BACKGROUND:Secondary spinal cord injury (SCI) involves intense neuroinflammation driven by pyroptosis and damage-associated molecular patterns (DAMPs). Targeting NINJ1, an executioner protein for plasma membrane rupture, may break this cycle. METHODS:A mouse contusion SCI model was used, with groups including Sham, Vehicle, Methylprednisolone, and low/high-dose NINJ1 monoclonal antibody (mAb). Functional recovery was assessed by BMS scoring and gait analysis. Histopathology and molecular changes (neuronal survival, pyroptosis proteins GSDMD/NLRP3/Caspase-1, microglial polarization markers, and related pathways) were analyzed at 14 dpi. HMGB1 release was quantified. An in vitro co-culture model of glutamate-injured HT22 neurons and BV2 microglia validated the effects. RESULTS:NINJ1 mAb treatment significantly improved motor function and reduced pathology versus controls. It enhanced neuronal survival, suppressed pyroptosis executers (GSDMD, while NINJ1 mRNA expression remained unchanged), and decreased HMGB1 release. The treatment shifted microglial polarization from pro-inflammatory M1 to anti-inflammatory M2, correlating with reduced nuclear p-NF-κB p65 and increased p-STAT3. In vitro, NINJ1 mAb directly protected neurons and suppressed microglial M1 polarization and pro-inflammatory cytokine release. CONCLUSION:The NINJ1 monoclonal antibody promotes recovery after SCI by inhibiting pyroptotic membrane rupture, reducing DAMP release, and modulating microglia toward an anti-inflammatory phenotype, presenting a novel therapeutic strategy.

Introduction:Unilateral biportal endoscopic unilateral laminotomy for bilateral decompression (UBE-ULBD) is a minimally-invasive yet technically demanding procedure for lumbar spinal stenosis. Aim:This study aimed to compare the learning curves of 2 generations of surgeons performing UBE-ULBD under a structured mentorship model. Materials and methods:We retrospectively analyzed 200 consecutive surgeries performed between January 2020 and June 2024. The first-generation surgeon (FGS) performed all procedures independently. The second-generation surgeon (SGS) assisted in 30 surgeries and then received on-site supervision for the first 15 independent cases. Data on operative time, blood loss, complications, and clinical outcomes, including the Visual Analog Scale and Oswestry Disability Index, were collected. Operative time-based cumulative sum analysis was used to evaluate the learning curves. Results:The FGS achieved proficiency after 37 and the SGS after 29 procedures. The SGS had significantly shorter mean (SD) operative time of 127.6 (13.2) vs 137.1 (19.3) minutes and lower blood mean (SD) loss of 49 (13.6) vs 57.7 (20.6) milliliters, as compared with his first-generation counterpart. Complications and clinical outcomes were comparable. After achieving proficiency, both surgeons showed improved efficiency without differences in safety or outcomes. Conclusions:Under the guidance of the FGS, the SGS achieved proficiency in UBE-ULBD more rapidly, as reflected in the shorter operative time. Moreover, once proficiency was reached, no notable differences were observed between the 2 surgeons in terms of postoperative complications or clinical outcomes.

The binary diagnostic approach does not reflect the entire spectrum of metabolic dysfunction associated steatotic liver disease (MASLD. We used an elastography technology, dual elastography ultrasound (DEUS), to discriminate the different stages of MASLD. This prospective multicenter study was conducted from December 2020 to March 2022. All patients underwent DEUS scan, a liver biopsy, and a liver function laboratory test. The optimal model was developed (ModelDEUSC) with 10 machine learning algorithms by combining DEUS and selected clinical parameters and tested the diagnostic accuracy for distinguishing the three progression stages of MASLD: low-, intermediate-, and high-risk. The diagnostic ability of ModelDEUSC for MASH with advanced fibrosis (≥ F3) was compared with other four non-invasive tests. The study included 312 patients in the derivation cohort and 135 in the validation cohort (7:3). Combining DEUS and clinical parameters, a ternary classification of MASLD in the validation cohort achieved a macro-average AUC of 0.858 (95

