Publications by authors named "M L Del Basso De Caro"

Ischemic stroke is the result of a permanent or transient occlusion of a brain artery, leading to irreversible tissue injury and long-term sequelae. Despite ongoing advancements in revascularization techniques, stroke remains the second leading cause of death worldwide. A comprehensive understanding of the complex and interconnected mechanisms, along with the endogenous mediators that modulate stroke responses is essential for the development of effective interventions.

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Article Synopsis
  • Developing an automatic acne grading system using machine learning requires extensive data and labeling, but the authors created a model that performs well on low-resolution images from fewer patients with uneven acne severity distribution.
  • The study utilized 1,374 paired images from 391 patients, expertly labeled, to train a deep learning model which achieved 66.67% accuracy on the test set.
  • The results suggest that this model can be a viable tool for medical practitioners and provide standardized assessments for patients, highlighting its potential despite limited data.
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Tuberculosis (TB) remains one of the leading causes of death among infectious diseases, with 10.6 million new cases and 1.3 million deaths reported in 2022, according to the most recent WHO report.

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In ovine populations, the enzootic nature of () is attributed to its capacity to establish persistent intracellular infections, which necessitate a cellular immune response mediated by interferon-gamma (IFN-) for effective resolution. In both natural hosts and murine models, interleukin-10 (IL-10) has been demonstrated to modulate the cellular immune response crucial for the eradication of . During gestation, it has also been shown to play a role in preventing inflammatory damage to gestational tissues and foetal loss through the downregulation of pro-inflammatory cytokines.

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Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power.

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