BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

OBJECTIVES: Oblique lateral interbody fusion (OLIF) has become a well-established treatment for lumbar spinal stenosis (LSS) due to its advantages of being minimally invasive, effective, and associated with fewer complications. However, relying solely on lateral fixation provides limited strength and uneven load distribution. Conventional posterior bilateral fixation after OLIF typically requires intraoperative repositioning, increases fluoroscopy frequency, and involves extensive dissection of posterior muscles and soft tissues, resulting in greater trauma, blood loss, and risks of dural tear, nerve root injury, and persistent postoperative low back pain. This study aims to compare the clinical efficacy of robot-assisted single-position OLIF with lateral plating and posterior unilateral fixation, OLIF with lateral fixation alone, and OLIF combined with posterior bilateral fixation for treating single-segment LSS, and to explore how to enhance fixation stability, reduce trauma, and achieve precise minimally invasive outcomes without changing patient positioning. METHODS: A retrospective analysis was conducted on the clinical data from patients treated for single-segment LSS between January 2020 and June 2023 at the First Affiliated Hospital of Kunming Medical University. Patients were divided into 3 groups: Robot group (robot-assisted single-position OLIF with lateral plate and posterior unilateral fixation, 33 cases), lateral group (OLIF with lateral fixation alone, 52 cases), and combined group (OLIF with posterior bilateral fixation, 45 cases). Surgical time, intraoperative blood loss, fluoroscopy frequency, hospital stay, pedicle screw placement accuracy, and complication rates were recorded. Pain visual analogue scale (VAS) scores and Oswestry disability index (ODI) scores were assessed preoperatively, postoperatively, and at the final follow-up. Radiological evaluations (X-ray, computed tomography, and magnetic resonance imaging) measured interbody disc height (IDH), intervertebral foraminal height (IFH), and cross-sectional area (CSA) of the dural sac. Differences between pre- and postoperative imaging indices were statistically analyzed, and complication rates, fusion rates, and cage subsidence rates were recorded. RESULTS: All patients exhibited good positioning of internal fixation devices and cages, with significant symptom relief and no cases of spinal cord injury or symptom worsening. The follow-up time was (15.2±3.6) months. The operation time of the robot group was (70.62±8.99) min, which was longer than that of the lateral group (45.90±6.09) min and shorter than that of the combined group (110.12±8.44) min. The intraoperative blood loss of the robot group was (44.27±6.87) mL, which was more than that of the lateral group (33.58±9.73) mL and less than that of the combined group (79.19±10.35) mL. The number of intraoperative fluoroscopy times of the robot group was (9.49±2.25), which was comparable to that of the lateral group (7.45±2.02) but less than that of the combined group (12.24±4.25). The hospital stay of the robot group was (9.28±2.10) days, which was longer than that of the lateral group (7.95±1.91) days and shorter than that of the combined group (12.49±5.07) days. The screw placement accuracy of the robot group was 98.48%, which was higher than that of the combined group (90.55%). Postoperative and final follow-up VAS and ODI scores were significantly lower than preoperative scores in all 3 groups (all P<0.05), and there were no significant differences in preoperative VAS and ODI scores among the groups (all P>0.05). Radiologically, IDH, IFH, and CSA at the surgical segment were significantly increased postoperatively and at final follow-up compared to preoperatively and at final follow-up compared to preoperative values (all P<0.05), with no significant differences among the groups postoperatively (all P>0.05). Internal fixation remained stable during the follow-up period, and all cages achieved fusion at final follow-up. The intervertebral fusion rate of the robot-assisted group was 93.40%, which was similar to that of the combined group (95.56%) and higher than that of the lateral approach group (90.34%). The complication rate of the robot-assisted group was 6.1%, which was comparable to that of the combined group (8.9%) and lower than that of the lateral approach group (15.4%) (P<0.05). No cases of fixation loosening or breakage were observed throughout the follow-up period. CONCLUSIONS Robot-assisted single-position OLIF with lateral plate combined with posterior unilateral fixation effectively achieves indirect decompression and excellent spinal stability without the need for intraoperative repositioning. It provides high pedicle screw accuracy, reduces intraoperative blood loss, fluoroscopy times, and complication rates, offering a fully minimally invasive new treatment option for single-segment LSS.
Humans ; Spinal Stenosis/surgery* ; Robotic Surgical Procedures/methods* ; Lumbar Vertebrae/surgery* ; Spinal Fusion/instrumentation* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Treatment Outcome ; Bone Plates ; Minimally Invasive Surgical Procedures/methods* ; Adult