To develop and validate a machine learning (ML) model integrating dual elastography, clinical features, and serum biomarkers for noninvasive prediction of severe drug-induced liver injury (DILI). This prospective multicenter study enrolled consecutive DILI patients undergoing liver biopsy and dual elastography. Severe DILI was defined as Scheuer inflammation grade plus fibrosis stage ≥ 5 (G + S ≥ 5). Dual elastography-derived activity index (A index) and fibrosis index (F index) correlated with pathological inflammation (G0–4) and fibrosis (S0–4) stages. The dataset was stratified and split 7:3 into training and test sets. LASSO regression was applied for feature selection. Eight ML models were constructed and compared, optimized using 5-fold cross-validation and Bayesian methods. Performance was evaluated by area under the curve (AUC), sensitivity, and specificity. SHapley Additive exPlanations (SHAP) were used to interpret the models. A total of 305 participants were included (median age 49 years, IQR 40–56; 98 male), comprising 55 with severe DILI and 250 without. A and F indices increased with inflammation grade and fibrosis stage, respectively (p < 0.01). Combining clinical and dual elastography features with serum biomarkers, the optimized regularized regression model performed best in the test set (AUC 0.862 [95 https://wznng666.shinyapps.io/RR55555/ .

Background and objective Variceal bleeding carries high mortality and recurrence rates, underscoring the urgent need for effective non-invasive tests (NITs) to assess the severity of esophageal varices (EVs) and predict bleeding risk. This study aimed to develop and validate a robust model for safely excluding high-risk varices (HRVs) in patients with hepatitis B virus (HBV)-related cirrhosis and to compare its diagnostic performance and clinical utility with established NITs.Methods Consecutive patients with HBV-related cirrhosis were prospectively recruited from five centers and underwent dual elastography (dual-elasto) within 1 week of esophagogastroduodenoscopy (EGD). A training cohort from four centers was used to identify predictive variables for HRVs, which were analyzed using machine learning to develop the most reliable model. The final model was externally validated in an independent cohort from the fifth center. Diagnostic efficacy and clinical utility were compared across multiple NITs, including the Baveno VI criteria (B6C), expanded Baveno VI criteria (EB6C), platelet-spleen ratio (PSR), and RESIST-HCV criteria (RESIST).Results Among 703 screened patients, 328 were enrolled (training cohort n = 184; validation cohort n = 144). Using logistic regression, we developed the DELU model incorporating six variables: platelet count (PLT), alanine aminotransferase (ALT), ascites, portal vein thrombosis (PVT), inverse difference moment (IDM), and liver stiffness (liver Vs). DELU achieved areas under the receiver operating characteristic curve (AUROCs) of 0.822 (training) and 0.779 (validation), outperforming RESIST (0.600 and 0.626) and B6C (0.569 and 0.575). EB6C and PSR were excluded because they exceeded the 5% missed-HRV threshold. DELU demonstrated the highest spared-EGD rates (20.7% training; 11.1% validation) and maintained robust performance across subgroups, including Child-Pugh B/C (AUROC 80.3%), Child-Pugh A (79.7%), antiviral therapy (ART)-treated (82.5%), and virologically suppressed patients (84.6%). Performance was not influenced by hepatocellular carcinoma (HCC) status, sex, or body mass index (BMI).Conclusion DELU is a reliable and safe non-invasive tool for excluding HRVs in HBV-related cirrhosis, providing superior diagnostic accuracy and significant endoscopy-sparing potential across diverse clinical settings. RESIST may serve as a safe alternative when elastography is unavailable.Clinical trial registration [ClinicalTrials.gov], identifier [NCT04640350].

