This study explored the role of cellular senescence in the progression of rheumatoid arthritis (RA) and evaluated the targeting of Cyclin E2 (CCNE2) in synovial fibroblasts as a potential therapeutic approach. A risk prediction model for RA was developed using LASSO regression analysis, which involved analyzing differential gene expression and performing Gene Set Enrichment Analysis (GSEA). The model was validated using the Receiver Operating Characteristic (ROC) curve. CCNE2 expression was examined via Western blotting. Knockdown of CCNE2 in RA synovial fibroblasts (RASFs) using shRNA resulted in reduced cell viability, increased apoptosis, and elevated levels of senescence markers such as p16, p21, and p53. Additionally, senescence-associated β-galactosidase (SA-β-Gal) activity and H3K9me3 fluorescence intensity were significantly increased. In vivo, Adeno-Associated Virus (AAV)-mediated intra-articular injection of shCCNE2 in a collagen-induced arthritis (CIA) mouse model significantly reduced the arthritis index, alleviated joint inflammation, and suppressed CCNE2 expression. Furthermore, the secretion of SASP factors such as MMP-3 and IL-8 was significantly enhanced. These findings suggest that targeting CCNE2 induces senescence in RASFs and may offer a novel strategy to mitigate RA progression and inflammation.

To explore the value of Mannheim Peritonitis Index (MPI) combined with ASA Classification in predicting mortality of patients with digestive tract perforation (DTP) who underwent surgical treatment. A retrospective analysis was conducted on the clinical data of 248 patients with secondary abdominal infection caused by DTP, who were admitted to the Department of Emergency Surgery, The First Affiliated Hospital of Naval Medical University from August 2021 to August 2025. The patients were divided into the mortality group (n = 41) and the non-mortality group (n = 207). Univariate and multivariate analyses were performed to identify the risk factors for mortality in DTP patients. Receiver operating characteristic (ROC) curve was used to determine the cut-off values of continuous variables, and to evaluate the predictive value of MPI score combined with ASA Classification. The total mortality rate of DTP patients was 16.5%. Univariate regression analysis showed that mortality in DTP patients was significantly associated with age, operative time, perforation site, etiology, septic shock, extent of peritonitis, peritoneal exudate, CRP, PCT, Hb, Alb, SCr, ALT, AST, PT, D-dimer, INR, ASA Classification, and MPI score (all p < 0.05). Multivariate regression analysis and ROC curve analysis indicated that ASA Classification ≥ Grade 4 and MPI score ≥ 27 were potential risk factors for mortality in DTP patients. The area under the ROC curve (AUC) of MPI score combined with ASA Classification was 0.904, which was higher than that of MPI score alone (AUC = 0.790) or ASA Classification alone (AUC = 0.786). For predicting mortality in DTP patients, the combined assessment had a sensitivity of 87.8% and a specificity of 81.6%. In the prediction of mortality risk in DTP patients, the combined assessment of MPI score and ASA Classification exhibits better predictive performance compared with the application of MPI score or ASA Classification alone.

The aim of this study is to evaluate the association between genes related to cellular senescence and ferroptosis and their relevance to hepatocellular carcinoma (HCC). Genes associated with senescence and ferroptosis in HCC were retrieved from public databases. A protein-protein interaction (PPI) network was constructed to identify for hub genes, and validate their expression. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, while prognostic significance was determined through Kaplan-Meier analysis. A prognostic nomogram model was developed based on selected hub genes and Tumor Node Metastasis (TNM) staging. A total of 52 senescence-ferroptosis-related genes were identified in HCC. ROC analysis indicated moderate to high diagnostic efficacy for TP53 (AUC = 0.723, CI: 0.669-0.776), JUN (AUC = 0.733, CI: 0.659-0.806), RELA (AUC = 0.854, CI: 0.808-0.901), and CDKN2A (AUC = 0.953, CI: 0.932-0.974). Kaplan-Meier analysis revealed that TP53, HIF1A, and CDKN2A were significantly associated with overall survival (OS) in patients with HCC. A nomogram model incorporating these three genes and TNM staging achieved a concordance index (C-index) of 0.699. Calibration curves indicated concordance between the predicted and observed survival probabilities at 1-, 2-, and 3-year intervals. The senescence-ferroptosis-related genes TP53, HIF1A, and CDKN2A demonstrated potential as diagnostic and prognostic biomarkers in HCC. The developed nomogram may support individualized prognostic assessment and inform early diagnostic and therapeutic strategies in patients with HCC.

