Selenium Deficiency Can Promote the Expression of VEGF and Inflammatory Factors in Cartilage Differentiation and Mediates Cartilage Injury.

Selenium plays a crucial role as a micronutrient, primarily exerting its biological functions through selenoproteins. It has been established that selenium deficiency adversely impacts cartilage development, leading to alterations in chondrocyte function. In regions with low selenium intake, endemic osteochondrosis has been documented, characterized by compromised growth plate and articular cartilage formation. Vascular endothelial growth factor (VEGF) stands out as a pivotal angiogenic factor, with elevated levels contributing significantly to vascular invasion into chondrocytes. This VEGF-mediated invasion serves as a key signal, prompting morphological changes in the growth plate and initiating cartilage remodeling. In animal models, the selenium deficiency group exhibited heightened levels of the cartilage damage marker matrix metalloproteinases 13 (MMP13). This resulted in articular cartilage degeneration, accompanied by a substantial increase in VEGF expression within the growth plate and articular cartilage, as compared to the normal group. In a chondrogenic progenitor cell (CPC) differentiation model, insufficient selenium induced chondrocyte damage and upregulated inflammatory factors such as inducible NO synthase (iNOS) and cyclooxygenase-2 (COX2). The selenium-deficient groups showed elevated expressions of VEGF, VEGFR2, MMP13, Collagen X, and Angiopoietin 1, accelerating the degradation of the extracellular matrix (ECM), which further promoted the development of cartilage-related diseases. Taken together, these findings provide novel insights for a better understanding of the role of low selenium in cartilage degeneration and angiogenesis. They shed light on the intricate influence of low selenium levels on the development of articular cartilage, emphasizing the interconnected pathways and processes involved.

Multi-omics analysis of MRPL-13 as a tumor-promoting marker from pan-cancer to lung adenocarcinoma.

BACKGROUND: As a member of the mitochondrial ribosomal protein family, mitochondrial ribosomal protein L13 (MRPL13) is responsible for synthesizing mitochondrial proteins in cells. Several studies have indicated that MRPL13 is associated with the proliferation cycle, migration ability, apoptosis and autophagy of cancer cells. However, a thorough examination of MRPL13 across cancers remains uncertain. Therefore, we tried to clarify the relationship between MRPL13 and pan-cancer, and verified it in lung adenocarcinoma by various methods. Finally, our research is expected to reveal new targets for pan-cancer treatment and improve the prognosis of cancer patients. METHODS: Using bioinformatics tools, we quantified the differential expression of MRPL13 between cancer tissues and corresponding or noncorresponding normal tissues across cancers. We also analyzed the relationships between MRPL13 expression levels and several factors, including diagnosis, prognosis, mutation, functional signaling pathways, immune infiltration, RNA modification, and the relationship with cuproptosis-related genes. Furthermore, we studied the relationship between the expression level of MRPL13 across cancers and the change in cancer functional status through single-cell data. In addition, quantitative experiments (PCR and Western blot) proved that the expression of MRPL13 was significantly different between LUAD and control samples. Finally, the effect of knocking out MRPL13 on cancer cells was compared by gene silencing experiments. In summary, we used a combination of bioinformatics and experimental applications to study the potential roles of MRPL13 in cancer. RESULTS: After conducting a multidimensional analysis, we found that the application of MRPL13 multigroup analysis can effectively improve the diagnostic efficiency of various cancers and predict the prognosis of cancer. Moreover, MRPL13 in pan-cancer is related to the cancer immune infiltration pattern, methylation level and cuproptosis-related genes. Furthermore, single-cell data analysis showed that the modules of metastasis, EMT, cell cycle, DNA repair, invasion, DNA damage and proliferation were positively correlated with the expression of MRPL13 in LUAD (Lung adenocarcinoma), while the modules of hypoxia and inflammation were negatively correlated. Moreover, through quantitative experiments, we observed higher expression of MRPL13 in cancer tissues at the RNA or protein level. Knockdown of MRPL13 in LUAD led to decreased cancer cell survival, delayed tumor division and migration, reduced invasion, and increased cancer cell apoptosis. CONCLUSIONS: Our study demonstrates the potential of using MRPL13 as a molecular biomarker for diagnosing and suggesting the prognosis of certain malignant tumors. Furthermore, our research shows that MRPL13 may be an effective therapeutic target for lung adenocarcinoma.

The Protective Effect of Selenium Nanoparticles in Osteoarthritis: In vitro and in vivo Studies.

