Publications by authors named "T Dottorini"
In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V.
View Article and Find Full Text PDFSharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods.
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- Prediction of calving in dairy cows is essential for effective herd management, especially as herd sizes grow.
- An automated machine learning algorithm was developed, utilizing data from a reticuloruminal bolus sensor to predict calving with high accuracy (up to 87.81%) using temperature, activity, and drinking data up to 5 days in advance.
- Combining multiple feature characteristics improves prediction performance, with significant increases in key metrics like sensitivity and positive predictive value when incorporating data beyond just temperature.
Article Synopsis
- China leads in antimicrobial consumption, making improved surveillance crucial to tackle antimicrobial resistance (AMR).
- A study on chicken farms and abattoirs identified 145 potentially mobile antibiotic resistance genes (ARGs) shared among chickens and their environments, emphasizing the link between gut microbes and AMR in Escherichia coli.
- Findings suggest environmental factors like temperature and humidity influence ARG presence, highlighting the complex interplay between livestock environments, microbial communities, and AMR that could inform better surveillance strategies.
Introduction: Characterization of the tumour immune infiltrate (notably CD8+ T-cells) has strong predictive survival value for cancer patients. Quantification of CD8 T-cells alone cannot determine antigenic experience, as not all infiltrating T-cells recognize tumour antigens. Activated tumour-specific tissue resident memory CD8 T-cells (T) can be defined by the co-express of CD103, CD39 and CD8.
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