Objectives: To assess the diagnostic accuracy of a deep learning (DL)-based algorithm for non-invasive computation of fractional flow reserve (FFR-CT) from coronary computed tomography angiography (CCTA) and to evaluate the model’s ability to automatically assign cardiovascular risk categories according to the Coronary Artery Disease–Reporting and Data System (CAD-RADS). Materials and Methods: Sixty patients with suspected coronary artery disease who underwent both CCTA and invasive coronary angiography (ICA) were retrospectively included in this multicenter study. Curved multiplanar reconstructions derived from CCTA were analyzed by the deep learning-based model to estimate FFR-CT values and to automatically assign CAD-RADS risk categories. The diagnostic performance of the software for the identification of hemodynamically significant coronary stenoses was evaluated using ICA as the reference standard. Receiver operating characteristic (ROC) curve analysis was performed to determine the area under the curve (AUC), sensitivity, and specificity on both a per-patient and per-vessel basis. Finally, agreement between CAD-RADS risk categories assigned by the DL algorithm and those determined by an expert radiologist was assessed. Results: FFR-CT demonstrated high diagnostic accuracy, with AUC of 0.935, sensitivity of 93.2%, specificity of 93.7%, and excellent agreement with reference standard (k = 0.836) on a per-patient level. Per-vessel diagnostic performance was consistently high across all major coronary arteries, with the left anterior descending artery (LAD) showing the highest accuracy (AUC = 0.932). Automated CAD-RADS classifications generated by the software showed good agreement with those assigned by human (k = 0.765). Conclusions: The DL-based model demonstrated high diagnostic accuracy and represents a promising noninvasive approach for ischemia assessment and cardiovascular risk stratification.

Background and Objective Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of type 2 diabetes mellitus (T2DM). Although glycated hemoglobin (HbA1c) is a primary metric for glycemic control, many patients develop or experience progression of DPN despite achieving HbA1c targets, suggesting the importance of other dynamic glycemic parameters. Glycemic variability (GV) may contribute to nerve injury via mechanisms such as oxidative stress, inflammation, and neurotrophic factor dysregulation. However, clinical evidence linking GV to DPN remains inconsistent, and rigorous studies controlling for confounders are scarce. This study aimed to determine whether GV is independently associated with DPN beyond HbA1c in a propensity score-matched (PSM) cohort and to explore the potential mediating roles of inflammatory cytokines and neurotrophic factors. Methods This single-center retrospective cohort study screened T2DM patients hospitalized between January 1, 2020, and December 31, 2024. Patients with complete 72-hour continuous glucose monitoring (CGM) data and bilateral nerve conduction studies (NCS) were included. DPN was diagnosed according to the Chinese Diabetes Society guidelines. Propensity score matching (PSM, 1:1, caliper=0.02) was used to balance the DPN and non-DPN groups on age, sex, BMI, diabetes duration, HbA1c, systolic blood pressure, LDL-C, and estimated glomerular filtration rate. Primary outcomes included GV parameters (mean amplitude of glycemic excursions [MAGE], coefficient of variation [CV], standard deviation [SD]) and a composite nerve conduction velocity (NCV) Z-score. Serum inflammatory cytokines (IL-6, TNF-α) and neurotrophic factors (NGF, IGF-1) were measured in a nested subcohort. Data were analyzed using multivariable linear regression, dose-response analysis, causal mediation analysis, and receiver operating characteristic (ROC) curve analysis. Results After PSM, 256 well-matched patients (128 in each group) were included, with excellent covariate balance (all standardized mean differences &amp;lt;0.1). GV parameters (MAGE, CV, and SD) remained significantly higher in the DPN group compared to the non-DPN group after matching (all P &amp;lt; 0.001). Within the DPN group, stratification by MAGE tertiles revealed a clear dose-response relationship: higher MAGE tertiles were associated with progressively worse composite NCV Z-scores (P for trend &amp;lt;0.001). Subgroup analysis (n=160) showed that higher MAGE tertiles were associated with elevated IL-6 and TNF-α levels and decreased NGF levels (P for trend &amp;lt;0.05). Multivariable linear regression confirmed MAGE (β = -0.38, P &amp;lt; 0.001) and CV (β = -0.31, P &amp;lt; 0.001) as independent negative predictors of NCV after adjusting for confounders including HbA1c. Mediation analysis indicated that IL-6 and TNF-α collectively mediated approximately 32% of the negative effect of MAGE on NCV (indirect effect β = -0.12, P &amp;lt; 0.001). ROC curve analysis identified optimal GV thresholds for discriminating DPN: MAGE ≥5.8 mmol/L (AUC = 0.84, sensitivity 76%, specificity 79%) and CV ≥32.5% (AUC = 0.81, sensitivity 72%, specificity 77%). Conclusion In this propensity score-matched cohort study, higher glycemic variability is independently and robustly associated with the presence and severity of diabetic peripheral neuropathy in patients with T2DM, even after accounting for HbA1c and other conventional risk factors. This association exhibits a dose-response relationship and is partially mediated by systemic inflammation. Our findings advocate for incorporating GV assessment into clinical practice for better DPN risk stratification and suggest that therapeutic strategies aimed at reducing glycemic variability may offer additional neuroprotective benefits.

