The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.

Atopic dermatitis and psoriasis are members of a family of inflammatory skin disorders. Cellular immune responses in skin tissues contribute to the development of these diseases. However, their underlying immune mechanisms remain to be fully elucidated. We developed a computational pipeline for analyzing the single-cell RNA-sequencing profiles of the Human Cell Atlas skin dataset to investigate the pathological mechanisms of skin diseases. First, we applied the maximum relevance criterion and the Boruta feature selection method to exclude irrelevant gene features from the single-cell gene expression profiles of inflammatory skin disease samples and healthy controls. The retained gene features were ranked by using the Monte Carlo feature selection method on the basis of their importance, and a feature list was compiled. This list was then introduced into the incremental feature selection method that combined the decision tree and random forest algorithms to extract important cell markers and thus build excellent classifiers and decision rules. These cell markers and their expression patterns have been analyzed and validated in recent studies and are potential therapeutic and diagnostic targets for skin diseases because their expression affects the pathogenesis of inflammatory skin diseases.

Background: The molecular mechanisms regulating the therapeutic effects of plant-based ingredients on the exercise-induced fatigue (EIF) remain unclear. The therapeutic effects of both tea polyphenols (TP) and fruit extracts of Lycium ruthenicum (LR) on mouse model of EIF were investigated.Methods: The variations in the fatigue-related biochemical factors, i.e., lactate dehydrogenase (LDH), superoxide dismutase (SOD), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), and interleukin-6 (IL-6), in mouse models of EIF treated with TP and LR were determined. The microRNAs involved in the therapeutic effects of TP and LR on the treatment of mice with EIF were identified using the next-generation sequencing technology.Results: Our results revealed that both TP and LR showed evident anti-inflammatory effect and reduced oxidative stress. In comparison with the control groups, the contents of LDH, TNF-α, IL-6, IL-1β, and IL-2 were significantly decreased and the contents of SOD were significantly increased in the experimental groups treated with either TP or LR. A total of 23 microRNAs (21 upregulated and 2 downregulated) identified for the first time by the high-throughput RNA sequencing were involved in the molecular response to EIF in mice treated with TP and LR. The regulatory functions of these microRNAs in the pathogenesis of EIF in mice were further explored based on Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses with a total of over 20,000–30,000 target genes annotated and 44 metabolic pathways enriched in the experimental groups based on GO and KEGG databases, respectively.Conclusion: Our study revealed the therapeutic effects of TP and LR and identified the microRNAs involved in the molecular mechanisms regulating the EIF in mice, providing strong experimental evidence to support further agricultural development of LR as well as the investigations and applications of TP and LR in the treatment of EIF in humans, including the professional athletes.

Background: γ-Poly-Glutamic Acid (γ-PGA) is a naturally occurring homo-polyamide produced by various strains of Bacillus. It is made from repeating units of L-glutamic acid, D-glutamic acid, or both connected through amide linkages between α-amino and γ-carboxylic acid groups. As a biopolymer substance, the attractive properties of γ-PGA are that it is water-soluble, biodegradable, biocompatible, non-toxic, non-immunogenic, and edible. Therefore, it can be used as a green and environmentally friendly biological material. Methods: The review concentrates on the reports revealing the functions and potential use of γ-PGA and its derivatives in medicine. Results & Discussion: γ-PGA is described to possess several properties that may be exploited in medicine. The biopolymer reportedly has been successfully applied not only as a metal chelator, drug carrier/ deliverer, and gene vector, but also used safely as a vaccine adjuvant, tissue engineering material, and contrast agent. Conclusion: γ-PGA could be potentially considered as a potential biomedical material in the field of medicine.

We investigated the functions of microRNAs in the therapeutic effects of noni (Morinda citrifolia L.) fruit juice on mouse models of acute gouty arthritis induced with monosodium urate (MSU). Compared with the model group (treated with MSU), mice in both the positive control group (treated with both MSU and colchicine) and noni fruit juice group (treated with MSU and noni fruit juice) showed a significantly decreased degree of paw swelling in 5 days, as well as the contents of two types of proinflammatory cytokines (i.e., NALP3 and TNF-α). Based on the next-generation sequencing technology, a total of 3896 microRNAs (234 known and 3662 novel) were identified in mice treated with noni fruit juice. A large amount of differentially expressed miRNAs were identified in the noni fruit juice group, suggesting the significant effects of noni fruit juice on the mice with acute gouty arthritis, while the different patterns of change in the numbers of both upregulated and downregulated miRNAs in both noni fruit juice and positive control groups indicated that the mice of acute gouty arthritis may be regulated by differential mechanisms between the treatments of noni fruit juice and colchicine. The target genes of microRNAs involved in the pathogenesis and pathology of acute gouty arthritis in mice were identified and further annotated by both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our results revealed the therapeutic effects of noni fruit juice on acute gouty arthritis in mice with a group of microRNAs involved in the pharmacological mechanisms of noni fruit juice, providing scientific evidence to support both the agricultural cultivation and pharmacological significance of noni plants.