Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard.

Debiasing Sinkhorn divergence in optimal transport of cellular dynamics.

Single-cell RNA-seq analysis characterizes developmental mechanisms of cellular differentiation, lineage determination, and reprogramming with differential conditioning of the microenvironment. In this article, the underlying dynamics are formulated via optimal transport with algorithms that calculate the transition probability of the state of cell dynamics over time. The algorithmic biases of optimal transport (OT) due to entropic regularization are balanced by Sinkhorn divergence, which normally de-biases the regularized transport by centering them.

Large-scale genetic characterization of Parkinson's disease in the African and African admixed populations.

Elucidating the genetic contributions to Parkinson's disease (PD) etiology across diverse ancestries is a critical priority for the development of targeted therapies in a global context. We conducted the largest sequencing characterization of potentially disease-causing, protein-altering and splicing mutations in 710 cases and 11,827 controls from genetically predicted African or African admixed ancestries. We explored copy number variants (CNVs) and runs of homozygosity (ROHs) in prioritized early onset and familial cases.