Objectives It is well established that calcified plaques are highly likely to lead to residual stenosis after stenting; however, the specific characteristics responsible for this effect remain unknown. This study aimed to identify both qualitative and quantitative imaging risk factors for residual stenosis using computed tomography angiography. Methods We retrospectively enrolled 233 patients with carotid artery stenosis. Patients were categorized into two groups based on the presence or absence of postoperative residual stenosis. Carotid computed tomography angiography evaluated plaque characteristics both qualitatively and quantitatively. Logistic regression analysis identified independent risk factors for residual stenosis. We evaluated the predictive model’s discriminative ability by calculating the area under the receiver operating characteristic (ROC) curve. Results Univariate analysis indicated a statistical difference in age, creatinine, total plaque volume, percentage of total calcified plaque, percentage of total soft plaque, maximum slice attenuation value, maximum thickness, total length, and a circumferential calcification score ≥2 points ( p &amp;lt; 0.05). Multivariable logistic regression identified creatinine (OR = 1. 020; 95%CI: 1.005–1.035; p = 0.010), maximum slice attenuation value(Z-score; OR = 1.627; 95%CI: 1.024–2.585; p = 0.039), percentage of calcified plaque volume(Z-score; OR = 1.872; 95%CI: 1.137–3.082; p = 0.014) and circumferential calcification score ≥2 (OR = 3.257; 95%CI: 1.620–6.548; p &amp;lt; 0.001) as independent factors associated with residual stenosis. Furthermore, receiver operating characteristic curve analysis revealed that the area under the curve for the combined model in diagnosing residual stenosis was 0.784. Conclusion In conclusion, preoperative CTA-based assessment of specific plaque characteristics, such as calcified plaque volume percentage, circumferential calcium score, and the maximum slice attenuation value of calcification are related to residual stenosis.

Background &amp; Aims: The accurate, noninvasive assessment of hepatic steatosis is essential in living liver donor evaluation, where disease prevalence is low, and donor safety is paramount. This study evaluated commonly used noninvasive diagnostic tools for detecting hepatic steatosis in a real-world donor screening setting. Methods: We analyzed 108 living liver donor candidates (18–53 years) with complete MRI, CT, transient elastography (FibroScan®), and biochemical data obtained during routine donor evaluation. Hepatic steatosis was defined as an MRI-proton density fat fraction (PDFF) ≥5%, which served as the noninvasive reference standard. Diagnostic performance metrics, receiver operating characteristic (ROC) analyses, and correlations with serum fibrosis indices (FIB-4 and APRI) were assessed. Results: MRI-PDFF identified hepatic steatosis in 21 donors (19.4%). Controlled attenuation parameter (CAP), measured by transient elastography, demonstrated high sensitivity (90.5%) and negative predictive value (97.1%), supporting its role as a rule-out screening tool. CT showed excellent specificity (97.7%) but lower sensitivity (61.9%), consistent with a confirmatory role when MRI is unavailable. Serum fibrosis indices were generally low and did not correlate strongly with imaging-based steatosis. Conclusions: In the low-prevalence setting of living liver donor evaluation, CAP-based transient elastography provides effective noninvasive screening for hepatic steatosis, while MRI-PDFF serves as a confirmatory reference when indicated. These findings support a stepwise, clinically practical diagnostic approach that prioritizes donor safety and workflow efficiency.

