Microenvironmental interference with intra-articular stem cell regeneration influences the onset and progression of arthritis.

Studies have indicated that the preservation of joint health and the facilitation of damage recovery are predominantly contingent upon the joint's microenvironment, including cell-cell interactions, the extracellular matrix's composition, and the existence of local growth factors. Mesenchymal stem cells (MSCs), which possess the capacity to self-renew and specialize in many directions, respond to cues from the microenvironment, and aid in the regeneration of bone and cartilage, are crucial to this process. Changes in the microenvironment (such as an increase in inflammatory mediators or the breakdown of the extracellular matrix) in the pathological context of arthritis might interfere with stem cell activation and reduce their ability to regenerate. This paper investigates the potential role of joint microenvironmental variables in promoting or inhibiting the development of arthritis by influencing stem cells' ability to regenerate. The present status of research on stem cell activity in the joint microenvironment is also outlined, and potential directions for developing new treatments for arthritis that make use of these intervention techniques to boost stem cell regenerative potential through altering the intra-articular environment are also investigated. This review's objectives are to investigate these processes, offer fresh perspectives, and offer a solid scientific foundation for the creation of arthritic treatment plans in the future.

Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.

Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not only significantly impair patients' quality of life but also complicate and raise the expense of therapy. It has been discovered in recent years that the pathophysiology of orthopedic disorders is significantly influenced by the microbiota. By employing machine learning and deep learning techniques to conduct a thorough analysis of the disease-causing microbiome, we can enhance our comprehension of the pathophysiology of many illnesses and expedite the creation of novel treatment approaches. Today's science is undergoing a revolution because to the introduction of machine learning and deep learning technologies, and the field of biomedical research is no exception. The genesis, course, and management of orthopedic disorders are significantly influenced by pathogenic microbes. Orthopedic infection diagnosis and treatment are made more difficult by the lengthy and imprecise nature of traditional microbial detection and characterization techniques. These cutting-edge analytical techniques are offering previously unheard-of insights into the intricate relationships between orthopedic health and pathogenic microbes, opening up previously unimaginable possibilities for illness diagnosis, treatment, and prevention. The goal of biomedical research has always been to improve diagnostic and treatment methods while also gaining a deeper knowledge of the processes behind the onset and development of disease. Although traditional biomedical research methodologies have demonstrated certain limits throughout time, they nevertheless rely heavily on experimental data and expertise. This is the area in which deep learning and machine learning approaches excel. The advancements in machine learning (ML) and deep learning (DL) methodologies have enabled us to examine vast quantities of data and unveil intricate connections between microorganisms and orthopedic disorders. The importance of ML and DL in detecting, categorizing, and forecasting harmful microorganisms in orthopedic infectious illnesses is reviewed in this work.

Research trends and hotspots in the immune microenvironment related to osteosarcoma and tumor cell aging: a bibliometric and visualization study.

Background: It is well known that cancers have a common feature that even if the environment is extremely poor in nutrients, they can still make good use of them to maintain viability as well as to produce new biomass, which is one of the reasons why tumor cells are powerfully less susceptible to senescence and death. The microenvironment has a profound impact on the senescence as well as the growth and development of tumor cells, and it is also the focus of scientists' research because it may even affect the discovery of the treatment and pathogenesis of cancer. And so the study of the microenvironment in the tumor cells is of great significance to the analysis of the tumor cells as well as to the impact of their senescence. Similarly, the microenvironment of osteosarcoma is also crucial for its impact, but to our knowledge, there is no bibliometric study that systematically analyzes and describes the trends and future hotspots in this field of research as we do, and we are going to fill this gap in this study. Methods: We searched the Web Science Core Collection 2010-2023 in WOS on August 1, 2023. Based on the criteria needed for the search, we retained articles that matched the topic, excluded studies other than articles and reviews, and selected only studies whose language was English. We performed an intuitive visualization and bibliometric approach to analyze the research content in this field and a systematic visualization of global trends and hotspots in the research of osteosarcoma and the microenvironment, for which we used multiple specialized For this purpose, we used several specialized software packages, such as VOSviewer and the Bibliometrix package for R software. Because research in this area of osteosarcoma and the microenvironment has begun to gain popularity in the last 10 years or so, and is a very novel piece of research, there were almost no studies in this area prior to 2010 and they were not very informative, and in the end, we chose to look at studies from after 2010. Results: Based on the criteria needed for the search, resulting in a final selection of 821 articles. In the research area related to osteosarcoma and microenvironment, we found that China in Asia and the United States in North America and Italy in Europe were the three countries or regions with the highest number of published articles. In addition, the institution that published the most research in this area was Shanghai Jiao Tong University. In terms of publications in the field of osteosarcoma and microenvironmental research, Baldini, Heymann, and Avnet are among the top 3 authors. The terms "cancer", "cells" and "expression" are found to be more commonly employed. Conclusion: Using a variety of highly specialized software, we have undertaken a visual and bibliometric study of the current state of research and potential future hotspots in the field of osteosarcoma and microenvironment research. The microenvironment has a profound impact on the senescence and growth and development of cells in tumors, including osteosarcoma, and may even influence the discovery of cancer treatment and pathogenesis, and is also a hotspot and focus that scientists have begun to gradually study in recent years. This analysis and visualization will help guide future research in the field.

