Development of a quantitative prediction algorithm for human cord blood-derived CD34 hematopoietic stem-progenitor cells using parametric and non-parametric machine learning models.

The transplantation of CD34 hematopoietic stem-progenitor cells (HSPCs) derived from cord blood serves as the standard treatment for selected hematological, oncological, metabolic, and immunodeficiency disorders, of which the dose is pivotal to the clinical outcome. Based on numerous maternal and neonatal parameters, we evaluated the predictive power of mathematical pipelines to the proportion of CD34 cells in the final cryopreserved cord blood product adopting both parametric and non-parametric algorithms. Twenty-four predictor variables associated with the cord blood processing of 802 processed cord blood units randomly sampled in 2020-2022 were retrieved and analyzed.

Highly stabilized curcumin nanoparticles tested in an in vitro blood-brain barrier model and in Alzheimer's disease Tg2576 mice.

The therapeutic effects of curcumin in treating Alzheimer's disease (AD) depend on the ability to penetrate the blood-brain barrier. The latest nanoparticle technology can help to improve the bioavailability of curcumin, which is affected by the final particle size and stability. We developed a stable curcumin nanoparticle formulation to test in vitro and in AD model Tg2576 mice.