Publications by authors named "G Pascual"

Recent developments have broadened our perception of SARS-CoV-2, indicating its capability to affect the body systemically beyond its initial recognition as a mere respiratory pathogen. However, the pathways of its widespread are not well understood. Employing a dual-modality approach, we integrated findings from a Murine Hepatitis Virus (MHV) infection model with corroborative clinical data to investigate the pervasive reach of Coronaviruses.

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Background/objectives: Prostate cancer (PCa) is the leading malignancy and the third most common cause of cancer-related death in Argentinian men. Predicting outcomes in localized PCa remains difficult due to tumor heterogeneity. In this study, we assessed the impact of (CAG) and c.

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Prosthetic mesh infection constitutes one of the major postsurgical complications following abdominal hernia repair. Antibacterial coatings represent a prophylactic strategy to reduce the risk of infection. This study assessed the in vitro response of two antibacterial gels made of 1% carboxymethylcellulose (CMC) functionalized with an antiseptic (chlorhexidine, CHX) or an antibiotic (rifampicin, RIF), developed for the coating of polypropylene (PP) meshes for hernia repair.

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Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (, , , , , , and ) was validated across independent cohorts and patient-derived xenograft (PDX) models.

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Article Synopsis
  • * The study used machine learning methods to develop a 7-gene signature, which was validated through various datasets and models, showing strong links to patient survival outcomes.
  • * This gene signature not only helps identify specific harmful NEPC subtypes but also predicts poor prognosis in prostate cancer cases displaying this signature, enhancing personalized treatment strategies.
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