Establishment and validation of a nomogram for progression to diabetic foot ulcers in elderly diabetic patients.

Background: Many diabetic patients develop and progress to diabetic foot ulcers, which seriously affect health and quality of life and cause great economic and psychological stress, especially in elderly diabetic patients who often have various underlying diseases, and the consequences of their progression to diabetic foot ulcers are more serious and seriously affect elderly patients in surgery. Therefore, it is particularly important to analyze the influencing factors related to the progression of elderly diabetic patients to diabetic foot, and the column line graph prediction model is drawn based on regression analysis to derive the influencing factors of the progression of elderly diabetic patients to diabetic foot, and the total score derived from the combination of various influencing factors can visually calculate the probability of the progression of elderly diabetic patients to diabetic foot. Objective: The influencing factors of progression deterioration to diabetic foot in elderly diabetic patients based on LASSO regression analysis and logistics regression analysis, and the column line graph prediction model was established by statistically significant risk factors. Methods: The clinical data of elderly diabetic patients aged 60 years or older in the orthopedic ward and endocrine ward of the Third Hospital of Shanxi Medical University from 2015-01-01 to 2021-12-31 were retrospectively analyzed and divided into a modeling population (211) and an internal validation population (88) according to the random assignment principle. Firstly, LASSO regression analysis was performed based on the modeling population to screen out the independent influencing factors for progression to diabetic foot in elderly diabetic patients; Logistics univariate and multifactor regressions were performed by the screened influencing factors, and then column line graph prediction models for progression to diabetic foot in elderly diabetic patients were made by these influencing factors, using ROC (subject working characteristic curve) and AUC (their area under the curve), C-index validation, and calibration curve to initially evaluate the model discrimination and calibration. Model validation was performed by the internal validation set, and the ROC curve, C-index and calibration curve were used to further evaluate the column line graph model performance. Finally, using DCA (decision curve analysis), we observed whether the model could be used better in clinical settings. Results and conclusions: (1) LASSO (Least absolute shrinkage and selection operator) regression analysis yielded a more significant significance on risk factors for progression to diabetic foot in elderly diabetic patients, such as age, presence of peripheral neuropathy, history of smoking, duration of disease, serum lactate dehydrogenase, and high-density cholesterol; (2) Based on the influencing factors and existing theories, a column line graph prediction model for progression to diabetic foot in elderly diabetic patients was constructed. The working characteristic curves of subjects in the training group and their area under the curve (area under the curve = 0.840) were also analyzed simultaneously with the working characteristic curves of subjects in the external validation population and their area under the curve (area under the curve = 0.934), which finally showed that the model was effective in predicting column line graphs; (iii) the C-index in the modeled cohort was 0.840 (95%CI: 0.779-0.901) and the C-index in the validation cohort was 0.934 (95%CI: 0.887-0.981), indicating that the model had good predictive accuracy; the calibration curve fit was good; (iv) the results of the decision curve analysis showed that the model would have good results in clinical use; (v) it indicated that the established predictive model for predicting progression to diabetic foot in elderly diabetic patients had good test efficacy and helped clinically screen the possibility of progression to diabetic foot in elderly diabetic patients and give personalized interventions to different patients in time.

Studies related to osteosarcoma and metabolism from 1990 to 2022: A visual analysis and bibliometric study.

Background: Osteosarcoma is the most common primary bone tumor, its high incidence of metastasis and poor prognosis have led to a great deal of concern for osteosarcoma. In many cancer types, metabolic processes are important for tumor growth progression, so interfering with the metabolic processes of osteosarcoma may be a therapeutic option to stall osteosarcoma progression. A key mechanism of how metabolic processes contribute to the growth and survival of various cancers, including osteosarcoma, is their ability to support tumor cell metabolism. Research related to this field is a direction of great importance and potential. However, to our knowledge, no bibliometric studies related to this field have been published, and we will fill this research gap. Methods: Publications were retrieved on January 1, 2023 from the 1990-2022 Science Citation Index of the Web of Science Core Collection. The Bibliometrix package in R software, VOSviewer and CiteSpace software were used to analyze our research directions and to visualize global trends and hotspots in osteosarcoma and metabolism related research. Results: Based on the search strategy, 833 articles were finally filtered. In this area of research related to osteosarcoma metabolism, we found that China, the United States and Japan are the top 3 countries in terms of number of articles published, and the journals and institutions that have published the most research in this area are Journal of bone and mineral research, Shanghai Jiao Tong University. In addition, Baldini, Nicola, Reddy, Gs and Avnet, Sofia are the top three authors in terms of number of articles published in studies related to this field. The most popular keywords related to the field in the last 30 years are "metabolism" and "expression", which will guide the possible future directions of the field. Conclusion: We used Bibliometrix, VOSviewer, and Citespace to visualize and bibliometrically analyze the current status and possible future hotspots of research in the field of osteosarcoma metabolism. Possible future hotspots in this field may focus on the related terms "metabolism", "expression", and "migraation".

