Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks.

Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quantification in adipocytes is significant for understanding the origins of obesity and associated complications, it remains challenging and requires the implementation of computer science innovations.

The Role of microRNA in the Regulation of Differentiation and the Functionality of Osteoblasts, Osteoclasts, and Their Precursors in Osteoporosis.

Osteoporosis is a complex disease that is affected by a variety of factors, including genetic and epigenetic influences. While DNA markers for osteoporosis have been identified, they do not fully explain the hereditary basis of the disease. Epigenetic factors, such as small microRNAs (miRNAs), may provide a missing link in understanding the molecular mechanisms underlying osteoporosis.

Pathogenetic therapeutic approaches for endocrine diseases based on antisense oligonucleotides and RNA-interference.

Molecular therapy uses nucleic acid-based therapeutics agents and becomes a promising alternative for disease conditions unresponsive to traditional pharmaceutical approaches. Antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs) are two well-known strategies used to modulate gene expression. RNA-targeted therapy can precisely modulate the function of target RNA with minimal off-target effects and can be rationally designed based on sequence data.

Complex Analysis of Micronutrient Levels and Bone Mineral Density in Patients with Different Types of Osteogenesis Imperfecta.

Osteogenesis imperfecta (OI) is a rare monogenic connective tissue disorder characterized by fragility of bones and recurrent fractures. In addition to the hereditary component, there are a number of factors that influence the course of the disease, the contribution of which is poorly understood, in particular the levels of micronutrients. A cross-sectional study was conducted involving 45 with OI and 45 healthy individuals.

[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].

Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.