INTRODUCTION:Lumbar degenerative disease (LDD) is increasingly common, and causes back and leg pain that impairs quality of life. Lumbar interbody fusion (LIF) is effective for patients with neural compression and segmental instability. Unilateral biportal endoscopic LIF (UBE-LIF) allows for minimally-invasive decompression and fixation, while navigation- and robot-assisted systems improve pedicle screw accuracy and intraoperative guidance. AIM:This study compared perioperative outcomes and clinical efficacy of navigation-assisted vs robot-assisted single-level UBE-LIF. MATERIALS AND METHODS:Patients with single-level LDD who underwent navigation-assisted (Na group; n = 23) or robot-assisted (Ra group; n = 29) UBE-LIF between January 2020 and December 2024 were retrospectively enrolled. Clinical outcomes were assessed using the Numeric Rating Scale, Oswestry Disability Index, and modified MacNab criteria. Pedicle screw placement and radiological parameters, including disc height, lumbar lordosis, and segmental lumbar lordosis, were evaluated, and IF was assessed at 12 months postoperatively. RESULTS:Endoscopic operative time was shorter in the Na group than in the Ra group (116.74 vs 127.86 min; P = 0.03), whereas screw insertion time and pedicle screw placement were superior in the Ra group (39.55 vs 46.52 min; P = 0.001 and 98.5% vs 92.4%; P = 0.04, respectively). Both groups showed comparable improvements in clinical outcomes, radiological parameters, and fusion rates, with similarly low complication rates. CONCLUSIONS:Navigation- and robot-assisted UBE-LIF are safe and effective procedures. Robot-assisted surgery offers higher screw accuracy and faster insertion, while the navigation-assisted approach reduces endoscopic operating time. Clinical outcomes and fusion rates between the 2 techniques are similar.

Domain generalization poses a central challenge in medical ultrasound imaging: clinical centers differ substantially in equipment manufacturers, scanning protocols, and patient demographics, yet diagnostic models must remain robust across these variations. Existing style-based augmentation methods focus on perturbation strategies during training while overlooking the inherent discrepancy between training and test distributions, leading to poor generalization when deployed at previously unseen centers. To address this limitation, we propose the Multi-Center Adaptive Cross-Domain Style Alignment (MACS) framework, which bridges the train-test distribution gap through domain-specific style prototype-guided test-time adaptive normalization. MACS comprises four synergistic modules: 1) Domain-wise Correlation-Aware Style Augmentation (CASA) independently models the channel covariance structure for each source domain and generates correlation-aware style perturbations along principal variation directions; 2) Domain Style Prototype Learning (DSPL) accumulates representative style statistics for each source domain via exponential moving average, forming stable alignment anchors; 3) Style-based Domain Discriminator (SDD) identifies domain membership from channel-wise means and variances of feature maps; 4) Test-Time Adaptive Style Alignment (TTASA) fuses source domain style prototypes through discriminator-guided weighting, constructing an adaptive normalization target for each test sample. We validate MACS on an international multi-center elastography ultrasound dataset comprising 1,937 chronic liver disease patients from 17 clinical centers across China, Japan, and Europe. Extensive experiments show that MACS consistently outperforms existing domain generalization methods across diverse cross-domain evaluation settings, achieving superior accuracy and stability. Ablation studies further confirm the synergistic contributions of each module. As a plug-and-play solution with negligible computational overhead, MACS supports real-time inference and is well suited for practical deployment in multi-center clinical diagnosis.

Background:Degenerative lumbar spinal stenosis (DLSS) is a leading cause of low back and leg pain in the elderly. While conventional posterior lumbar interbody fusion (PLIF) is effective, it remains associated with significant tissue trauma and a relatively high incidence of chronic postoperative low back pain, even when empowered by current robotic-assisted technologies. Existing evidence suggests that robotic-assisted midline lumbar interbody fusion with cortical bone trajectory (RA-MIDLIF-CBT) may offer advantages in terms of minimal invasiveness. However, there is a lack of high-quality evidence regarding its non-inferiority in efficacy compared to the equally robotic-assisted PLIF technique, as well as its potential for enhancing accelerated recovery after surgery. Methods:A single-center, prospective, randomized controlled, non-inferiority trial will be conducted. Seventy-four patients aged 60-80 years with single-level DLSS refractory to conservative treatment and meeting definitive criteria for lumbar interbody fusion will be enrolled and randomly assigned (1:1 ratio) to either Group A (control): Robot-assisted PLIF with pedicle screw fixation, or Group B (experimental): Robot-assisted MIDLIF-CBT. The primary outcome measure is the Oswestry Disability Index (ODI). Secondary outcomes include Visual Analog Scale (VAS) scores for low back pain and leg pain, Japanese Orthopaedic Association (JOA) score, operative time, intraoperative blood loss, radiation exposure, screw placement accuracy, compliance with enhanced recovery after surgery (ERAS) protocols, and health economic parameters. Patients will be followed up for 12 months postoperatively. Discussion:This study will be the first to provide high-level evidence on the non-inferiority of RA-MIDLIF-CBT compared to robot-assisted PLIF for treating DLSS. Leveraging the foundational platform of robotic assistance, the findings have the potential to establish RA-MIDLIF-CBT as a novel fusion technique that balances minimal invasiveness, safety, and cost-effectiveness. Additionally, the results will contribute evidence-based support for optimizing ERAS pathways in geriatric spine surgery. Trial Registration:The trial protocol was registered at Chinese Clinical Trial Registry (www.chictr.org.cn, Registration Number: ChiCTR2500095896).