Establishment and validation of systematic prognostic nomograms in patients over 60 years of age with osteosarcoma: A multicenter external verification study.

BACKGROUND: The aim of this study was to develop and validate systematic nomograms to predict cancer specific survival (CSS) and overall survival (OS) in osteosarcoma patients aged over 60 years. METHODS: We used data from the Surveillance, Epidemiology, and End Results (SEER) database and identified 982 patients with osteosarcoma over 60 years of age diagnosed between 2004 and 2015. Overall, 306 patients met the requirements for the training group. Next, we enrolled 56 patients who met the study requirements from multiple medical centers as the external validation group to validate and analyze our model. We collected all available variables and finally selected eight that were statistically associated with CSS and OS through Cox regression analysis. Integrating the identified variables, we constructed 3- and 5-year OS and CSS nomograms, respectively, which were further evaluated by calculating the C-index. A calibration curve was used to evaluate the accuracy of the model. Receiver operating characteristic (ROC) curves measured the predictive capacity of the nomograms. The Kaplan-Meier analysis was used for all patient-based variables to explore the influence of various factors on patient survival. Finally, a decision curve analysis (DCA) curve was used to analyze whether our model would be suitable for application in clinical practice. RESULTS: Cox regression analysis of clinical variables identified age, sex, marital status, tumor grade, tumor laterality, tumor size, M-stage, and surgical treatment as prognostic factors. Nomograms showed good predictive capacity for OS and CSS. We calculated that the C-index of the OS nomogram of the training population was 0.827 (95% CI 0.778-0.876), while that of the CSS nomogram was 0.722 (95% CI 0.665-0.779). The C-index of the OS nomogram evaluated on the external validation population was 0.716 (95% CI 0.575-0.857), while that of the CSS nomogram was 0.642 (95% CI 0.50-0.788). Furthermore, the calibration curve of our prediction models indicated the nomograms could accurately predict patient outcome. CONCLUSIONS: The constructed nomogram is a useful tool for accurately predicting OS and CSS at 3 and 5 years for patients over 60 years of age with osteosarcoma and can assist clinicians in making appropriate decisions in practice.

A nomogram based on radiological features of MRI for predicting the risk of severe pain in patients with osteoarthritis of the knee.

Methods: This study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort (n = 150). An internal validation cohort (n = 64) was applied to validate the model. Results: Eight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren-Lawrance (K-L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain. Conclusions: Based on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839-0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722-0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and <0.86 threshold probability intervals. These findings demonstrate that the nomogram can predict patient prognosis and guide personalized treatment.

Patient satisfaction after total hip arthroplasty: Influencing factors.

It is reported that the dissatisfaction rate after primary total hip arthroplasty (THA) is between 7% and 20%. Patient satisfaction has already become a public health problem that puzzles the world, and it is a problem to be solved that cannot be ignored in the development of global public health. The purpose of this paper is to conduct a narrative review of the literature to answer the following questions: what are the main factors leading to high patient satisfaction or dissatisfaction after THA? The literature on patient satisfaction after THA was reviewed. As far as we know, there is no such detailed and timely overview of THA satisfaction as this article, and the purpose articles we use search engines to search are all RCT (Randomized Controlled Trial) type works, excluding cross-sectional studies and other experiments with low evidence level. Hence, the quality of this article is high. The search engines used are MEDLINE (PubMed) and EMBASE. The keywords used are "THA" and "satisfaction." The main preoperative, perioperative, and postoperative factors that affect patient satisfaction are summarized in detail below.

Endocrine regulation and metabolic mechanisms of osteopontin in the development and progression of osteosarcoma, metastasis and prognosis.

Osteosarcoma is the most common type of malignant bone tumor, occurring in adolescents and patients over 60. It has a bimodal onset and a poor prognosis, and its development has not yet been fully explained. Osteopontin (OPN) is a high protein consisting of 314 amino acid residues with a negative charge and is involved in many biological activities. OPN is not only an essential part of the regulation of the nervous system and endocrine metabolism of skeletal cells. Still, it is also involved in several other important biological activities, such as the division, transformation, and proliferation of skeletal cells and their associated cells, such as bone tumor cells, including bone marrow mesenchymal stem cells, hematopoietic stem cells, osteoblasts, and osteoclasts. Osteoblasts and osteocytes. Recent studies have shown a strong correlation between OPN and the development and progression of many skeletal diseases, such as osteosarcoma and rheumatoid arthritis. This review aims to understand the mechanisms and advances in the role of OPN as a factor in the development, progression, metastasis, and prognosis of osteosarcoma in an attempt to provide a comprehensive summary of the mechanisms by which OPN regulates osteosarcoma progression and in the hope of contributing to the advancement of osteosarcoma research and clinical treatment.