Biology subjects

Garcia-Garcia, J.

Publications and source records attributed to Garcia-Garcia, J..

3 recordsLinked to original sources

bioRxiv2024

The relationship between gut and nasopharyngeal microbiome composition can predict the severity of COVID-19

Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity, and microbiota could play a key role in the infection, progression, and outcome of the disease. SARS-CoV-2 infection has been associated with nasopharyngeal and gut dysbiosis and higher abundance of opportunistic pathogens. To identify new prognostic markers for the disease, a multicenter prospective observational cohort study was carried out in COVID-19 patients divided into three cohorts based on symptomatology: mild (n=24), moderate (n=51), and severe/critical (n=31). Faecal and nasopharyngeal samples were taken, and the microbiota was analyzed. Linear discriminant analysis identified M. salivarium, P. dentalis, and H. parainfluenzae as biomarkers of severe COVID-19 in nasopharyngeal microbiota, while P. bivia and P. timonensis were defined in faecal microbiota. Additionally, a connection between faecal and nasopharyngeal microbiota was identified, with a significant ratio between P. timonensis (faeces) and P. dentalis and M. salivarium (nasopharyngeal) abundances found in critically ill patients. This ratio could serve as a novel prognostic tool for identifying severe COVID-19 cases.

microbiology↗

bioRxiv2019

Isolation and quantification of blood apoptotic bodies in neurological patients

Dysregulation of apoptosis may contribute to the etiology and/or progression of several prevalent diseases, including stroke and neurodegenerative pathologies. So, detection of the apoptotic processes in patients would be useful in daily clinical practice, However, the in vivo analysis of apoptosis that occurs in tissue has limitations. We therefore propose to use circulating apoptotic bodies as biomarkers for measuring apoptotic death in patients. Since there is no scientific literature establishing the most appropriate method for measuring apoptotic bodies from human blood samples, we here describe a reproducible centrifugation-based method combined with electron microscopy, dynamic light scattering and flow cytometry studies to isolate, characterize and quantify plasma apoptotic bodies of patients with ischemic stroke, multiple sclerosis and Parkinsons disease. The analysis revealed that our isolation protocol achieves notable recovery rates of highly-purified intact apoptotic bodies. This easy and rapid procedure would help physicians to implement the use of plasma apoptotic bodies analysis as a non-invasive tool to monitor apoptotic death in neurological patients for prognostic purposes and for following disease activity and assessing drug effectiveness.

neuroscience↗

bioRxiv2018

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.

bioinformatics↗