Introduction:Chronic discogenic low back pain (DLBP) with active discopathy (Modic type 1 changes) is a specific and debilitating phenotype. Transforaminal epidural steroid injection (TESI) and transforaminal intradiscal steroid injection (TISI) are commonly used treatments, yet their comparative efficacy remains uncertain due to a lack of high-quality, direct comparative studies. Objectives:To compare the clinical efficacy and safety of TISI versus TESI and to investigate whether the anatomical target of corticosteroid delivery (intradiscal vs. epidural) influences clinical and radiological outcomes in patients with chronic DLBP with active discopathy. Trial design:This is a single-center, prospective, parallel-group, assessor- and patient-blinded, randomized, controlled trial. Methods:A total of 118 eligible participants will be randomly allocated in a 1:1 ratio to receive either TISI or TESI. The primary outcome is the change in low back pain intensity from baseline to 1-month post-intervention, measured by the Numerical Rating Scale (NRS). Secondary outcomes, assessed at multiple time points up to 12 months, include longitudinal pain intensity (NRS), functional status (Oswestry Disability Index, Japanese Orthopaedic Association score), health-related quality of life (12-item Short Form Health Survey), psychological status (Hospital Anxiety and Depression Scale), radiological changes (Modic classification, intervertebral disc heigh and Pfirrmann grade), and the incidence of procedure-related adverse events. The primary analysis will follow the modified intention-to-treat principle, with longitudinal data analyzed using a linear mixed model. Discussion:This trial addresses a critical evidence gap by conducting a direct head-to-head comparison of two mechanistically distinct injection therapies. The findings are anticipated to significantly inform clinical practice and guide the development of evidence-based treatment algorithms for managing chronic low back pain with active discopathy. Ethics and dissemination:The study protocol has been approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (IIT2024-032-003). Written informed consent will be obtained from all participants. The results of this trial will be submitted for publication in peer-reviewed journals and presented at scientific conferences, regardless of the outcome. Clinical trial registration:ChiCTR2500096006, https://www.chictr.org.cn/showproj.html?proj=249912.

Background: This study aimed to identify risk factors for postoperative complications following a one-stage posterior approach for thoracolumbar Brucellosis spondylitis. Methods: We retrospectively analyzed data from 61 patients with thoracolumbar Brucellosis spondylitis who underwent a one-stage posterior approach at this institution between January 2015 and January 2019. To compare clinical characteristics, patients were divided into two groups based on the presence of postoperative complications: a complication group (14 cases) and a control group (47 cases). Logistic regression analysis was then used to identify factors associated with postoperative complications. Results: Disease duration, fever status, and hemoglobin levels differed significantly between groups (all P < 0.05). Further, multivariate analysis identified diabetes, fever, and psoas muscle abscess as independent risk factors for postoperative complications (all P < 0.05). Conclusion: Having diabetes, fever, and psoas muscle abscess are independent risk factors for complications after a one-stage posterior approach for thoracolumbar Brucellosis spondylitis. Therefore, careful selection of surgical indications, strict surgical technique, proper blood glucose management in diabetic patients, and preoperative control of body temperature are essential to reduce complication rates.

