Osteoporosis is a disease that impacts the elderly. Low estrogen is related to changes in DNA methylation and consequent alterations in gene expression, leading to a new direction in research related to the pathophysiology of osteoporosis. We constructed an Ovariectomized (OVX) mouse model in our study, and the mouse models had osteoporosis based on the phenotype and methylation levels in the mouse’s bone. Furthermore, the methylation level of the OVX mice was significantly changed compared to that of SHAM mice. Therefore, we performed genome-level analysis on the mouse model using transcriptome and Whole Genome Bisulfite Sequencing (WGBS) by combining the data of two omics and discovered that the changes in gene expression level caused by osteoporosis primarily focused on the decrease of bone and muscle development and the activation of the immune system. According to intersection analysis of methylation and transcriptome data, the differentially expressed genes and pathways are consistent with the differentially expressed methylation locations and regions. Further, the differentially expressed methylation sites were mainly concentrated in promoters, exons, and other critical functional regions of essential differentially expressed genes. This is also the primary cause of gene differential expression variations, indicating that estrogen deficiency might regulate gene expression by altering methylation modification, leading to osteoporosis. We demonstrated the clinical value of methylation modification research, and these findings would improve the current understanding of underlying molecular mechanisms of osteoporosis incidence and development and provide new ideas for early detection and treatment of osteoporosis.

Background: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. Methods: A total of 52 707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. Findings: The eXGBM model exhibited the best prediction performance, with an area under the curve (AUC) of 0.908 (95% CI: 0.881–0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/, based on the eXGBM model. The comparative testing revealed that the AI application’s predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P<0.001). Conclusions: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Myoblast is a kind of activated muscle stem cell. Its biological activities, such as proliferation, migration, differentiation, and fusion, play a crucial role in maintaining the integrity of the skeletal muscle system. These activities of myoblasts can be significantly influenced by the extracellular matrix. Collagen, being a principal constituent of the extracellular matrix, substantially influences these biological activities. In skeletal muscle, collagen I and III are two kinds of primary collagen types. Their influence on myoblasts and the difference between them remain ambiguous. The purpose of this study is to discover the influence of collagen I and III on biological function of myoblasts and compare their differences. We used C2C12 cell line and primary myoblasts to discover the effect of collagen I and III on proliferation, migration and differentiation of myoblasts and then performed the transcriptome sequencing and analysis. The results showed that both collagen I and III enhanced the proliferation of myoblasts, with no statistical difference between them. Similarly, collagen I and III enhanced the migration of myoblasts, with collagen I was more pronounced in Transwell assay. On the contrary, collagen I and III inhibited myoblasts differentiation, with collagen III was more pronounced at gene expression level. The transcriptome sequencing identified DEGs and enrichment analysis elucidated different terms between Type I and III collagen. Collectively, our research preliminarily elucidated the influence of collagen I and III on myoblasts and their difference and provided the preliminary experimental foundation for subsequent research.

The conundrum of wound healing has transformed into an imminent medical challenge. Presently, cell-free therapy centered around extracellular vesicles (EVs) has become a pivotal and promising research avenue. EVs generated from three-dimensional (3D) cell cultures have been previously established to possess enhanced tissue regeneration potential, although the underlying mechanisms remain elusive. In this study, we observed higher expression of annexin ANXA1 in 3D-cultured EVs. Remarkably, 3D-EVs with elevated ANXA1 expression demonstrated a more potent capacity to promote macrophage polarization from the M1 phenotype to the M2 phenotype. Concurrently, they exhibited superior abilities to enhance cell migration and tube formation, facilitating expedited wound healing in animal experiments. Conversely, the application of an ANXA1 inhibitor counteracted the positive effects of 3D-EVs. Taken together, our data validate that extracellular vesicles derived from 3D-cultured MSCs regulate macrophage polarization via ANXA1, thereby fostering wound healing.

Post-traumatic stress disorder (PTSD) is a debilitating condition that occurs following exposure to traumatic events. Current treatments, such as psychological debriefing and pharmacotherapy, often have limited efficacy and may result in unwanted side effects, making early intervention is a more desirable strategy. In this study, we investigated the efficacy of a single dose of pulsed (10 Hz) 810 nm laser-phototherapy (P-PT) as an early intervention for preventing PTSD-like comorbidities in rats induced by single inescapable electric foot shock following the single prolonged stress (SPS&S). As indicated by the results of the open filed test, elevated plus maze test, and contextual fear conditioning test, P-PT prevented the development of anxiety and freezing behaviors in rats exposed to the SPS&S. We also compared the effects of P-PT and continuous wave 810 nm laser-phototherapy (CW-PT) in preventing PTSD-like comorbidities in rats. The results revealed that P-PT was effective in preventing both freezing and anxiety behavior in stressed rats. In contrast, CW-PT only had a preventive effect on freezing behavior but not anxiety. Additionally, P-PT significantly reduced the c-fos expression in cingulate cortex area 1(Cg1) and infralimbic cortex (IL) of stressed rats, while CW-PT had no significant effects on c-fos expression. Taken together, our results demonstrate that P-PT is a highly effective strategy for preventing the occurrence of PTSD-like comorbidities in rats.

Background: Frailty is an aggregate expression of susceptibility to adverse health outcomes because of age- and disease-related deficits that accumulate across multiple domains. Previous studies have found the presence of preoperative frailty is associated with an increased risk of adverse outcomes. The number of older adults undergoing orthopedic surgery is rapidly increasing. However, there has been no evidence-based study on the relationship between frailty and outcomes in patients undergoing orthopedic surgery. Objective: The aims of this study are to investigate the association between frailty and outcomes in patients who underwent orthopedic surgery as well as patient factors associated with frailty. Methods: The methods to be used for this systematic review are reported according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) 2015 checklist. An extensive search will be conducted in PubMed, Embase, the Cochrane Library, and other mainstream databases. Any study where patients undergoing orthopedic surgery were assessed using a defined or validated measure of frailty and the association of frailty with patient factors and/or outcomes was reported will be included. A total of 2 researchers will independently screen articles for inclusion, with disagreements resolved by a third reviewer. We will perform a narrative synthesis of the factors associated with frailty, prevalence of frailty, effect of frailty on patient outcomes, and interventions for patients who are frail. A meta-analysis focusing on individual factors associated with frailty and the effect of frailty on patient outcomes will be performed, if applicable. The risk of bias will be evaluated. A subgroup analysis and sensitivity analysis will be performed. Results: Literature searches were conducted in September 2021 and the review is anticipated to be completed by the end of July 2022. Conclusions: This systematic review and meta-analysis will provide an overview of frailty and investigate the relationship between frailty and patient outcomes as well as the relationship between patient factors and frailty in patients undergoing orthopedic surgery. This study could potentially increase patients’ awareness of the outcomes associated with frailty, compel clinical specialties to further acknowledge the concept of frailty, and enhance the development of assessment instruments and tools for frailty. Trial Registration: PROSPERO CRD42020181846; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=181846