Introduction: Osteoarthritis (OA) is a common chronic joint disease characterized by articular cartilage degeneration. OA usually manifests as joint pain, limited mobility, and joint effusion. Currently, the primary OA treatment is non-steroidal anti-inflammatory drugs (NSAIDs). Although they can alleviate the disease's clinical symptoms and signs, the drugs have some side effects. Selenium nanoparticles (SeNPs) may be an alternative to relieve OA symptoms. Materials and Results: We confirmed the anti-inflammatory effect of selenium nanoparticles (SeNPs) in vitro and in vivo experiments for OA disease in this study. In vitro experiments, we found that SeNPs could significantly reduce the expression of nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), the major inflammatory factors, and had significant anti-inflammatory and anti-arthritic effects. SeNPs can inhibit reactive oxygen species (ROS) production and increased glutathione peroxidase (GPx) activity in interleukin-1beta (IL-1ß)-stimulated cells. Additionally, SeNPs down-regulated matrix metalloproteinase-13 (MMP-13) and thrombospondin motifs 5 (ADAMTS-5) expressions, while up-regulated type II collagen (COL-2) and aggrecan (ACAN) expressions stimulated by IL-1ß. The findings also indicated that SeNPs may exert their effects through suppressing the NF-κB p65 and p38/MAPK pathways. In vivo experiments, the prevention of OA development brought on by SeNPs was demonstrated using a DMM model. Discussion: Our results suggest that SeNPs may be a potential anti-inflammatory agent for treating OA.

Association of smoking and osteoarthritis in US (NHANES 1999-2018).

Little is currently known about the effect of smoking on osteoarthritis (OA). This study aimed to investigate the relationship between smoking and OA in the United States (US) general population. Cross-sectional study. Level of evidence, 3. 40,201 eligible participants from the National Health and Nutrition Examination Survey 1999-2018 were included and divided into OA and non-arthritis groups. Participants demographics and characteristics were compared between the two groups. Then the participants were divided into non-smokers, former smokers, and current smokers based on their smoking status, also demographics and characteristics among the three groups were compared. Multivariable logistic regression was used to determine the relationship between smoking and OA. The current and former smoking rate in the OA group (53.0%) was significantly higher than that in the non-arthritis group (42.5%; p < 0.001). Multivariable regression analysis including body mass index (BMI), age, sex, race, education level, hypertension, diabetes, asthma and cardiovascular disease showed that smoking was an association for OA. This large national study highlights a positive association between smoking and OA prevalence in the general US population. It is necessary to further study the relationship between smoking and OA in order to determine the specific mechanism of smoking on OA.

Cerium Oxide Nanoparticles Promote Osteoplastic Precursor Differentiation by Activating the Wnt Pathway.

Osteoplastic precursors are critical for fracture repair and bone homeostasis maintenance. Cerium oxide nanoparticles (CeO2 NPs) can promote the osteogenic differentiation of mesenchymal stem cells and secrete vascular endothelial growth factors. However, little is known about its role in precursor osteoblasts; therefore, we further investigated the effect and mechanism of CeO2 NPs in precursor osteoblasts. Cell counting kit-8 analysis was utilized to detect the toxicity of CeO2 NPs on MC3T3-E1 mouse osteogenic precursor cells. Then, alizarin red S staining was employed to assess the degree of extracellular matrix mineralization, and quantitative real-time polymerase chain reaction analysis was performed to measure the levels of osteogenesis-related genes. To identify differentially expressed genes, mRNA-sequencing was performed. Subsequently, GO and KEGG analyses were deployed to identify the major downstream pathways, whereas Western blot was used for verification. CeO2 NPs significantly enhanced the ability of MC3T3-E1 precursor osteoblasts to enhance matrix mineralization and increased the expression of osteogenic genes such as runt-related transcription factor 2, collagen Iα1, and osteocalcin. Pathway analysis revealed that CeO2 NPs enhanced the nuclear translocation of ß-catenin and activated the Wnt pathway by promoting family with sequence similarity 53 member B/simplet expression, while Western blot analysis indicated the same results. After using a Wnt pathway inhibitor (KYA1797K), the simulative effect of CeO2 NPs was abolished. This study revealed that CeO2 NPs promoted MC3T3-E1 precursor osteoblast differentiation by activating the Wnt pathway.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.

Background: Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well. Methods: The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Results: Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance. Conclusion: Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Advanced injectable hydrogels for cartilage tissue engineering.

The rapid development of tissue engineering makes it an effective strategy for repairing cartilage defects. The significant advantages of injectable hydrogels for cartilage injury include the properties of natural extracellular matrix (ECM), good biocompatibility, and strong plasticity to adapt to irregular cartilage defect surfaces. These inherent properties make injectable hydrogels a promising tool for cartilage tissue engineering. This paper reviews the research progress on advanced injectable hydrogels. The cross-linking method and structure of injectable hydrogels are thoroughly discussed. Furthermore, polymers, cells, and stimulators commonly used in the preparation of injectable hydrogels are thoroughly reviewed. Finally, we summarize the research progress of the latest advanced hydrogels for cartilage repair and the future challenges for injectable hydrogels.

Osteoporosis, fracture and survival: Application of machine learning in breast cancer prediction models.