Background Gestational diabetes mellitus (GDM) remains a major obstetric concern, yet the optimal screening strategy and the prognostic value of oral glucose tolerance test (OGTT) parameters remain debated. We aimed to compare the diagnostic yield and clinical outcomes of a two-step OGTT strategy (50 g glucose challenge followed by 100 g OGTT) versus a one-step 75 g OGTT approach, and to evaluate the predictive performance of individual OGTT time points for pregnancy complications and treatment requirement. Methods In this prospective randomized controlled trial, 1,439 pregnant women undergoing routine screening at 24–28 weeks of gestation were randomized to either a two-step OGTT strategy (n=719) or a one-step 75 g OGTT strategy (n=720). GDM was classified as diet-controlled or insulin-requiring. Maternal risk factors, obstetric outcomes, and neonatal outcomes were recorded. Receiver operating characteristic (ROC) analyses assessed the predictive ability of OGTT parameters for polyhydramnios and insulin requirement. Results Overall GDM prevalence was 12.3%, including 8.4% diet-controlled and 3.9% insulin-requiring cases. The one-step strategy identified a numerically higher proportion of GDM without significant differences in maternal or neonatal outcomes compared with the two-step approach. Rates of polyhydramnios, hypertensive disorders, macrosomia, cesarean delivery, preterm birth, neonatal intensive care admission, small for gestational age (7.4%), and intrauterine growth restriction (4.2%) were comparable between groups. ROC analyses demonstrated that 2-hour OGTT values showed the strongest predictive performance for polyhydramnios (AUC up to 0.816) and insulin requirement (AUC up to 0.808), whereas the 50 g screening test showed only moderate discrimination. Conclusion The one-step 75 g OGTT increases diagnostic labeling without improving short-term clinical outcomes. Post-load OGTT values—particularly 2-hour glucose levels—provide the most clinically meaningful prognostic information and may support a risk-stratified approach to GDM management rather than expansion of diagnostic thresholds alone.

The main aim of this study is to evaluate value of maternal serum nuclear factor erythroid 2-related factor 2 (NRF2) levels at 12weeks in predicting the development of gestational diabetes mellitus (GDM) at 24-28weeks' gestation. Other maternal variables were also evaluated, and their relationships with NRF2 levels were assessed. We conducted a single-center prospective cohort study including 1,270 pregnant women who attended their first-trimester antenatal visit between October 2021 and October 2023. At 12weeks, fasting serum was collected to measure NRF2 by enzyme-linked immunosorbent assay (ELISA) and thyroid hormones on an automated chemiluminescent platform. Clinical data included age, pre-pregnancy body mass index (BMI), blood pressure, fasting plasma glucose, and lipids. All participants underwent a 75-g oral glucose tolerance test (OGTT) at 24-28weeks and GDM was diagnosed by International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria. Spearman correlation assessed associations between NRF2 and clinical variables. Receiver operating characteristic (ROC) analysis evaluated discrimination. Multivariable logistic regression identified independent predictors. Among 1,270 pregnant women enrolled, 177 (13.9%) developed GDM. At 12weeks of gestation, women who later developed GDM had significantly lower serum NRF2 levels than those without GDM. They also showed higher fasting plasma glucose (FPG), mean arterial pressure (MAP), as well as lower free thyroxine (FT4) levels. ROC analysis demonstrated that serum NRF2 at 12weeks had good predictive value for GDM, with an area under the curve (AUC) of 0.779 (95% confidence interval [CI] 0.745-0.812, p < 0.001). A combined model incorporating NRF2 with MAP, FT4, and FPG showed improved discrimination, with an AUC of 0.882 (95% CI 0.856-0.908, p < 0.001). In multivariable logistic regression, lower serum NRF2 (odds ratio [OR] = 0.948, 95% CI 0.938-0.957, p < 0.001), higher MAP (OR = 1.182, 95% CI 1.132-1.233, p < 0.001), higher FPG (OR = 3.911, 95% CI 2.471-6.190, p < 0.001), and lower FT4 (OR = 0.639, 95% CI 0.562-0.726, p < 0.001) were identified as independent predictors of GDM, whereas other baseline parameters were not significant. Decreased maternal serum NFR2 levels at 12weeks' gestation appear to be associated with an increased risk of developing GDM later in pregnancy. Combining this marker, together with FT4, MAP and potentially FPG may serve as a useful first-trimester screening for the risk to develop GDM.

