Publications by authors named "A De Silvestri"
Aim Of The Study: This real-life study is designed to investigate the short and long-term efficacy and safety of teduglutide (TED) and its effects on the quality of life (QoL) in a cohort of adult, stable patients with short bowel syndrome and chronic intestinal failure receiving long-term parenteral support (PS).
Patient And Methods: A prospective, single-center study was conducted for individuals who began to take TED between March 2017 and August 2023.
Results: Ten patients were included in the analysis, among whom the median duration of TED administration was 48 (range, 12-71) months.
Article Synopsis
- ! Antibiotics are given to 20%-30% of pregnant women, yet their impact on newborn immune development is not well understood, leading to increased susceptibility to sepsis in offspring of treated mothers.
- ! Newborn mice from antibiotic-treated mothers showed lower levels of immunoglobulin A (IgA) in breast milk and feces, leading to a weakened gut immune response and increased risks of gut bacteria entering the bloodstream.
- ! Restoring IgA production through treatments or using breast milk from untreated mothers improved immune profiles and offered protection against sepsis in pups, emphasizing the importance of breastfeeding in mitigating antibiotic effects.
Cross-kingdom RNA interference (ckRNAi) is a mechanism of interspecies communication where small RNAs (sRNAs) are transported from one organism to another; these sRNAs silence target genes in trans by loading into host AGO proteins. In this work, we investigated the occurrence of ckRNAi in Arbuscular Mycorrhizal Symbiosis (AMS). We used an in silico prediction analysis to identify a sRNA (Rir2216) from the AM fungus Rhizophagus irregularis and its putative plant gene target, the Medicago truncatula MtWRKY69 transcription factor.
View Article and Find Full Text PDFBackground: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
Objective: This study aims to systematically evaluate and quantify the performance of machine learning (ML) algorithms in predicting the risk of hospitalisation and emergency department (ED) admission for acute asthma exacerbations in children.