PHF21A expression as a biomarker of hepatocellular carcinoma progression and prognosis.

Previous studies have shown that PHF21A is associated with the initiation and progression of various tumors. However, its role in hepatocellular carcinoma (HCC) is still unclear. Thus, this study aimed to determine the expression and clinical significance of PHF21A in HCC. PHF21A expression in 201 liver cancer samples and 129 adjacent normal tissues was detected by immunohistochemistry. The correlation between PHF21A expression and the clinicopathological features and prognosis of HCC was verified in 70 other liver tissue microarray samples. The relationship between PHF21A expression and HCC immune cell infiltration was explored via the Tumor Immune Estimation Resource (TIMER). The mechanism underlying the effect of PHF21A on HCC progression was analyzed by gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. Immunohistochemical staining showed that PHF21A expression in HCC tissue was significantly lower than that in adjacent nontumor liver tissue and was associated with patient sex, tumor size, metastasis, and Edmondson grade (p<0.05). Kaplan-Meier analysis demonstrated that low PHF21A expression was associated with a poor prognosis, and Cox regression analysis showed that PHF21A was an independent predictor of prognosis. TIMER analysis showed that PHF21A is positively correlated with tumor immune cell infiltration levels. Functional annotation indicated that PHF21A is involved in important pathways, including transcriptional deregulation pathways in cancer. Finally, in vitro experiments confirmed the low expression of PHF21A in HCC cells. PHF21A affects the progression and prognosis of HCC, suggesting that PHF21A may play an important role in monitoring and preventing the development of HCC.

Unveiling hotspots and trends in hip arthroscopy research: A bibliometric and visualized analysis (1900-2022).

Over the past 10 years, hip arthroscopy has been increasingly employed to effectively diagnose and safely treat a range of hip pathologies. With research related to hip arthroscopy continually expanding, the number of articles connected with hip arthroscopy has also consistently grown. We aimed to investigate trends and hotspots in hip arthroscopy-related research, and analyze the top 100 most-cited articles on hip arthroscopy. We searched for ("hip arthroscopy") AND ("article" OR "review") AND "English" in the Web of Science database from 1900 to 2022, which was used to obtain all publications relating to hip arthroscopy. Distribution of country, affiliated institution, journal, authors, citation frequency and keywords were analyzed using VOSviewer. A total of 1094 articles were selected from the Web of Science Core Collection (WoSCC) from 1900 to 2022. The number of publications concerning hip arthroscopy displayed an ascending trend over time. Among the countries, the United States emerged as the largest contributor to the number of articles. The highest prolific institution was American Hip Institute. Among the journals, the highest-ranking journal was "Arthroscopy-the Journal of Arthroscopic and Related Surgery," with 8316 citation counts and 262 articles. The area of greatest research interest was diagnosis and therapy in the field. The scientific articles on the subject of hip arthroscopy have risen continuously in recent years. The United States was the most influential country and made the most significant contributions to this field globally. We identified the research direction and trend for the first time and provided the most recent bibliometric analysis on hip arthroscopy, which may assist researchers in conducting studies on hip arthroscopy.

Platelet membrane-coated oncolytic vaccinia virus with indocyanine green for the second near-infrared imaging guided multi-modal therapy of colorectal cancer.

Colorectal cancer (CRC) is a prevalent malignancy with insidious onset and diagnostic challenges, highlighting the need for therapeutic approaches to enhance theranostic outcomes. In this study, we elucidated the unique temperature-resistant properties of the oncolytic vaccinia virus (OVV), which can synergistically target tumors under photothermal conditions. To capitalize on this characteristic, we harnessed the potential of the OVV by surface-loading it with indocyanine green (ICG) and encapsulating it within a platelet membrane (PLTM), resulting in the creation of PLTM-ICG-OVV (PIOVV). This complex seamlessly integrates virotherapy, photodynamic therapy (PDT), and photothermal therapy (PTT). The morphology, size, dispersion stability, optical properties, and cellular uptake of PIOVV were evaluated using transmission electron microscopy (TEM). In vitro and in vivo experiments revealed specificity of PIOVV for cancer cells; it effectively induced apoptosis and suppressed CT26 cell proliferation. In mouse models, PIOVV exhibits enhanced fluorescence at tumor sites, accompanied by prolonged blood circulation. Under 808 nm laser irradiation, PIOVV significantly inhibited tumor growth. This strategy holds the potential for advancing phototherapy, oncolytic virology, drug delivery, and tumor-specific targeting, particularly in the context of CRC theranostics.

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.

LINC00482 sponged miR-2467-3p to promote bone metastasis of prostate cancer through activating Wnt/ß-catenin signaling pathway.

This study was designed to investigate the biological functions of LINC00482 in prostate cancer (PCa) with bone metastasis. TCGA dataset of PCa was applied for LINC00482 expression analysis and real time PCR was used to verify the expression level of LINC00482 in PCa tissues as well as PCa bone metastatic tissues. To detect the biological functions of LINC00482 in vitro, various assays were used including CCK-8, EdU, colony formation and transwell assays. The biological functions of LINC00482 were also identified in vivo by inoculating PCa cells into the left cardiac ventricle of mice, followed by evaluating the osteolytic lesions and osteolytic score. In addition, Starbase and Lncbase databases were applied for predicting the potential target miRNA of LINC00482, while TargetScan and Starbase databases were used for predicting the potential target of miRNA. The luciferase reporter assay was utilized to determine the interactions among these molecules and western blotting was employed to verified the targeted proteins. Results showed that high expression level of LINC00482 was observed in bone metastatic PCa tissues and associated with PCa progression. Silencing of LINC00482 inhibited cell proliferation, migration and invasion in PCa. Furthermore, LINC00482 was proved to act as a competing endogenous RNA by sponging miR-2467-3p to activate Wnt/ß-catenin signaling pathway, which may be a promising therapeutic target for PCa with bone metastasis.

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.

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.

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.

A Worldwide Bibliometric Analysis of Published Literature on Osteoporosis Vertebral Compression Fracture.

Background: With the aging population growth in the world, the prevalence of osteoporotic vertebral compression fracture (OVCF) is rapidly increasing and receiving widespread attention. Although there are numerous articles on the topic, the research status and hotspot analysis are unclear. Objective: The goal of this study is to identify trends in the OVCF field and to analyze the most highly cited original articles published in the Web of Science Index on OVCF using bibliometric analysis. Methods: All OVCF data were obtained from the Web of Science Core Collection, and the number of citations, institutions, journals, countries, and years of publication in this field were visually analyzed using VOSviewer, the bibliometrics online analysis platform, and Excel software. Simultaneously, the top 100 most cited articles were extracted and analyzed. Results: A total of 756 publications were related to OVCF were included from 1900 to 2022. In recent years, the number of articles on OVCF significantly increased. They are mainly from 41 countries/regions and 202 journals, led by China and the United States. Among all countries, China had the most significant contribution on OVCF (n = 363), and it also was cited most often (n = 3337). The institution with the most articles was Soochow University (n = 40). Osteoporosis International was the journal with most studies and has published 50 on this field. The journal of Spine was cited most often (n = 1968). The most productive periods were from 2016 to 2020, which received 294 articles and 4868 citations. After the analysis, the "vertebroplasty" and "kyphoplasty" of OVCF have been the most common research hotspots. Conclusion: This study represents an updated bibliometric analysis of OVCF. The aim is to identify current research hotspots and future trends to guide clinicians and researchers in this field.

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.

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.

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.