18F-Fibroblast activation protein inhibitor ([18F]FAPI) positron emission tomography (PET)/CT is an emerging tool for detecting IgG4-related disease (IgG4-RD). However, standardised quantitative analysis and image scoring present ongoing challenges in patients with IgG4-RD. To establish organ-specific reference thresholds for IgG4-RD involvement on [18F]FAPI PET/CT images, and to develop a novel, clinically applicable scoring system to monitor disease activity in patients with IgG4-RD. This retrospective study recruited 85 patients with IgG4-RD and 10 healthy individuals, all of whom underwent [18F]FAPI PET/CT. Organ-specific uptake thresholds were defined as [18F]FAPI uptake greater than the maximal standardised uptake value +3 SD in the corresponding anatomical regions of healthy individuals. Moreover, we developed a novel method, [18F]FAPI PET/CT Activity Score of IgG4-RD (FPAS-IgG4), for scoring disease activity using a logistic regression model with receiver operating characteristics curve analysis. Among 85 patients with IgG4-RD, 64 lesions suspected of IgG4-RD involvement were biopsied and 58 (90.6%) of these lesions were positive for IgG4. [18F]FAPI PET/CT achieved a sensitivity, specificity and accuracy of 86.2%, 83.3% and 85.9%, respectively, for detecting IgG4-RD based on organ-specific uptake thresholds. The active disease group had a higher FPAS-IgG4 than the inactive disease group (5.2±3.0 vs 1.7±1.3, p<0.0001). FPAS-IgG4 >2 showed a sensitivity of 93.2% and a specificity of 73.1% in distinguishing between active and inactive disease. Multivariable logistic regression analysis demonstrated that FPAS-IgG4 was strongly associated with active IgG4-RD (p=0.001). [18F]FAPI PET/CT could be a valuable tool for monitoring the activity of IgG4-RD, aiding in disease stratification.

Frail older adults with coronary heart disease (CHD) face significantly elevated risks of adverse clinical outcomes, including mortality, prolonged hospitalizations, and frequent readmissions. Conventional risk stratification tools, inadequately account for frailty and multi-morbidity, limiting their effectiveness in geriatric care. To address this gap, we developed and validated the first machine learning (ML) model that integrates frailty into a multi-outcome risk assessment framework, thereby enhancing clinical decision-making in geriatric cardiology. Utilizing electronic health records from hospitalized frail CHD patients, we developed a multinomial prediction model employing advanced ML techniques, including principal component analysis, gradient boosting, and random forest. The model incorporates explainable AI features to enhance interpretability and a clinical applicability, prioritizing key predictors such as biomarkers and comorbidities. The ML model demonstrated superior predictive performance with Receiver Operating Characteristic (ROC) curve analysis (Area Under the Curve [AUC] 0.94, 95% CI 0.88-1.00) for mortality, 0.72 (95% CI: 0.55-0.87) readmission and 0.68 (95% CI: 0.57-0.77) prolonged hospital stay, enabling earlier risk identification and personalized intervention strategies. This AI-driven approach represents a significant advancement in geriatric cardiology designed for integration into hospital dashboards, providing real-time patient-centred decision support, optimization of clinical workflow and resource allocation. By advancing digital health solutions and AI driven precision medicine this model sets a new standard for digital health innovations in aging care.

