Objective:Patients with decompensated hepatitis B virus (HBV)- and hepatitis C virus (HCV)-related cirrhosis are at high risk of first variceal bleeding, yet etiology-specific and stage-specific prediction models remain limited. This study aimed to develop and internally validate nomogram models for predicting the risk of first variceal bleeding in this population. Methods:This retrospective cohort study included 451 patients with decompensated HBV- or HCV-related cirrhosis (331 HBV-related and 120 HCV-related), of whom 230 experienced first variceal bleeding. Random Forest and XGBoost algorithms were used for feature selection, followed by multivariable logistic regression to develop etiology-specific nomogram models. Internal validation was performed using a training-testing split. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), bootstrap resampling, and decision curve analysis, respectively. Partial Dependence Plots (PDPs) midpoint risk (≈0.5) set clinical thresholds. Results:Seven predictors were retained for HBV (Hemoglobin, Ascites Depth, Total Protein, Prothrombin Activity [PTA], Total Bile Acid [TBA], D-dimer, and White Blood Cell Count [WBC]) and three for HCV (Hemoglobin, PTA, and TBA), with an AUC of 0.9239 (95% CI 0.8941-0.9537) and 0.9127 (95% CI 0.8646-0.9609), respectively. The models showed good calibration and clinical utility on decision curve analysis. PDPs indicated 0.5-risk thresholds at 96.12 g/L (Hemoglobin), 60.75 g/L (Total Protein), 30.58 mm (Ascites Depth), 49.2% (PTA), 25.78 µmol/L (TBA), 2.24 mg/L (D-dimer), and 5.33×109/L (WBC) for HBV, and 46.81% (PTA), 82.4 g/L (Hemoglobin), 47.51 µmol/L (TBA) for HCV. Conclusion:Etiology-specific nomogram models were developed to predict first variceal bleeding in patients with decompensated HBV- and HCV-related cirrhosis. The models showed good discriminative performance in internal validation and may assist in risk stratification. However, further prospective multicenter studies with external validation are needed before clinical implementation.
Background:Chronic kidney disease (CKD) is a major public health issue in China, especially among older adults, with rising prevalence linked to metabolic disorders such as hyperglycemia. This study aimed to assess the burden of CKD attributable to elevated fasting blood glucose (FBG) in elderly Chinese, focusing on the predictive value of composite metabolic indices derived from the triglyceride-glucose (TyG) index. Method:The study included two phases: (1) analysis of the 2023 Global Burden of Disease (GBD) database to assess the CKD burden due to hyperglycemia among adults aged ≥ 60 years in China from 1990 to 2023 and (2) evaluation using the China Health and Retirement Longitudinal Study (CHARLS) cohort, analyzing associations between 15 blood glucose-related indices and CKD in older adults. Results:The GBD analysis revealed a significant burden of CKD due to hyperglycemia in China, with increasing mortality and disability-adjusted life years (DALYs) projections. In the CHARLS cohort, four indices-TyG-HDL, TCBI, FBG-HDL, and CTI-were significantly associated with CKD. Elevated levels of these indices, particularly TyG-HDL, demonstrated strong associations with CKD risk, with nonlinear, threshold-dependent effects observed in restricted cubic spline analyses. Conclusion:Blood glucose-related indices, especially TyG-HDL, offer strong predictive value for CKD risk in older Chinese adults. These findings suggest that integrating these indices into screening programs could enhance early CKD detection and management strategies, helping mitigate the growing disease burden in aging populations.
Background:Cervical spondylosis frequently recurs after conservative treatment, but its predictors remain unclear. This study aimed to identify factors associated with recurrence and develop a predictive nomogram. Methods:This retrospective analysis used data from a multicenter cross-sectional survey (six Chinese cities, March-November 2025). We identified 668 patients who reported remission after systematic conservative treatment and retrospectively collected their post-treatment recurrence status via self-report. Potential predictors included demographics, lifestyle, clinical history, and radiographic findings. The sam ple was randomly split into training (70%) and validation (30%) sets. Multivariable logistic regression identified independent predictors, and a nomogram was constructed. Model performance was evaluated using AUC, calibration, and decision curve analysis. Supplementary analyses examined episode frequency and time to recurrence. Results:Six independent predictors were identified: exercise duration per session (OR=0.992), neck length (OR=0.850), disc space narrowing (OR=0.478), disease duration (overall P<0.001), pre-treatment attack frequency (overall P<0.001), and PCS score (OR=0.943). Compared with disease duration <3 months, 3-11 months increased risk (OR=2.691); pre-treatment attacks ≥6/month raised risk (OR=4.608). The nomogram showed good discrimination (training AUC=0.760) and net benefit. Longer exercise was associated with delayed recurrence, while longer disease duration and frequent prior episodes increased recurrence risk; disc narrowing paradoxically predicted lower recurrence. Conclusion:Post-treatment recurrence is associated with multifactorial factors, including modifiable elements like exercise and physical health, as well as disease chronicity. Imaging changes may prompt protective behaviors. These findings support individualized risk stratification, but causal inference requires prospective validation.