Accurate, noninvasive diagnosis of compensated advanced chronic liver disease (cACLD) is essential for effective clinical management but remains challenging. This study aimed to develop a deep learning-based radiomics model using international multicenter data and to evaluate its performance by comparing it to the two-dimensional shear wave elastography (2D-SWE) cut-off method covering multiple countries or regions, etiologies, and ultrasound device manufacturers. This retrospective study included 1937 adult patients with chronic liver disease due to hepatitis B, hepatitis C, or metabolic dysfunction-associated steatotic liver disease. All patients underwent 2D-SWE imaging and liver biopsy at 17 centers across China, Japan, and Europe using devices from three manufacturers (SuperSonic Imagine, General Electric, and Mindray). The proposed generalized deep learning radiomics of elastography model integrated both elastographic images and liver stiffness measurements and was trained and tested on stratified internal and external datasets. A total of 1937 patients with 9472 2D-SWE images were included in the statistical analysis. Compared to 2D-SWE, the model achieved a higher area under the receiver operating characteristic curve (AUC) (0.89 vs 0.83, P = 0.025). It also achieved a highly consistent diagnosis across all subanalyses (P values: 0.21–0.91), whereas 2D-SWE exhibited different AUCs in the country or region (P < 0.001) and etiology (P = 0.005) subanalyses but not in the manufacturer subanalysis (P = 0.24). The model demonstrated more accurate and robust performance in noninvasive cACLD diagnosis than 2D-SWE across different countries or regions, etiologies, and manufacturers.

STUDY DESIGN:Retrospective cohort study. OBJECTIVES:The goal of this study was to identify the clinical and radiological characteristics of patients with osteoporosis and to develop a practical clinical prediction model for patients for accurately predicting the risk of osteoporosis. METHODS:This study included 954 patients from September 2020 to September 2024 at our hospital. Independent risk factors were selected by the least absolute shrinkage and selection operator method (LASSO) regression. Then, a prediction model (nomogram) was established. Randomly split internal validation cohorts were used to test the nomogram model's calibration, discrimination, and clinical utility. RESULTS:Six independent prediction factors, age, female, glucocorticoid use, chronic obstructive pulmonary disease (COPD), cut-off values for Hounsfield unit (HU) and vertebral quality (VBQ) scores, were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the prediction nomogram was 0.86 in training set. The area under the receiver operating characteristic curve (AUC) was 0.87 in both the training and validation sets. The model has good practicability for clinics according to the decision curve analysis (DCA) and clinical impact curve (CIC). CONCLUSIONS:The nomogram model has good predictive performance and clinical practicability, which could provide a certain basis for simplifying osteoporosis diagnosis.

Magnesium phosphate cement (MPC) continues to gain attention in the field of biomedicine. However, its suboptimal mechanical strength and weak biological activity hinder its wider clinical application. Given the excellent biological characteristics of bioglass fiber (BGF), In this study, magnesium phosphate bone cement (BMPC) containing MPC and BGF with different concentrations (0%, 10%, 20%) are fabricated. Called (MPC, 10BMPC, 20BMPC) respectively. BGF-induced mechanical strengthening is verified through physical and chemical performance tests. In vitro experiments showed that BMPC have better osteogenic properties than MPC and can enhance the proliferation and adhesion capacity of human umbilical vein endothelial cells. In vivo experiment, 20BMPC can significantly promote bone regeneration and vascular network formation, and histological analysis further confirmed the osteogenic capacity of 20BMPC. Transcriptomic analyses confirmed that the activities of the Notch pathway and Hif1 pathway are upregulated in the 20BMPC group, reflecting the strong interconnection between osteogenesis and angiogenesis. 20BMPC, which have the highest BGF content, showed the best performance among all the tested materials. This study showed that BGF improved the mechanical strength of bone cement and enhanced its osteogenic and angiogenic abilities. Therefore, 20BMPC can be used as a new bone repair material.