Introduction: Mutations in ASXL1 represent one of the most frequent additional lesions in myelofibrosis (MF). Although the prognostic significance of ASXL1 mutations is well recognized, the role of ASXL1 variant allele frequency (VAF) as a potential modulator of genetic heterogeneity and clonal complexity in MF has not yet been fully elucidated. This study aims to explore whether ASXL1 VAF correlates with overall mutational burden and to identify a potential cut-off with biological and clinical relevance.Methods: We analyzed a cohort of 53 ASXL1-mutated myelofibrosis (MF) samples, including 6 early-PMF, 33 overt PMF, and 14 SMF cases. For each sample, targeted next-generation sequencing (NGS) was performed using a 73-gene myeloid panel with a minimum VAF detection threshold of 3%. For statistical analysis, only pathogenic and likely pathogenic mutations were considered. A comparison between ASXL1 VAF and the number of mutations or the number of pathways involved were performed using the Mann–Whitney test for quantitative variables and Fisher’s exact test for qualitative variables. A receiver operating characteristic (ROC) curve was generated using the free Jamovi software to identify the optimal ASXL1 VAF cut-off predictive of higher genetic complexity.Results: Samples with low ASXL1 VAF predominantly harbored two mutations, typically driver mutation in combination with ASXL1 (VAF median, 14.0%; VAF mean ± SD, 17.1 ± 13.8%). Conversely, samples with higher ASXL1 VAF exhibited a significantly greater mutational burden, generally &gt;2 variants - mean 3.92 (VAF median, 34.0%; VAF mean ± SD, 28.1 ± 15.6%) [Figure 1 – Table 1], frequently involving high-molecular-risk (HMR) genes such as EZH2, U2AF1 and SRSF2, as well as non-HMR genes like NRAS. ROC curve analysis identified an ASXL1 VAF of 19.70% as the optimal cutoff for discriminating cases with more than two variants or more than two functional classes involved (AUC = 0.714; sensitivity, 69.4%; specificity, 66.7%) [Figure 2 – Table 2]. These findings suggest a quantitative relationship between ASXL1 VAF and genomic complexity in myelofibrosis.Conclusions: Our data indicate that increasing ASXL1 VAF is associated with higher clonal complexity and the involvement of multiple functional pathways in myelofibrosis. This pattern may reflect an expansion of genetically heterogeneous subclones driven by chromatin dysregulation, leading to the loss of gene repression and contributing to clonal evolution, an effect that appears more pronounced as the size of the mutant ASXL1 clone increases. These findings support the concept that the allelic burden of ASXL1 could serve as a quantitative biomarker of genomic heterogeneity and disease evolution in myelofibrosis, potentially aiding future risk stratification and clinical decision-making.

This study aimed to evaluate the predictive value of quantitative gadobenate dimeglumine-enhanced MRI parameters in aggressiveness and prognosis of intrahepatic mass-forming cholangiocarcinoma (IMCC). A total of 158 patients with IMCC who underwent preoperative MRI at three centers were included, and their clinical and imaging data were analyzed retrospectively. Multimodal quantitative parameters were measured in various tumor areas, including relative intensity ratio (RIR) and relative enhancement ratio (RER) of the central and rim areas of the tumor to the liver in the hepatobiliary phase, and the center area-tumor volume ratio. Patients were classified into low-aggressiveness (Ki-67 LI < 25%) and high-aggressiveness (Ki-67 LI ≥ 25%) groups based on the Ki-67 labeling index (LI). Potential risk factors of aggressiveness were determined using multivariate logistic regression analysis. The prediction efficacy of factors was assessed using receiver operating characteristic (ROC) curves. Overall survival (OS) and disease-free survival (DFS) were evaluated using the Cox proportional-hazards regression model. The volume ratio (VR) and RIRrim were independent risk factors for aggressiveness (p < 0.05). The area under the ROC curve was 0.803 [95% confidence interval (CI), 0.728-0.878] and 0.799 (95% CI, 0.727-0.872), both higher than that of CA19-9 ≥ 34 U/mL and intratumoral necrosis (all, p < 0.05). VR and RIRrim were identified as independent predictors of OS and DFS in patients with IMCC (p < 0.05). The multimodal quantitative MRI parameters, VR and RIRrim, were effective risk factors for predicting both aggressiveness and prognoses in patients with IMCC. Noninvasive MRI hepatobiliary-phase quantification stratified aggressiveness and prognosis in intrahepatic mass-forming cholangiocarcinoma. It might provide important clinical information for treatment strategies. The volume ratio (VR), relative intensity ratio (RIRrim), CA19-9 ≥ 34 U/mL, and necrosis were independent predictors of high aggressiveness. The VR, RIRrim, CA19-9 ≥ 34 U/mL, and tumor boundary were independent predictors of poorer overall survival. The VR, RIRrim, CA19-9 ≥ 34 U/mL, tumor boundary, and tumor maximum size ≥ 3 cm were independent predictors of shorter disease-free survival.