Background:Hypertension is frequently accompanied by metabolic abnormalities that increase cardiovascular risk. The triglyceride-glucose (TyG) index is a practical surrogate marker of insulin resistance. This study aimed to compare TyG index values and related metabolic parameters between patients receiving RAAS blockers plus thiazide/thiazide-like diuretics and those receiving RAAS blockers plus calcium channel blockers (CCBs) as antihypertensive regimens. Methods:In this retrospective observational study, 157 of 189 screened patients met eligibility criteria and were included (RAAS blocker plus thiazide/thiazide-like diuretic, n=95; RAAS blocker plus CCB, n=62). Multivariable linear regression models and inverse probability of treatment weighting (IPTW) analyses were performed to assess the independence of findings from clinical confounders. Results:The TyG index was significantly higher in the diuretic group than in the CCB group [9.21 (9.01-9.73) vs 8.95 (8.66-9.40); p<0.001], as were triglyceride [186.00 vs 142.35 mg/dL; p<0.001] and uric acid levels [5.70 vs 5.00 mg/dL; p=0.004]. Blood pressure control rates were similar. The treatment group effect remained significant across all adjusted models, including IPTW-weighted regression (β=0.385, p=0.0003). A significant treatment group × diabetes mellitus interaction was observed (p=0.042), suggesting a stronger association in diabetic patients. A dose-stratified analysis within the diuretic group showed a significant dose-response relationship: higher hydrochlorothiazide (25 vs 12.5 mg/day) and indapamide (2.5 vs 1.25 mg/day) doses were both associated with significantly higher TyG index, fasting glucose, and uric acid levels. In an exploratory diuretic subgroup analysis, urea and BUN levels were higher with hydrochlorothiazide than indapamide; however, this analysis was underpowered (indapamide n=24) and should be interpreted with caution. Conclusions:RAAS blocker plus thiazide/thiazide-like diuretic regimens were associated with a significantly higher TyG index and a less favorable metabolic profile than RAAS blocker plus CCB regimens, independent of multiple clinical confounders. Metabolic risk profiles should be considered when selecting antihypertensive combination therapy; prospective studies are needed to establish causality.
Background:Remote mountainous areas face severe challenges regarding medical resource scarcity and delayed emergency treatment. While the "Walking Hospital" model has physically descended medical hardware to the village level, the efficacy of these interventions is bottlenecked by the limited diagnostic capabilities of grassroots doctors. Methods:This narrative review synthesizes literature identified through searches in PubMed, IEEE Xplore, and Web of Science, focusing on articles published between January 2017 and March 2026. Keywords included "mobile health", "artificial intelligence", "telemedicine", "rural health", "drone logistics", and "edge computing". We prioritized peer-reviewed articles, clinical trials, and policy analyses relevant to resource-limited settings. Due to the heterogeneity of study designs and the emerging nature of the topic, a formal meta-analysis was not conducted; instead, a qualitative synthesis of technological models and operational frameworks is presented. Results:The review finds that the deep integration of edge computing, natural language processing (NLP), and computer vision (CV) empowers village doctors with specialist-level diagnostic capabilities offline. Technically, lightweight AI models enable real-time ECG interpretation and ultrasound guidance in network dead zones. Operationally, a closed-loop ecosystem integrating Low-Earth-Orbit (LEO) satellite communications, medical drone logistics, and county-level medical consortia is identified as a sustainable framework. Global case studies from Rwanda, India, and Australia validate the feasibility of AI-optimized aerial logistics and edge-based diagnostics in resource-limited settings. However, critical barriers remain, including algorithmic generalization deficits (domain shift) and ambiguous liability frameworks. Conclusion:AI-empowered "Walking Hospitals" represent a paradigm shift from hardware distribution to capability enhancement. Future research must prioritize resolving domain shift through techniques like Federated Learning, establishing sustainable reimbursement models, and developing community-level data governance frameworks to transition these innovations from pilot projects to scalable global solutions.
Plasma cell mastitis (PCM) is a predominant form of non-lactational mastitis with an increasing incidence worldwide. The condition is characterized by a complex pathogenesis, difficulties in differential diagnosis, and a high rate of recurrence, thereby presenting significant challenges in clinical management. Conventional Western medicine is limited by high postoperative recurrence, difficulty in distinguishing PCM from breast cancer, and unsatisfactory long-term efficacy of single anti-inflammatory or hormone therapy. Against these clinical bottlenecks, Traditional Chinese medicine (TCM) shows unique advantages and accumulated extensive clinical experience for the treatment of PCM. This review systematically delineates the epidemiological characteristics and key pathophysiological mechanisms, including activation of the IL-6/STAT3 signaling pathway, immune cell dysregulation, autoimmune responses, distinctive plasma cell infiltration, and diagnostic methodologies such as imaging techniques, histopathological examination as the diagnostic gold standard, and emerging biomarkers. In addition, the theoretical foundations, contemporary pharmacological mechanisms, and clinical applications of comprehensive TCM therapy for PCM are summarized. Evidence indicates that TCM interventions, guided by syndrome differentiation and targeting liver qi stagnation, phlegm-stasis congealing, and heat-toxin accumulation, exert therapeutic effects by modulating inflammatory signaling pathways, correcting immune dysfunction, and restoring the mammary gland microecological balance. Clinical trials have demonstrated that combining TCM with surgical procedures or western medical treatments significantly improves clinical outcomes, reduces inflammatory mediator levels, and lowers recurrence rates. However, there remains a need for standardization in TCM syndrome differentiation, therapeutic evaluation criteria, and high-quality randomized controlled trials. In summary, integrated TCM therapy constitutes a safe and effective approach for managing PCM, and further rigorous research is warranted to enhance its clinical application and facilitate international recognition of TCM in PCM treatment.
Objective:To investigate serum myeloperoxidase-DNA (MPO-DNA) and C-C motif chemokine ligand 26 (CCL26) levels in pediatric multidrug-resistant organism (MDRO)-associated pneumonia and their association with 28-day prognosis. Methods:In this single-center prospective cohort study, 220 pediatric patients hospitalized for MDRO-associated pneumonia (February 2022-February 2025) were enrolled. Serum MPO-DNA, CCL26, C-reactive protein (CRP), and procalcitonin (PCT) were measured by ELISA within 24 h of admission. Patients were classified into good-prognosis (clinical improvement, ≥50% pulmonary lesion absorption, no severe complications) and poor-prognosis (treatment failure, severe complications, or death within 28 days) groups. Multivariable logistic regression and ROC analysis were performed. Results:Of 220 patients, 68 (30.9%) had poor prognosis. The poor-prognosis group showed significantly elevated CRP, PCT, MPO-DNA, and CCL26 (all P < 0.05). MPO-DNA and CCL26 were positively correlated (r = 0.507, P < 0.001). Multivariable analysis indicated elevated CRP (OR = 1.714, 95% CI: 1.389-2.116), PCT (OR = 1.739, 95% CI: 1.497-2.021), MPO-DNA (OR = 1.007, 95% CI: 1.003-1.011), and CCL26 (OR = 1.002, 95% CI: 1.001-1.004) as independent risk factors (all P < 0.05). ROC analysis showed AUC values for MPO-DNA, CCL26, and their combination of 0.799, 0.816, and 0.872, respectively, with the combined model significantly superior to either alone (all P < 0.05). Conclusion:Elevated serum MPO-DNA and CCL26 levels are associated with 28-day poor prognosis in pediatric MDRO pneumonia. Combined detection shows favorable discriminative performance. However, given the single-center design and lack of external validation, clinical utility requires further multicenter confirmation.
Background:Inflammatory chemokines may participate in the progression of diabetic kidney disease (DKD). However, the clinical value of macrophage inflammatory protein-1β (MIP-1β) for identifying macroalbuminuria in DKD remains insufficiently defined. This study aims to construct a nomogram-based prediction model to evaluate MIP-1β level in predicting DKD progression. Methods:In this prospective, single-center observational study, 198 DKD patients and 198 type 2 diabetes mellitus patients without DKD were consecutively recruited from July 2021 to July 2023. DKD patients were stratified into microalbuminuria (A2, n=146) and macroalbuminuria (A3, n=52) groups. Multivariate logistic regression identified risk factors for macroalbuminuria. A nomogram incorporating significant variables was constructed and internally validated using bootstrap method. Model performance was evaluated via receiver operating characteristic (ROC) analysis and decision curve analysis. Results:MIP-1β levels were significantly higher in the DKD group than non-DKD group (78.88±21.18 vs 67.75±16.25 pg/mL, P<0.001). For predicting DKD, MIP-1β had an area under the ROC curve of 0.711 (95% CI: 0.661-0.762), with 62.6% sensitivity and 74.2% specificity. Independent risk factors for macroalbuminuria included MIP-1β (adjusted odds ratio=1.089, 95% CI: 1.052-1.127), urea nitrogen (1.694, 95% CI: 1.142-2.513), and cystatin C (7.728, 95% CI: 1.843-32.400). The nomogram incorporating these predictors achieved 88.5% sensitivity and 91.1% specificity, with C-index of 0.852 and good calibration. Conclusion:MIP-1β level is independently associated with macroalbuminuria in DKD patients. The nomogram model demonstrates high predictive value for macroalbuminuria and may assist risk stratification in DKD patients; however, external validation is required.
Background:Carotid plaque calcification is an active multicellular process with heterogeneous clinical implications. However, endothelial cell (EC) heterogeneity and plaque-region-specific EC states associated with calcified lesions remain incompletely characterized. Methods:We performed an exploratory integrative analysis of the public single-cell RNA sequencing dataset GSE159677, comprising paired calcified core (AC) and proximal adjacent (PA) tissues from three patients, together with a single-center proteomic cohort of three additional patients with paired AC and PA samples. Major plaque cell populations and EC subclusters were identified by unsupervised clustering and canonical markers. Calcium signaling activity, pathway enrichment, ligand-receptor communication, and Monocle2 pseudotime trajectories were analyzed. Transcriptomic findings were compared with differentially expressed proteins to identify cross-omics candidate molecules. Results:A total of 35,890 cells were classified into seven major cell types. AC and PA tissues showed distinct cellular compositions and signaling patterns. Re-clustering of 4,925 ECs identified six subclusters, including a calcium signaling-high EC cluster enriched for extracellular matrix organization, inflammatory signaling, cytoskeletal regulation, and endothelial-to-mesenchymal transition-related programs. CellChat analysis indicated plaque-region-specific communication networks involving ECs, immune cells, fibroblasts, and smooth muscle cells. Pseudotime analysis suggested heterogeneous EC state transitions rather than a definitive longitudinal progression. Cross-omics comparison identified eight candidate molecules, FABP4, FABP5, MYL12A, POSTN, S100A10, SERPINB1, SOD2, and TMSB10, with concordant changes across transcriptomic and preliminary proteomic analyses. Conclusion:These exploratory findings characterize plaque-region-specific EC heterogeneity associated with carotid plaque calcification and nominate candidate pathways and molecules for further validation in larger cohorts and functional models.
Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive form of metabolic dysfunction-associated steatotic liver disease (MASLD) characterized by steatosis, inflammation, hepatocellular injury, and fibrosis. The pathogenesis of MASH is complex and involves multiple factors, such as lipotoxicity, insulin resistance (IR), genetic susceptibility, endoplasmic reticulum (ER) stress, mitochondrial dysfunction, and dysregulation of the hepatic immune microenvironment. Currently, the clinical treatment for MASH remains challenging. Traditional Chinese medicine (TCM) has shown broad prospects in the prevention and treatment of MASH owing to its multi-target and multi-pathway regulation. This review summarizes the pathophysiological basis of MASH relevant to TCM intervention and discusses the effects of TCM formulas, active compounds, and comprehensive treatment strategies on lipid metabolism, IR, inflammation, autophagy and ferroptosis, intestinal barrier function, immune regulation, fibrosis, and clinical outcomes. We also discuss current clinical evidence, safety considerations, and remaining limitations, with the aim of providing a clearer basis for the clinical application and further investigation of TCM in MASH management.
Objective:Traumatic spinal cord injury (TSCI) is a severe neurological urgency. Neuritin is a neurotrophic factor. This study aimed to explore prognostic predictive value of serum neuritin levels in TSCI. Methods:In this prospective cohort study, serum neuritin levels were quantified at admission of 126 patients and at study entry of 126 controls. The American Spinal Injury Association Impairment Scale (AIS) was used as the severity index. Poor prognosis was defined as no improvement in AIS grade at six months post TSCI versus admission. The results were analyzed using multivariate regression method. Results:Serum neuritin levels were significantly higher in patients with TSCI than in controls. No significant departure from linearity was detected between serum neuritin levels, AIS grade and poor prognosis in the restricted cubic spline. AIS grade was independently correlated with serum neuritin levels. Age, AIS grade, and serum neuritin levels were independently associated with poor prognosis. Receiver operating characteristic (ROC) curve analysis showed that poor prognosis was effectively predicted by serum neuritin levels. The results of the regression analysis were robust via sensitivity analysis, variance inflation factor estimation, the Hosmer-Lemeshow test, and Brier score calculation. The prognosis model incorporating AIS grade, age, and serum neuritin levels was visualized by nomogram, had satisfactory goodness of fit under the calibration curve, showed substantially high discrimination efficiency through the ROC curve approach, and displayed good clinical validity as demonstrated by the decision curve through internal validation. Serum neuritin levels partially mediated the association between AIS grade and poor prognosis. Conclusions:Elevated serum neuritin levels are closely associated with TSCI severity and poor prognosis following TSCI, indicating that serum neuritin may serve as a potential prognostic biomarker of TSCI.
Childhood asthma is a common but biologically heterogeneous disease, and this heterogeneity limits the performance of one-size-fits-all biomarkers for diagnosis, risk stratification, and disease monitoring. Microbiome and metabolome profiling are attractive in pediatric asthma because they reflect host-environment interactions at mucosal surfaces and may capture clinically relevant variation not fully explained by conventional markers. However, their translational value in children remains uncertain. This review critically examines the current evidence on microbiome- and metabolome-based biomarkers in childhood asthma from a clinically oriented perspective, with emphasis on four settings of practical relevance: early-life risk and disease development, allergic and non-allergic asthma, severe, uncontrolled, or exacerbation-prone disease, and lung-function or inflammatory phenotypes. Current data suggest that composite and phenotype-linked signatures are more informative than isolated taxa or single metabolites. The most convincing signals arise in early-life microbial maturation trajectories and in unstable disease, where upper-airway microbial patterns and integrated metabolic profiles show the greatest potential for clinical stratification. Allergic burden appears to be reflected more consistently by metabolomic than microbiome findings, whereas lung-function and inflammatory phenotypes currently show stronger metabolite-trait associations than reproducible airway microbial correlates. Across phenotypes, pathway-level convergence is more robust than single-marker reproducibility, with recurring signals involving microbial fermentation and short-chain fatty acid biology, bile acid metabolism, tryptophan and histamine pathways, and lipid remodeling. Nevertheless, most pediatric studies remain cross-sectional, modest in size, and heterogeneous in phenotype definitions, sampling matrices, and analytical platforms. No microbiome- or metabolome-based signature is currently ready for routine pediatric clinical use. The most realistic near-term translational direction is the development of age-contextualized, phenotype-oriented reduced panels that are prospectively validated in multicenter cohorts and shown to provide clinical value beyond existing tools for childhood asthma diagnosis, risk stratification, and monitoring.
Purpose:The implication of several microRNAs in ischemic stroke and preclinical studies suggests that targeting these microRNAs could aid stroke recovery. MicroRNA-184 (miR-184) is downregulated in ischemic stroke in rats, and its downregulation contributes to corneal neovascularization. However, it is not clear whether miR-184 plays a part in angiogenesis following brain ischemia-reperfusion. We sought to determine if miR-184 participates in angiogenesis in response to ischemic stroke with the aim of identifying a targetable pathway to facilitate recovery after stroke. Patients and Methods:In this study, we investigated the effect of miR-184 on nerve-associated angiogenesis following brain ischemia-reperfusion by analyzing miR-184 expression levels in the peripheral blood of 10 healthy individuals and 15 individuals with ischemic stroke, a rat middle cerebral artery occlusion model, and human SH-SY5Y neuroblastoma cells co-cultured with human umbilical vein endothelial cells and subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) to model interactions between neurons and brain endothelial cells. We used luciferase reporter assays to evaluate downstream target expression and microscopy to observe nerve-associated angiogenesis. Results:We found that miR-184 expression was significantly downregulated in patients with ischemic stroke, rats subjected to middle cerebral artery occlusion, and human SH-SY5Y cells after OGD/R. Inhibition of miR-184 expression markedly promoted angiogenesis in vitro, as evidenced by increased numbers of vascular nodes, meshes and segments, and elevated vascular endothelial growth factor A expression. Luciferase reporter assays verified type 2 phosphatidic acid phosphatase B (PPAP2B) mRNA as a direct downstream target of miR-184. Moreover, deletion of PPAP2B markedly reduced miR-184-associated angiogenesis. Conclusion:miR-184 is notably downregulated in brain tissue and cells in response to ischemia-reperfusion, thereby contributing to nerve-associated angiogenesis in vitro by enabling greater expression of PPAP2B and vascular endothelial growth factor A. These findings may aid the development of interventions for stroke recovery.
Background:Chronic schizophrenia patients often present with exacerbated clinical symptoms and higher rates of fatty liver disease. This study is designed to compare the occurrence rates of metabolic dysfunction-associated steatotic liver disease (MASLD) and associated factors among hospitalized chronic schizophrenia patients with different obesity metabolic phenotypes. Methods:In this cross-sectional study, we retrospectively analyzed a cohort of 302 chronic schizophrenia patients aged 18-70 years. Patients were classified according to their obesity status (defined as body mass index ≥28 kg/m2) and metabolic health profile, resulting in four distinct obesity metabolic phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Their sociodemographic information, biochemical and lipid parameters were retrospectively collected. Results:In this study, we found that the occurrence rate of metabolic dysfunction-associated steatotic liver disease (MASLD) among chronic schizophrenia patients with prolonged hospitalization was 54.6%. Within the MHNO grouping, MASLD-positive and MASLD-negative subgroups showed significant differences in key metabolic indicators, diabetes status, antipsychotic polypharmacy, and triglyceride-glucose-body mass index (TyG-BMI). Logistic regression analysis demonstrated that Antipsychotic polypharmacy (OR=2.656, P=0.007, 95% CI=1.299-5.429) and TyG-BMI (OR = 1.043, P <0.001, 95% CI = 1.029-1.057) were independently associated with MASLD in the MHNO group. Conclusion:Metabolic dysfunction-associated steatotic liver disease is prevalent among chronic schizophrenia patients, with a notable prevalence gradient across obesity metabolic phenotypes. For metabolically healthy non-obese chronic schizophrenia patients, the importance of regular metabolic dysfunction-associated steatotic liver disease screening cannot be overlook ed.
Background:Differences among analytical platforms may contribute to inconsistent associations between circulating adipokines and coronary artery disease severity. This study compared the analytical robustness and clinical performance of the immunoturbidimetric assay (ITA) and enzyme-linked immunosorbent assay (ELISA) for measuring retinol-binding protein 4 (RBP4) and adiponectin (APN). Methods:A total of 128 patients with angiographically confirmed coronary artery disease were prospectively enrolled. Serum RBP4 and APN concentrations were measured in parallel using ITA and ELISA. Analytical performance was evaluated by precision, linearity, and recovery testing. In an additional cohort of 20 healthy volunteers, the effects of room-temperature storage, repeated freeze-thaw cycles, centrifugation conditions, and anticoagulant type were assessed. Coronary lesion severity was quantified using the Gensini score, and cardiac function was evaluated by Doppler echocardiography. Correlation, receiver operating characteristic, Bland-Altman, and multivariable regression analyses were performed. Results:Both assays demonstrated acceptable linearity and recovery, but ITA showed superior within-run and between-run precision. ELISA-derived measurements were more susceptible to prolonged room-temperature exposure, repeated freeze-thaw cycles, and lithium-heparin anticoagulation. RBP4 concentrations increased and APN concentrations decreased with increasing coronary lesion severity (all P<0.001). ITA-derived measurements showed stronger associations with Gensini score and selected echocardiographic indices than ELISA-derived measurements. ITA-derived RBP4 and APN demonstrated higher diagnostic discrimination for severe coronary artery disease, with areas under the curve of 0.872 and 0.851, respectively. Bland-Altman analysis showed that ELISA produced a positive systematic bias relative to ITA, with increasing intermethod variability at higher RBP4 concentrations. Conclusion:Assay-dependent analytical variability affects the magnitude of biomarker-disease associations and the classification of coronary disease severity. ELISA-derived RBP4 measurements exhibited a positive systematic bias relative to ITA; therefore, adipokine concentrations and proposed diagnostic thresholds should be interpreted with consideration of the analytical platform used. Assay harmonization and standardized preanalytical procedures are necessary before RBP4- and APN-based risk stratification can be applied reliably across laboratories.
Background:Poor sleep quality is relatively common among patients with diabetic kidney disease, yet its multidimensional biopsychosocial independent correlates have not yet been fully elucidated. Purpose:This study aims to explore the independent correlates of poor sleep quality in patients with diabetic kidney disease and to describe the network structure relationships between poor sleep quality and its associated factors. Methods:This retrospective cross-sectional observational study included 336 patients with diabetic kidney disease admitted to hospitals between 2024 and 2025. Twenty-seven biopsychosocial variables were extracted from electronic health records. Independent correlated variables were screened using LASSO regression and multivariate logistic regression. Network analysis was performed via 1000 bootstrap resamples to estimate expected influence (EI) and bridge expected influence (BEI). Results:Among patients with diabetic kidney disease, the prevalence of poor sleep quality was 40.2%. The LASSO method identified 19 candidate variables, whereas multivariate logistic regression retained 13 independent, significant variables. Caffeine intake, smoking history, restless legs syndrome, anxiety, peripheral neuropathy, and nocturia were positively correlated with poor sleep quality. In contrast, glucocorticoid use and elevated SOD showed a negative correlation with poor sleep quality. In the network analysis, the strongest direct connections associated with poor sleep quality were observed for caffeine intake (r=0.842), anxiety (r=0.629), restless legs syndrome (r=0.526), peripheral neuropathy (r=0.430), and nocturia (r=0.378). The EI value for caffeine intake was the highest (1.323), followed by glucocorticoid use (0.461); the bridging connectivity for caffeine intake (BEI=1.671) and glucocorticoid use (BEI=0.729) was relatively high. The Bootstrap analysis indicated that the network stability was acceptable (CS coefficient = 0.470). Conclusion:Among patients with DKD, poor sleep quality is associated with various biopsychosocial factors. Caffeine intake, anxiety, restless legs syndrome, peripheral neuropathy, and nocturia exhibit a strong direct association with poor sleep quality. These variables may serve as important candidate factors in future prospective studies; however, their causal relationships require further validation.
Background:Benford's Law describes a theoretical first-digit distribution observed in many natural and scientific datasets. However, empirical datasets rarely match this distribution exactly, and the appearance of a Benford-like pattern may depend on the numerical range and data-generating process. Objective:This exploratory study aimed to describe the first-digit distributions of X-ray-based human long-bone measurements and derived quantities, and to compare these observed distributions with the theoretical distribution predicted by Benford's Law. Methods:Anteroposterior X-ray images of half of the long bones from three adult patients were retrospectively retrieved from the Picture Archiving and Communication System. The lengths, perimeters, and projected areas of each long bone were measured. The squares and cubes of bone lengths were subsequently calculated as exploratory dimension-based transformations. First-digit distributions were compared with the Benford-expected distribution using Pearson's chi-square goodness-of-fit test as an exploratory statistical comparison. Mean absolute deviation (MAD) between observed and expected first-digit proportions was additionally calculated as a descriptive measure of discrepancy. Results:The first-digit distributions of raw lengths and perimeters showed statistically detectable discrepancies from the Benford-expected distribution (both P = 0.001). Projected areas and squared length values did not show statistically detectable evidence against the Benford-expected distribution in the chi-square comparison (P = 0.260 and P = 0.292, respectively). Cubed length values showed the smallest apparent discrepancy among the variables examined (P = 0.910). MAD values were 0.042 for length, 0.037 for perimeter, 0.024 for projected area, 0.026 for square of length, and 0.014 for cube of length. Conclusion:In this small exploratory dataset, projected areas and derived squared or cubed length values showed more Benford-like first-digit patterns than raw lengths and perimeters. These findings should be interpreted as descriptive evidence of digit-distribution characteristics in long-bone morphometric data rather than as categorical evidence establishing conformity or nonconformity to Benford's Law.