BACKGROUND:Lumbar spinal stenosis (LSS) is a common degenerative disorder characterized by neural compression, leading to radicular pain, neurogenic claudication, and lower limb dysfunction. Unilateral biportal endoscopic unilateral laminotomy for bilateral decompression (UBE-ULBD) has emerged as an effective minimally invasive surgical technique, but it requires advanced surgical skills and has a steep learning curve. The use of computer-assisted navigation has been increasingly adopted to improve surgical efficiency and safety; however, its clinical effectiveness and safety in UBE-ULBD for single-level LSS remain insufficiently investigated. METHODS:A total of 119 patients undergoing UBE-ULBD were included and divided into Group A (navigation-assisted, n = 57) and Group B (fluoroscopy-guided, n = 62), with a minimum follow-up of 12 months. Perioperative outcomes and clinical efficacy were evaluated using operative time, fluoroscopy frequency, estimated blood loss, postoperative hospital stay, complication rates, as well as visual analogue scale (VAS) scores for back and leg pain, Oswestry Disability Index (ODI), and modified MacNab criteria. RESULTS:No statistically significant differences were observed in baseline characteristics between the two groups. Group A had a significantly shorter operative time, fewer intraoperative fluoroscopies, and lower estimated blood loss compared with Group B (all P < 0.001), while postoperative hospital stay and overall complication rates did not differ significantly (P > 0.05). No surgical site infections or permanent nerve injuries occurred in either group. Both groups showed significant postoperative improvements in VAS and ODI scores relative to preoperative values; however, no intergroup differences were found at any follow-up point (P > 0.05). At the final follow-up, the excellent/good rate based on the modified MacNab criteria was 93.0% in Group A and 87.1% in Group B, with no significant difference (P > 0.05). CONCLUSION:Navigation-assisted UBE-ULBD can significantly improve surgical efficiency and markedly reduce intraoperative radiation exposure for surgeons, while demonstrating comparable safety and clinical efficacy to conventional fluoroscopy-guided procedures. These advantages highlight its potential clinical value as a safer and more efficient alternative for minimally invasive decompression in lumbar spinal stenosis.

BACKGROUND/AIMS:A large percentage of patients undergoing esophagogastroduodenoscopy (EGD) screening do not have esophageal varices (EV) or have only small EV. We evaluated a large, international, multicenter cohort to develop a novel score, termed FIB-4plus, by combining the fibrosis-4 (FIB-4) score, liver stiffness measurement (LSM), and spleen stiffness measurement (SSM) to identify high-risk EV (HRV) in compensated cirrhosis. METHODS:This international cohort study involved patients with compensated cirrhosis from 17 Chinese hospitals and one Croatian institution (NCT04546360). Two-dimensional shear wave elastography-derived LSM and SSM values, and components of the FIB-4 score (i.e., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. Shapley Additive exPlanations method was used to interpret the model predictions. RESULTS:We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. The XGBoost-FIB-4plus score demonstrated superior predictive performance for HRV, with an area under the receiver operating characteristic curve (AUROC) of 0.927 (95% confidence interval [CI] 0.897-0.957) in the training cohort (n=268), and 0.919 (95% CI 0.843-0.995) and 0.902 (95% CI 0.820-0.984) in the first (n=118) and second (n=82) external validation cohorts, respectively. Additionally, the XGBoost-FIB-4plus score exhibited high AUROC values for predicting EV across all cohorts. The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4). CONCLUSION:The FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes.

IntroductionLow back pain (LBP), primarily driven by intervertebral disc degeneration (IDD), imposes a significant global health burden. While type 2 diabetes mellitus (T2DM) is a recognized risk factor for IDD, the shared molecular mechanisms remain incompletely characterized.MethodsThis study employed integrated bioinformatics (WGCNA, machine learning - LASSO, RF, ANN) on human T2DM and IDD transcriptomic datasets, alongside scRNA-seq analysis of diabetic mouse nucleus pulposus (NP) tissue, to identify key drivers of diabetes-associated IDD.ResultsBioinformatics analysis of human data identified three diagnostic biomarkers (S100A12, IL1R1, FCGR2B) and constructed a robust ANN diagnostic model (AUCs: 0.744-0.868). IL1R1 emerged as the most significant risk factor. scRNA-seq revealed altered cellular composition in diabetic discs, notably increased proportion of granulocytes (predominantly neutrophils) and decreased proportion of nucleus pulposus (NP) cells. IL1R1 was highly expressed in specific diabetes-associated NP subpopulations and showed significant positive correlation with neutrophil infiltration. Functional enrichment linked IL1R1 to inflammation, DNA repair, and immune pathways. Furthermore, we constructed a regulatory network (STAT1/STAT6-IL1R1-miRNAs-lncRNAs) and identified icariin as a potential therapeutic candidate via molecular docking.DiscussionThese findings establish IL1R1 as a pivotal molecular bridge connecting T2DM and IDD, driven by neutrophil-mediated inflammation and NP cell dysfunction, offering novel diagnostic and therapeutic avenues.