Background: Multiple organ dysfunction syndrome (MODS) remains a predominant cause of mortality in Pediatric Intensive Care Units (PICUs). While the Pediatric Logistic Organ Dysfunction-2 (PELOD-2) score is the established standard for assessing severity, it requires time-consuming serial calculations. There is an urgent need for a rapid, admission-based prognostic biomarker. This study evaluates the association between serum procalcitonin (PCT) and the severity of organ dysfunction in critically ill children. Methods: A prospective cross-sectional study was conducted at Dr. Moewardi Regional General Hospital, Indonesia, involving 25 children aged 1 month to 18 years with suspected infection. Organ dysfunction was quantified using the PELOD-2 score, and serum PCT was measured via Enzyme-Linked Fluorescent Assay (ELFA) within 24 hours of admission. Statistical analysis utilized Spearman’s rank correlation, multivariate linear regression, and Receiver Operating Characteristic (ROC) curve analysis. Results: The cohort had a median age of 12 months. The median PCT level was 0.88 ng/mL. A significant positive correlation was observed between serum PCT and PELOD-2 scores (r = 0.39, p = 0.051; multivariate beta = 0.42, p = 0.043). ROC analysis identified a PCT threshold of greater than 11 ng/mL as the optimal indicator for moderate-to-severe organ dysfunction (AUC 0.82). Patients exceeding this threshold had a significantly elevated risk (Risk Ratio = 2.20; 95 percent CI: 1.15–4.24; p = 0.035). Conclusion: Early serum procalcitonin measurement serves as a powerful independent factor associated with organ dysfunction severity. A cutoff value of greater than 11 ng/mL significantly stratifies risk, allowing clinicians to anticipate the progression of organ failure.

To evaluate the impact of applying the International Diabetes Federation (IDF) 1-hour plasma glucose (1h-PG) criteria on diagnosing dysglycaemia in young Chinese adults with obesity, and to validate the metabolic basis and diagnostic performance of these criteria in this high-risk population. This Cross-Sectional Study Included 2484 Obese Individuals (Aged 18-40 Years, BMI ≥ 28 kg/m2). Participants underwent a 75-g oral glucose tolerance test (OGTT). Glycaemic status was classified using both traditional American Diabetes Association (ADA) criteria (fasting and 2-hour PG) and the IDF criteria (1h-PG cutoffs: 8.6 mmol/L for intermediate hyperglycaemia, 11.6 mmol/L for diabetes). Insulin sensitivity, secretion and β-cell function indices were calculated. Reclassification patterns were assessed, and the diagnostic accuracy of the 1h-PG cutoffs was evaluated using receiver operating characteristic (ROC) curve analysis against the ADA criteria. Applying IDF criteria doubled the prevalence of diabetes (from 12.0% to 22.7%) and significantly increased total dysglycaemia. This newly identified dysglycaemic population exhibited an intermediate metabolic phenotype with worsening insulin resistance and impaired early-phase β-cell function. ROC analysis demonstrated excellent diagnostic accuracy for the IDF diabetes cutoff (11.6 mmol/L; AUC 0.927), which matched the population-optimal cutoff. The cutoff for intermediate hyperglycaemia (8.6 mmol/L) showed high sensitivity (86.8%) for detecting ADA-defined prediabetes. The IDF 1h-PG criteria uncover a substantial, previously unrecognised burden of dysglycaemia in young Chinese adults with obesity, supported by clear metabolic defects and strong diagnostic performance. These findings support the use of 1h-PG as a practical tool for earlier risk stratification in this vulnerable population.

Background Despite the success of antiretroviral therapy (ART) for human immunodeficiency virus (HIV) in China, immune non-response (INR) remains critical for the long-term quality of life of people living with HIV (PLWH). Although the consensus on diagnosis and management of immunological non-responders in HIV infection (Version 2023) was published in China to standardize diagnostic criteria, prediction models for INR based on these criteria remain scarce. This study aims to develop and validate a nomogram model for early identification of INR risk based on the diagnostic criteria in this consensus, so as to facilitate clinical intervention. Methods In this retrospective study, the primary cohort included 615 PLWH who initiated ART and completed over 4 years of follow-up at Zhongnan Hospital of Wuhan University (January 2016 to May 2025). They were randomly split into a training set (n=433) and an internal validation set (n=182) in a 7:3 ratio. An external validation set comprised 213 PLWH from Xishui County People’s Hospital (January 2012 to August 2025). Least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to identify independent predictors of INR, and a nomogram was constructed. The receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to evaluate the discrimination, calibration, and clinical utility of the model in the training set and validation sets, respectively. Results LASSO regression identified seven candidate variables: age at ART initiation, baseline CD4 + T cell count, body mass index (BMI), white blood cell count (WBC), hemoglobin (Hb), aspartate aminotransferase (AST), and World Health Organization (WHO) clinical stage. Subsequent multivariable logistic regression confirmed baseline CD4 + T cell count (p &amp;lt; 0.001), age at ART initiation (p = 0.001), and AST level (p = 0.014) as independent INR predictors. The resulting nomogram demonstrated area under the curve (AUC) values of 0.896, 0.903, and 0.766 in the training, internal validation, and external validation sets, respectively. The calibration curve and DCA further indicated satisfactory consistency and clinical net benefit. Conclusion The developed nomogram effectively predicts INR risk in PLWH initiating ART, providing clinicians a practical tool for individualized management to improve patient prognosis.

