The involvement of epidural fat in ossification of the ligamentum flavum: From the perspective of exosomal proteome.

Ossification of the ligamentum flavum (OLF) is the primary etiology of thoracic spinal stenosis. The functional properties of epidural fat (EF), an adipose tissue located in close proximity to ligamentum flavum (LF), have been scarcely investigated. The metabolic state of adipocytes significantly influences their functionality, and exosomes play a pivotal role in intercellular communication. This study aimed to investigate the role of EF-derived exosomes in OLF and characterize their protein profile by proteomics analysis. Our findings demonstrate that exosomes obtained from EF adjacent to OLF possess the ability to enhance osteogenesis of fibroblasts in vitro. Furthermore, proteomics analysis revealed metabolic dysfunction in EF adipocytes and identified lactate dehydrogenase A (LDHA) as a potential mediator involved in the development of OLF. This study provides new insights into the pathogenic mechanism underlying OLF and offers a theoretical basis for preventing and treating ligament ossification.

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning.

The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy's composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys.