The immune cells in modulating osteoclast formation and bone metabolism.

Osteoclasts are pivotal in regulating bone metabolism, with immune cells significantly influencing both physiological and pathological processes by modulating osteoclast functions. This is particularly evident in conditions of inflammatory bone resorption, such as rheumatoid arthritis and periodontitis. This review summarizes and comprehensively analyzes the research progress on the regulation of osteoclast formation by immune cells, aiming to unveil the underlying mechanisms and pathways through which diseases, such as rheumatoid arthritis and periodontitis, impact bone metabolism.

Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography-feasibility and accuracy.

Objective: Coronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions. Methods: We evaluated 137 CAD patients with WMH who underwent coronary computed tomography angiography (CCTA) and two magnetic resonance imaging (MRI) scans from March 2018 to February 2023. Patients were categorized into progressive (n = 66) and nonprogressive groups (n = 71) by the change in WMH volume from the first to the second MRI. We collected demographic, clinical, and imaging data for analysis. Independent risk factors for WMH progression were identified using logistic regression. Three models predicting WMH progression were developed and assessed. Finally, patients were divided into groups based on their total CAC score (0 to <100, 100 to 400, and > 400) to compare their WMH changes in nine brain regions. Results: Alcohol abuse, maximum pericoronary fat attenuation index (pFAI), CT-fractional flow reserve (CT-FFR), and CAC risk grade independently predicted WMH progression (p < 0.05). The logistic regression model with all four variables performed best (training: AUC = 0.878, 95% CI: 0.790, 0.938; validation: AUC = 0.845, 95% CI: 0.734, 0.953). An increased CAC risk grade came with significantly higher WMH volume in the total brain, corpus callosum, and frontal, parietal and occipital lobes (p < 0.05). Conclusion: This study demonstrated the application of the CCTA-derived CAC score to predict WMH progression in elderly people (≥60 years) with CAD.

Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.

PURPOSE: To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients. METHODS: Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models. RESULTS: High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p < 0.05), as well as obtained good predictive performance in the test set (AUC = 0.837). CONCLUSION: Among the five machine learning models, the RF-based radiomics model was the most stable and performed excellently in identifying high histological grade of HCC.

Multi-omics analysis of an in vitro photoaging model and protective effect of umbilical cord mesenchymal stem cell-conditioned medium.

BACKGROUND: Skin ageing caused by long-term ultraviolet (UV) irradiation is a complex biological process that involves multiple signalling pathways. Stem cell-conditioned media is believed to have anti-ageing effects on the skin. The purpose of this study was to explore the biological effects of UVB irradiation and anti-photoaging effects of human umbilical cord mesenchymal stem cell-conditioned medium (hUC-MSC-CM) on HaCaT cells using multi-omics analysis with a novel cellular photoaging model. METHODS: A cellular model of photoaging was constructed by irradiating serum-starved HaCaT cells with 20 mJ/cm2 UVB. Transcriptomics and proteomics analyses were used to explore the biological effects of UVB irradiation on photoaged HaCaT cells. Changes in cell proliferation, apoptosis, and migration, the cell cycle, and expression of senescence genes and proteins were measured to assess the protective effects of hUC-MSC-CM in the cellular photoaging model. RESULTS: The results of the multi-omics analysis revealed that UVB irradiation affected various biological functions of cells, including cell proliferation and the cell cycle, and induced a senescence-associated secretory phenotype. hUC-MSC-CM treatment reduced cell apoptosis, inhibited G1 phase arrest in the cell cycle, reduced the production of reactive oxygen species, and promoted cell motility. The qRT-PCR results indicated that MYC, IL-8, FGF-1, and EREG were key genes involved in the anti-photoaging effects of hUC-MSC-CM. The western blotting results demonstrated that C-FOS, C-JUN, TGFß, p53, FGF-1, and cyclin A2 were key proteins involved in the anti-photoaging effects of hUC-MSC-CM. CONCLUSION: Serum-starved HaCaT cells irradiated with 20 mJ/cm2 UVB were used to generate an innovative cellular photoaging model, and hUC-MSC-CM demonstrates potential as an anti-photoaging treatment for skin.

Sphingomyelin synthase 2 promotes H2O2-induced endothelial dysfunction by activating the Wnt/ß-catenin signaling pathway.

Atherosclerosis (AS) is the primary cause of various cardiovascular and cerebrovascular diseases and has high morbidity and mortality rates. Oxidative stress­induced endothelial cells (ECs) dysfunction is the pathological basis of AS. In addition, sphingomyelin (SM) and the Wnt/ß­catenin signaling pathway are considered to be closely associated with AS; however, the specific mechanism is not clear. Therefore, the present study investigated whether SM may induce ECs dysfunction through the Wnt/ß­catenin signaling pathway. Firstly, a sphingomyelin synthase 2 (SMS2) overexpression cell model was constructed. It was identified that the expression of SMS2 was increased when ECs were treated with H2O2. In addition, these results demonstrated that SMS2 overexpression promoted apoptosis and macrophage adhesion of H2O2­induced ECs, thereby increasing the expression of ß­catenin. Furthermore, SMS activity was inhibited with Dy105, combined with simultaneous treatment with LiCl or H2O2. This additionally confirmed that Dy105 significantly inhibited SMS activity and decreased the level of ECs dysfunction and ß­catenin content; however, LiCl served a key role in activating the Wnt/ß­catenin signaling pathway to promote ECs dysfunction. Collectively, these results suggested that SMS2 overexpression may promote ECs dysfunction by activating the Wnt/ß­catenin signaling pathway, while Dy105 may inhibit the evolution of oxidative stress­induced dysfunction.