Prognostic value of hemodynamic indices prior to tMCS explantation: An analysis from the Cardiogenic Shock Working Group.

Development and Validation of a Risk Prediction Model for Postoperative Pulmonary Infection in Renal Transplant Patients.

Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoperative risk stratification remains limited, and radiomics may provide non invasive imaging biomarkers to support prognosis.&#xD;&#xD;Objective: This study aimed to develop a CT based radiomics signature capable of generating risk scores for predicting overall survival in patients undergoing CRLM resection.&#xD;&#xD;Methodology: Preoperative CT scans from 197 CRLM patients were retrospectively obtained from The Cancer Imaging Archive. A total of 851 radiomics features were extracted using 3D Slicer, along with 256 deep features from a 3D convolutional neural network. Data were randomly divided into training (70%) and testing (30%) sets. Feature selection included correlation-based filtering, univariate Cox regression, and multivariate Cox regression with LASSO regularization. A radiomics-based risk score (Rad score) was calculated to stratify patients into high- and low- risk groups using the median value. Model performance was compared with clinical variables using time-dependent receiver operating characteristic (ROC) analysis.&#xD;&#xD;Results: The eight feature signature was prognostic in both internal splits. In multivariable Cox models, the Rad score remained an independent predictor of overall survival in the training cohort (HR = 2.671, 95% CI 2.023 to 3.527, P-value < 0.001; Harrell C index = 0.738, 95% CI 0.678 to 0.793) and the testing cohort (HR = 3.036, 95% CI 1.421 to 6.486, P-value = 0.004; Harrell C index = 0.663, 95% CI 0.558 to 0.752). Kaplan Meier analysis showed shorter survival in the high risk group than the low risk group in training (median overall survival 56.5 versus 76.00 months; log rank P-value = 0.0000) and testing (61.45 versus 74.25 months; log rank P-value = 0.0103). In an external cohort of 105 patients, the Rad score also separated risk (Harrell C index = 0.614, 95% CI 0.534 to 0.697; median overall survival 15.7 versus 29.87 months; log rank P-value = 0.0015).&#xD;&#xD;Conclusion: A compact CT radiomics signature derived from preoperative imaging provided independent prognostic information for overall survival and enabled risk stratification in internal testing and external validation. Further validation in independent colorectal liver metastases cohorts is required before clinical deployment.

BACKGROUND Hip fractures in geriatric patients carry high morbidity and mortality due to advanced age, frailty, and multiple comorbidities. Accurate preoperative risk assessment is therefore essential. The hemoglobin-albumin-lymphocyte-platelet (HALP) score and prognostic nutritional index (PNI) are emerging immunonutritional biomarkers reflecting inflammatory and nutritional status. This study aimed to evaluate and compare the prognostic value of preoperative HALP and PNI scores for predicting 6-month mortality and postoperative complications in elderly hip fracture patients. MATERIAL AND METHODS This retrospective cohort included 549 patients aged≥³65 years who underwent surgical repair of proximal femoral fractures between January 2021 and July 2024. Demographic characteristics, comorbidities, fracture type, and preoperative laboratory data were analyzed. HALP and PNI scores were calculated from admission blood tests. Independent predictors of 6-month all-cause mortality were identified using Cox regression, and receiver-operating characteristic (ROC) analysis determined optimal cut-off values. RESULTS The mean age was 78±9 years, and 51.9% were female. Six-month mortality was 16.4%. Non-survivors had significantly lower HALP and PNI scores (P<0.001). In multivariate Cox analysis, coronary artery disease (HR 2.57, 95% CI 1.66-4.00), postoperative complications (HR 3.97, 95% CI 2.57-6.15), and lower HALP levels (HR 3.11, 95% CI 1.19-8.13) were independently associated with mortality. Additionally, ROC analysis identified a HALP cut-off value of 0.176 for predicting mortality. CONCLUSIONS The HALP score showed modest prognostic value for 6-month mortality and can complement established clinical predictors. Its use in preoperative evaluation could help identify higher-risk patients, but its discriminatory ability should be interpreted with caution.