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.

Advanced Nanofiber-Based Scaffolds for Achilles Tendon Regenerative Engineering.

The Achilles tendon (AT) is responsible for running, jumping, and standing. The AT injuries are very common in the population. In the adult population (21-60 years), the incidence of AT injuries is approximately 2.35 per 1,000 people. It negatively impacts people's quality of life and increases the medical burden. Due to its low cellularity and vascular deficiency, AT has a poor healing ability. Therefore, AT injury healing has attracted a lot of attention from researchers. Current AT injury treatment options cannot effectively restore the mechanical structure and function of AT, which promotes the development of AT regenerative tissue engineering. Various nanofiber-based scaffolds are currently being explored due to their structural similarity to natural tendon and their ability to promote tissue regeneration. This review discusses current methods of AT regeneration, recent advances in the fabrication and enhancement of nanofiber-based scaffolds, and the development and use of multiscale nanofiber-based scaffolds for AT regeneration.

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study.

Background: Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM. Methods: PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. Results: A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. Conclusion: We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

The 100 most cited articles in lateral epicondylitis research: A bibliometric analysis.

Introduction: Lateral epicondylitis is a significant clinical problem in orthopaedics. There have been numerous articles written about this. Bibliometric analysis is critical for determining a field's most influential study. We attempt to identify and analyze the top 100 citations in lateral epicondylitis research. Materials and methods: On December 31, 2021, an electronic search was conducted in the Web of Science Core Collection and the Scopus search engine with no restrictions on publication years, language, or study design. We reviewed each article's title and abstract until the top 100 were documented and evaluated in various ways. Results: Between 1979 and 2015, the 100 most cited articles were published in 49 journals. The total number of citations ranged from 75 to 508 (mean ± SD, 145.5 ± 90.9), with citation densities ranging from 2.2 to 37.6 citations per year (mean ± SD, 8.7 ± 6.5). The United States is the most productive country, and the 2000s witnessed a surge in lateral epicondylitis research. The year of publication had a moderately positive correlation with citation density. Conclusion: Our findings offer readers a fresh perspective on historical development hotspot areas of lateral epicondylitis research. Disease progression, diagnosis, and management have always been topics of discussion in articles. PRP-based biological therapy has emerged as a promising area for future research.

The application prospect of metal/metal oxide nanoparticles in the treatment of osteoarthritis.

The current understanding of osteoarthritis is developing from a mechanical disease caused by cartilage wear to a complex biological response involving inflammation, oxidative stress and other aspects. Nanoparticles are widely used in drug delivery due to its good stability in vivo and cell uptake efficiency. In addition to the above advantages, metal/metal oxide NPs, such as cerium oxide and manganese dioxide, can also simulate the activity of antioxidant enzymes and catalyze the degradation of superoxide anions and hydrogen peroxide. Degrading of metal/metal oxide nanoparticles releases metal ions, which may slow down the progression of osteoarthritis by inhibiting inflammation, promoting cartilage repair and inhibiting cartilage ossification. In present review, we focused on recent research works concerning osteoarthritis treating with metal/metal oxide nanoparticles, and introduced some potential nanoparticles that may have therapeutic effects.

Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis.

Background: The overall survival (OS) of pancreatic cancer (PC) patients with bone metastasis (BM) is extremely low, and it is pretty hard to treat bone metastasis. However, there are currently no effective nomograms to predict the diagnosis and prognosis of pancreatic cancer with bone metastasis (PCBM). Therefore, it is of great significance to establish effective predictive models to guide clinical practice. Methods: We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2016. The independent risk factors of PCBM were identified from univariable and multivariable logistic regression analyses, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors affecting the prognosis of PCBM. In addition, two nomograms were constructed to predict the risk and prognosis of PCBM. We used the area under the curve (AUC), C-index and calibration curve to determine the predictive accuracy and discriminability of nomograms. The decision curve analysis (DCA) and Kaplan-Meier(K-M) survival curves were employed to further confirm the clinical effectiveness of the nomogram. Results: Multivariable logistic regression analyses revealed that risk factors of PCBM included age, primary site, histological subtype, N stage, radiotherapy, surgery, brain metastasis, lung metastasis, and liver metastasis. Using Cox regression analyses, we found that independent prognostic factors of PCBM were age, race, grade, histological subtype, surgery, chemotherapy, and lung metastasis. We utilized nomograms to visually express data analysis results. The C-index of training cohort was 0.795 (95%CI: 0.758-0.832), whereas that of internal validation cohort was 0.800 (95%CI: 0.739-0.862), and the external validation cohort was 0.787 (95%CI: 0.746-0.828). Based on AUC of receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance. Conclusion: Nomogram is sufficiently accurate to predict the risk and prognostic factors of PCBM, allowing for individualized clinical decisions for future clinical work.