The increasing healthcare burden from kidney disease which includes chronic kidney disease (CKD) and end-stage kidney disease (ESKD) demonstrates how diabetes and hypertension have become major health concerns for the Saudi Arabian population. The review examines Saudi Vision 2030 through its current epidemiological data and healthcare system assessment and existing obstacles to effective treatment and potential future developments. A literature search identified key studies on prevalence, risk factors, workforce, and innovations. The population screening results shows that CKD prevalence ranges from 4.76% to 13.8% with diabetic nephropathy and hypertensive nephropathy as the main causes. Although nephrologist density reaches approximately 39 doctors per million residents in certain areas, both regional disparities and workforce training deficits remain active issues. The system faces multiple difficulties which include delayed patient referrals, inadequate use of home treatment options, financial constraints and variable patient access to transplantation procedures and advanced diagnostic methods. The field of medicine will experience major changes through new developments which include fresh biomarkers and artificial intelligence (AI)-based risk assessment tools and telemedicine and personalized medical solutions. The implementation of prevention measures along with early detection programs and multidisciplinary treatment methods and digital health solutions will help achieve sustainable and fair kidney care. The review presents specific steps which will help decrease disease rates and death rates and financial costs while advancing the health transformation objectives established in Vision 2030.
Objective:This study investigated the associations between five insulin resistance (IR)-related surrogate indices and gout and compared their associations and discriminative abilities for gout. The indices included the triglyceride-glucose (TyG) index, TyG combined with body mass index (TyG-BMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and metabolic score for insulin resistance (METS-IR). Methods:Data from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed. Comparisons between the gout and nongout groups were performed using t-tests and chi-square tests. Multivariable logistic regression and subgroup analyses were used to assess the associations. Results:Among 14,582 participants (4.80% with gout), adjusted analyses identified the highest METS-IR quartile as the strongest predictor of gout (adjusted OR = 2.823, 95%CI 1.643-4.851), followed by TyG-BMI (OR = 2.290, 95%CI 1.555-3.373). Restricted cubic splines revealed nonlinear associations of TyG and LAP with gout risk, contrasting with linear trends for TyG-BMI, VAI, and METS-IR. Subgroup analyses suggested that elevated TyG and VAI were positively associated with gout in nondiabetic individuals (interaction p < 0.05). Conclusion:All five IR-related indices showed positive associations with gout, particularly in those with central obesity. These indices showed modest discriminative ability for gout, and further validation is needed.
Objective:To investigate the association between iron metabolism markers and microalbuminuria in patients with Type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD). Methods:A total of 137 patients with T2DM and MASLD were enrolled and stratified into microalbuminuria-positive and microalbuminuria-negative groups based on the urinary albumin-to-creatinine ratio (UACR). Iron metabolism markers, including serum ferritin, serum iron, total iron-binding capacity (TIBC), transferrin saturation (TS), and hepcidin, were measured. Hepatic iron and fat content were assessed using magnetic resonance imaging (MRI), including R2∗ mapping (MRI-R2∗) and proton density fat fraction (MRI-PDFF). Results:Patients with microalbuminuria had significantly higher levels of serum ferritin, serum iron, hepcidin, and MRI-R2∗ compared with those without microalbuminuria (all p < 0.05). Correlation analyses showed that hepcidin (r s = 0.208, p = 0.015) and MRI-R2∗ (r s = 0.248, p = 0.003) were positively associated with UACR. However, after multivariable adjustment for confounding factors, MRI-R2∗ remained independently associated with microalbuminuria (p = 0.049). Conclusions:Altered iron metabolism is associated with microalbuminuria in patients with T2DM and MASLD. MRI-R2∗, a quantitative imaging marker of hepatic iron content, showed an independent association with microalbuminuria after multivariable adjustment and may provide complementary information for early renal injury assessment in this population.