Objective:To investigate the efficacy and safety of daratumumab-based regimens for the treatment of relapsed/refractory multiple myeloma (RRMM).Methods:A retrospective case series study was conducted. Thirty-seven RRMM patients treated with daratumumab-based regimens at Shanxi Bethune Hospital from January 2017 to November 2023 were selected, and their efficacy and adverse reactions were analyzed.Results:The median age [ M ( Q1, Q3)] of 37 RRMM patients was 62 (56, 68) years, the median number of previous treatment lines was 2 (1, 3.5) lines, 12 cases (32.4%) had extramedullary lesions, 12 cases (32.4%) had lactate dehydrogenase (LDH) ≥ 245 U/L, and 11 cases (29.7%) had previously received the third-line or more treatment. Among 27 patients who completed fluorescence in situ hybridization testing, 8 cases (29.6%) had high-risk cytogenetical changes. The median time from diagnosis to use of daratumumab was 23.1 (5.9, 52.0) months. The overall response rate (ORR) of 37 RRMM patients after treatment was 75.7% (28/37), with ORR of 88.0% (22/25) and 50.0% (6/12) for patients without and with extramedullary lesions, respectively, and the difference was statistically significant ( P = 0.036). The median follow-up time was 12.3 (4.6, 22.7) months, the median progression-free survival (PFS) time was 7.8 months (95% CI: 2.0- 13.7 months), and the median overall survival (OS) time was 22.4 months (95% CI: 17.5-29.5 months). The median PFS time for patients without and with extramedullary lesions was 11.8 and 4.2 months, and the median OS time was 23.5 and 8.3 months, respectively, and the differences in PFS and OS between the two were statistically significant (both P < 0.05); the median PFS time for patients with LDH ≥ 245 U/L and < 245 U/L was 6.5 and 11.9 months, and the median OS time was 30.2 and 12.1 months, respectively, and the differences in PFS and OS between the two were statistically significant (both P < 0.05). The incidence of non-hematological adverse reactions was 32.4% (12/37), with the most common being infusion-related adverse reactions (29.7%, 11/37), all of which were grade 1-2; the incidence of ≥ grade 3 hematological adverse reactions was 13.5% (5/37), with thrombocytopenia being the most common (8.1%, 3/37). Conclusions:The ORR of RRMM patients treated with daratumumab-based regimens is high, and the adverse reactions are tolerable.

Network analysis serves as a critical methodological approach for enhancing the efficiency of health management in elderly patients with chronic diseases,demonstrating unique advantages in precisely identifying intervention targets.This article comprehensively reviews recent advancements in the application of network analysis for managing chronic conditions in older adults,systematically organizing research findings across 3 analytical dimensions:cross-sectional networks,dynamic networks,and individualized networks.The review aims to provide healthcare professionals with a robust theoretical foundation and methodological support for developing personalized health management strategies.

Head and neck squamous cell carcinoma(HNSCC)progression is closely associated with chronic inflammation mediated by the NLRP3 inflammasome within the tumor microenvironment(TME).The NLRP3 inflammasome integrates pathogen-associated and stress-in-duced signals to dynamically regulate IL-1β/IL-18 secretion and pyroptosis.Through its action,NLRP3 inflammasome exhibits a functional du-ality,exerting tumor-suppressive effects by activating the Caspase-1/GSDMD pathway to induce tumor cell pyroptosis while also driving ma-lignant progression through remodeling of immunosuppressive TMEs.This remodeling includes driving M2-type macrophage polarization and suppressing NETs-associated pyroptosis,thereby promoting cancer stem cell maintenance and contributing to chemotherapy resistance.Preclinical studies have confirmed that NLRP3-targeted strategies,including small-molecule inhibitors(MCC950 and BAY11-7082),Bacopa monnieri,and IL-1 signaling biologics,significantly suppress tumor growth and metastasis in HNSCC and other cancers.In this review,we systematically decipher the NLRP3 regulatory network,evaluate the translational potential of targeted interventions,and offer novel thera-peutic avenues to overcome treatment barriers in HNSCC.