Lumbar spondylolysis of a single lumbar vertebra with a fracture of the pedicle on the opposite side, as well as fractures of both pedicles and bilateral spondylolysis, have been extensively reported in the literature. These cases are commonly linked to factors such as trauma, sports activities, and spinal surgeries. We report a unique case of a unilateral lumbar spondylolysis with a fracture on the opposite side including the pedicle and lamina. To the best of our knowledge, this specific case has not been previously reported. Additionally, we provide a comprehensive assessment of the existing literature on this subject.

Aims : Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk GEV (HRV). Methods : This was a multicenter, prospective study conducted from October 2020 to September 2022 across 12 hospitals in China. Patients with compensated advanced chronic liver disease (cACLD) were enrolled, with informed consent obtained. A total of 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk; in this manner, we constructed models to predict GEV and HRV. Results : A multimodality deep learning risk prediction (DLRP) model was constructed to assess GEV and HRV based on LSM and SSM images and clinical information. Validation analysis revealed that the area under the curve (AUC) values of DLRP were 0.91 for GEV (95% confidence interval [CI], 0.90-0.93, p < 0.05) and 0.88 for HRV (95% CI, 0.86-0.89, p < 0.01), which were significantly and robustly better than those of canonical risk indicators, including the values of LSM (0.63 and 0.68 for GEV and HRV) and SSM (0.75 for both GEV and HRV). Moreover, the DLRP model outperformed the model using individual parameters. In HRV prediction, the 2D-SWE SSM images (0.75) were more informative than LSM (0.68, p < 0.01). Conclusion : Our DLRP model shows excellent performance in predicting GEV and HRV, outperforming the canonical risk indicators LSM and SSM. Additionally, the 2D-SWE SSM images provided more information and thus better accuracy in HRV prediction than the LSM images. Trial Registration : ClinicalTrials.gov; NCT04546360.

Objective:This study aims to evaluate the diagnostic efficacy of shear wave elastography (SWE) and super-resolution imaging (SRI) in detecting moderate-to-severe renal fibrosis (MSRF) among patients with chronic kidney disease (CKD). Methods:In this prospective study, 202 CKD patients who underwent SWE and SRI prior to renal biopsy were enrolled. Based on pathological findings, patients were categorized into a mild renal fibrosis group (n=107) and an MSRF group (n=95). LASSO logistic regression was employed to identify independent risk factors for MSRF. Four diagnostic models-isolated, series, parallel, and integrated-were developed by combining elasticity values from SWE and vascular density values from SRI. Additionally, a nomogram incorporating clinical parameters and ultrasound composite parameters was constructed to assess MSRF in CKD patients. Results:LASSO and subsequent logistic regression analysis revealed that age, diabetes history, estimated glomerular filtration rate (eGFR), elasticity, and vascular density were independently associated with MSRF. The integrated model, utilizing a logistic algorithm, demonstrated superior diagnostic performance with an area under the curve (AUC) of 0.83 (P < 0.001), sensitivity of 80.4%, and specificity of 75.8%, outperforming all other models. Furthermore, the nomogram, which integrated clinical factors and ultrasound composite parameters, exhibited excellent predictive performance (AUC = 0.878, 95% CI 0.782-0.974). Calibration and decision curve analyses confirmed the model's robust calibration and clinical utility. Conclusion:The integration of SWE-derived elasticity and SRI-derived vascular density significantly enhances the diagnostic accuracy for MSRF in CKD patients. This comprehensive approach offers a promising non-invasive strategy for assessing renal fibrosis severity.