Publications by authors named "J Petucci"
Article Synopsis
- Intracerebral hemorrhage accounts for 15% of strokes and is linked to a significant risk of developing post-stroke epilepsy, but current predictive models for seizures are unreliable and underutilize available real-world data and AI technology.
- This study analyzes patients with intracerebral hemorrhage from 2015 to 2022 to develop machine-learning models that aim to predict seizure occurrence at 1 and 5 years after the hemorrhage.
- Results showed that out of a cohort of 85,679 patients, 4.57% experienced seizures within 1 year and 6.27% within 5 years, indicating a need for more effective prediction methods.
Article Synopsis
- The study aims to create a machine learning model that predicts the risk of seizures after ischemic stroke (IS) using a large dataset from Electronic Health Records, serving as a tool for clinical decision-making.
- Seizures often occur post-stroke and can negatively impact patient outcomes, making it essential to distinguish between high and low-risk individuals to improve treatment and clinical trial strategies.
- The study analyzed 430,254 IS patients and found 4.3% had seizures within 1 year and 5.3% within 5 years, using metrics like AUROC to evaluate the model's prediction performance, achieving various accuracy levels.
One-third of protein domains in the CATH database contain a recently discovered tertiary topological motif: non-covalent lasso entanglements, in which a segment of the protein backbone forms a loop closed by non-covalent interactions between residues and is threaded one or more times by the N- or C-terminal backbone segment. Unknown is how frequently this structural motif appears across the proteomes of organisms. And the correlation of these motifs with various classes of protein function and biological processes have not been quantified.
View Article and Find Full Text PDFA hydrogen atom can either physisorb or chemisorb onto a graphene surface. To describe the interaction of H with graphene, we trained the C-C, H-H, and C-H interactions of the ReaxFF CHO bond order potential to reproduce Density Functional Theory (DFT) generated values of graphene cohesive energy and lattice constant, H dissociation energy, H on graphene adsorption potentials, and H formation on graphene using the Eley-Rideal (ER) and Langmuir-Hinshelwood (LH) processes. The results, generated from the trained H-graphene potentials, are in close agreement with the corresponding results from DFT.
View Article and Find Full Text PDFThis is a report on a study of the adsorption characteristics of ethane on aggregates of unopened dahlia-like carbon nanohorns. This sorbent presents two main groups of adsorption sites: the outside surface of individual nanohorns and deep, interstitial spaces between neighbouring nanohorns towards the interior of the aggregates. We have explored the equilibrium properties of the adsorbed ethane films by determining the adsorption isotherms and isosteric heat of adsorption.
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