Publications by authors named "S Tiberi"
Background: Non-tuberculous mycobacterial (NTM) pulmonary disease (PD) is a significant concern in China, compounding the existing burden of TB. This review aims to summarise the treatment outcomes for NTM-PD in China.
Methods: We reviewed the evidence on NTM-PD, including treatment regimens and clinical outcomes, from 17 studies identified through screening of three Chinese biomedical databases.
Introduction: Global migration has increased in recent decades owing to war, conflict, persecution and natural disasters, but also secondary to increased opportunities related to work or study. Migrants' risk of tuberculosis (TB) differs depending on migration, socioeconomic status, mode of travel and TB risk in transit, TB incidence and healthcare provision in country of origin. Despite advances in TB care for migrants and new treatment strategies, decisions for managing migrants at risk of TB often rely on expert opinions, rather than clinical evidence.
View Article and Find Full Text PDFObjective: Many biologic agents cause some degree of immunosuppression, which can increase the risk of reactivation of tuberculosis infection (TBI). This risk is variable between individual biologics. We aimed to assess current (and recommended) clinical practice of TBI screening and treatment among patients initiating treatment with biologic agents.
View Article and Find Full Text PDFArticle Synopsis
- Studying protein isoforms is crucial for biomedical research, but current methods using bottom-up mass spectrometry often face challenges like noisy detection and shared peptides, making it hard to analyze individual isoforms.
- A new statistical method is introduced to enhance protein isoform analysis by combining mass spectrometry and transcriptomics data in a Bayesian framework, addressing uncertainties in peptide detection and abundance allocation.
- The method shows strong performance in simulations and real datasets, accurately inferring protein isoform presence, estimating their abundance, and detecting differences between protein and transcript levels; it is available as a free Bioconductor R package with usage examples.