In recent years, immunotherapy represented by immune checkpoint inhibitors has made important breakthroughs in the treatment of various malignant tumors. At present, immune checkpoint inhibitors have been widely used in patients with metastatic and recurrent advanced head and neck squamous cell carcinoma (HNSCC). Biomarkers play an important role in predicting the efficacy of immunotherapy for HNSCC. The biomarkers that can predict immunotherapy effect of patients with HNSCC mainly include programmed-death receptor ligand 1, tumor mutation load, human papillomavirus, and immune cell related indicators. This article reviews the predictive markers of immunotherapy for HNSCC and the current research situation. The further understanding of the predictive markers can help to accurately screen out the patients receiving the immunotherapy and to improve the prognosis of patients.

Objective:To investigate and analyze basic status of radiotherapy units in Hebei Province in 2024.Methods:Led by the Fourth Hospital of Hebei Medical University, the Radiation Oncology Branch of the Hebei Medical Association, and the Radiation Oncology Expert Committee of the Hebei Society of Clinical Oncology, a province-wide survey was conducted using structured questionnaires. The survey covered key aspects such as basic information of radiotherapy institutions, personnel allocation, equipment configuration, and implementation of radiotherapy techniques. Collected data were summarized and descriptively analyzed comparing with a 2013 survey of radiotherapy in Hebei Province.Results:All 158 radiotherapy institutions across Hebei Province participated in the survey. A total of 2273 radiotherapy professionals were reported, including 1317 radiation oncologists (57.94%), 332 medical physicists (14.61%), 71 radiotherapy engineers (3.12%), and 553 radiotherapy technologists (24.33%). The number of radiotherapy devices significantly increased from 121 in 2013 to 237 in 2024, including 68 domestic radiotherapy equipment. The current inventory includes 195 medical linear accelerators (2.61 units per million population), 2 cobalt-60 units, 27 afterloading machines, 9 tomotherapy (TOMO) systems, 3 CyberKnife units, and 1 proton therapy system. Three-dimensional conformal radiotherapy and stati intensity-modulated radiotherapy have been widely adopted across the province, while advanced techniques such as volumetric-modulated arc therapy, stereotactic body radiotherapy, and respiration-gated technology, and respiratory gating are gradually being implemented.Conclusions:In recent years, the configuration of radiotherapy personnel in Hebei Province has become more balanced, and the availability of precision radiotherapy equipment has significantly improved. There is a growing trend in the adoption of domestically manufactured radiotherapy equipment, marking substantial progress in the development of radiation oncology services in the region.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

Objective To analyze the clinical characteristics and interventional treatment effects of pa-tients with renal arteriovenous fistula in this hospital over the past 10 years.Methods A retrospective analy-sis was conducted on the clinical and imaging data of 40 patients with renal arteriovenous fistula who were treated in this hospital from October 2012 to October 2022.After classifying the fistula type using digital sub-traction angiography(DSA),an individualized embolization plan was developed by a vascular surgery team.Postoperatively,the embolization materials,embolization success rate,clinical success rate,perioperative renal function changes and complications were analyzed.Results A total of 29 cases(72.5%)had acquired renal arteriovenous fistula,while 11 cases(27.5%)had non-acquired renal arteriovenous fistula.Forty-three super-selective transcatheter arterial embolization(TAE)procedures were performed,achieving a technical success rate of 100.0%(40/40)and a clinical success rate of 95.0%(38/40).The mean serum creatinine levels after TAE were(91.39±23.72)mmol/L in the non-acquired group and(105.94±35.51)mmol/L in the acquired group.No statistically significant difference was observed in perioperative serum creatinine changes between the two groups(P=0.095).No severe complications such as renal function deterioration occurred postopera-tively in any patient.Conclusion Gross hematuria serves as the primary clinical manifestation of renal arterio-venous fistula,with abdominal pain being more prevalent in non-acquired renal arteriovenous fistula.The em-bolization approach combining coils with polyvinyl alcohol particles demonstrates favorable safety and efficacy in treating renal arteriovenous fistula.