2010. The municipality then transitioned from low to moderate endemicity in less than 10 years. Yet, there remains an insufficient understanding of the virus circulation dynamics, particularly concerning DENV-1, in the region, as well as the genetic characteristics of the virus. To address this, we generated 37 complete or partial DENV-1 genomes sampled from 2015 to 2022 in Araraquara. Then, using also Brazilian and worldwide DENV-1 sequences we reconstructed the evolutionary history of DENV-1 in Araraquara and estimated the time to the most recent common ancestor (tMRCA) for the serotype 1, for the genotype V and its main lineages. Within the last ten years, there were at least three introductions of genotype V in Araraquara, distributed in the two main lineages (LIa and LIb, and LII). The tMRCA for the first sampled lineage (2015/2016 epidemics) was approximately 15 years ago (in 2008). Crucially, our analysis challenges existing assumptions regarding emergence time of the DENV-1 genotypes, suggesting that genotype V might have diverged more recently than previously described. The presence of the two lineages of genotype V in the municipality might have contributed to the extended persistence of DENV-1 in the region.

genomics↗

bioRxiv2018

Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization

BackgroundWe created a family of prognostic models for Ebola virus disease from the largest dataset of EVD patients published to date. We incorporated these models into an app, \"Ebola Care Guidelines\", that provides access to recommended, evidence-based supportive care guidelines and highlights the signs/symptoms with the largest contribution to prognosis.\n\nMethodsWe applied multivariate logistic regression on 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone during the 2014-16 outbreak. We validated the models with two independent datasets from Sierra Leone.\n\nFindingsViral load and age were the most important predictors of death. We generated a parsimonious model including viral load, age, body temperature, bleeding, jaundice, dyspnea, dysphagia, and referral time recorded at triage. We also constructed fallback models for when variables in the parsimonious model are unavailable. The performance of the parsimonious model approached the predictive power of observational wellness assessments by experienced health workers, with Area Under the Curve (AUC) ranging from 0.7 to 0.8 and overall accuracy of 64% to 74%.\n\nInterpretationMachine-learning models and mHealth tools have the potential for improving the standard of care in low-resource settings and emergency scenarios, but data incompleteness and lack of generalizable models are major obstacles. We showed how harmonization of multiple datasets yields prognostic models that can be validated across different cohorts. Similar performance between the parsimonious model and those incorporating expert wellness assessments suggests that clinically-guided machine learning approaches can recapitulate clinical expertise, and thus be useful when such expertise is unavailable. We also demonstrated with our guidelines app how integration of those models with mobile technologies enables deployable clinical management support tools that facilitate access to comprehensive bodies of medical knowledge.\n\nFundingHoward Hughes Medical Institute, US National Institutes of Health

bioinformatics↗