Analysis of Trabecular Bone Mechanics Using Machine Learning

“Bone remodeling” is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of “drug design, treatment, and therapy” of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data.

Spatial gradients of blood vessels and hematopoietic stem and progenitor cells within the marrow cavities of the human skeleton

This report evaluates the spatial profile of blood vessel fragments (BVFs) and CD34+ and CD117+ hematopoietic stem and progenitor cells (HSPCs) in human cancellous bone. Bone specimens were sectioned, immunostained (anti-CD34 and anti-CD117), and digitally imaged. Immunoreactive cells and vessels were then optically and morphometrically identified and labeled on the corresponding digital image. The distance of each BVF, or CD34+ or CD117+ HSPC to the nearest trabecular surface was measured and binned in 50-μm increments. The relative concentration of HSPCs and BVFs within cancellous marrow was observed to diminish with increasing distance in the marrow space. On average, 50% of the CD34+ HSPC population, 60% of the CD117+ HSPC population, and 72% of the BVFs were found within 100 μm of the bone surfaces. HSPCs were also found to exist in close proximity to BVFs, which supports the notion of a shared HSPC and vessel spatial niche.