Chronic diabetic wounds are difficult to heal due to impaired neovascularization, dysregulated tissue repair, and prolonged inflammation. To address these challenges, PLA/nanoclay/Nd₂O₃ nanocomposite fiber mats were fabricated by electrospinning. Structural and morphological analyses confirmed successful incorporation and uniform dispersion of nanoclay and Nd₂O₃ within the PLA matrix. Nanoclay modulated the scaffold surface, while Nd₂O₃ enhanced mechanical strength. The intercalated nanoclay network further improved load transfer, reduced fiber diameter, and enhanced surface wettability, supporting cellular interactions. The scaffold showed more than 90% cell viability across all tested samples, demonstrating good cytocompatibility. The optimized scaffold exhibited significant antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). The enhanced vascular density of the scaffolds in the chick chorioallantoic membrane (CAM) assay confirms their angiogenic potential. The scaffold also demonstrated controlled degradation, with 8.1 ± 0.5% weight loss after 28 days in simulated body fluid. In vivo evaluation using a diabetic rat wound model showed accelerated wound contraction, complete reepithelialization, neovascularization and minimal inflammatory response by day 21. The findings of this study suggest that PLA/Clay/Nd₂O₃ bioactive nanofibrous scaffolds have considerable potential for diabetic wound-healing applications.

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease worldwide. Early identification of DKD remains challenging in routine clinical practice.

We aimed to develop and externally validate a clinically accessible model integrating metabolic and renal biomarkers for identifying DKD among individuals with diabetes.

Data from 3494 diabetic participants in the China Health and Retirement Longitudinal Study (CHARLS) were utilized for model development. Independent predictors were identified via multivariable logistic regression. To ensure robust generalizability, external validation was conducted in two independent cohorts: a national survey cohort from the National Health and Nutrition Examination Survey (NHANES) and a real-world clinical cohort from our hospital. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).

The final model incorporated age, gender, the triglyceride-glucose (TyG) index, blood urea nitrogen and Cystatin C. In the CHARLS cohort, the model demonstrated robust discriminative power (AUC = 0.868) and excellent calibration. At the optimal cutoff (8.02%), the negative predictive value (NPV) reached 98.43%. In the NHANES validation, the model maintained high discriminative ability (AUC = 0.82). Crucially, in the hospital-based clinical cohort, the model demonstrated stable performance (AUC = 0.781, 95% CI: 0.648-0.888). DCA confirmed a consistent and significant net clinical benefit across a wide threshold range (0.2 to 0.8) in real world.

The proposed model may serve as an adjunctive tool for identifying individuals with diabetes who have a high probability of prevalent DKD and who may therefore benefit from confirmatory kidney assessment.

Valvular heart disease encompasses a diverse group of hemodynamic disorders that are frequently associated with heart failure, recurrent hospitalizations, and substantial morbidity despite advances in transcatheter and surgical therapies. Sodium-glucose cotransporter-2 (SGLT2) inhibitors have emerged as a cornerstone of cardiovascular and renal disease management, demonstrating consistent benefits across a broad range of patient populations. Beyond their established effects in heart failure, diabetes mellitus, and chronic kidney disease, accumulating evidence suggests that SGLT2 inhibitors may favorably influence key biological pathways involved in valvular heart disease, including inflammation, oxidative stress, fibrosis, endothelial dysfunction, and adverse cardiac remodeling. Preclinical and translational studies have provided mechanistic support for a potential role of SGLT2 inhibition in modifying valvular and myocardial disease processes. Emerging clinical evidence suggests possible benefits across several forms of valvular heart disease, including degenerative aortic stenosis, functional mitral regurgitation, tricuspid regurgitation, rheumatic mitral stenosis, and bioprosthetic valve degeneration. In addition, growing interest has focused on the integration of SGLT2 inhibitors into contemporary structural heart practice, particularly among patients undergoing transcatheter aortic valve replacement, transcatheter edge-to-edge repair, and other structural interventions. This narrative review summarizes the mechanistic rationale, current clinical evidence, and practical considerations surrounding the use of SGLT2 inhibitors in valvular heart disease and structural heart interventions. We discuss the strengths and limitations of the existing literature, identify important gaps in knowledge, and highlight future directions for research aimed at defining the role of SGLT2 inhibitors as an adjunctive therapy in structural heart disease.

The role of nuclear factor erythroid 2-related factor 2 (NRF2) as a central transcription factor in the regulation of cellular antioxidant stress is of significant interest, particularly in its potential clinical relevance for elderly patients with type 2 diabetes mellitus (T2DM)-associated osteoporosis (T2DM-OP).

A cohort of 196 patients with T2DM who received treatment were recruited and stratified into three groups according to T-scores derived from bone mineral density (BMD) assessments. Serum concentrations of 25-hydroxyvitamin D3, osteocalcin (OC), procollagen type I N-terminal propeptide (P1NP), b-isomerized C-terminal telopeptide of type I collagen (b-CTX), as well as nuclear factor erythroid 2-related factor 2 (NRF2), superoxide dismutase (SOD), and malondialdehyde (MDA) were quantified utilizing enzyme-linked immunosorbent assay techniques. Pearson correlation analysis was conducted to explore the relationships among NRF2, SOD, MDA, and BMD. Additionally, multivariate logistic regression analysis was employed to investigate the association between Asprosin levels and the risk of T2DM-OP.

There were no apparent differences in baseline characteristics (gender, age, BMI, duration of diabetes mellitus, and metabolic parameters) among the three groups (P > 0.05). Analysis revealed that lumbar spine BMD decreased evidently with declining bone mass (normal bone mass group 1.13 g/cm2, osteopenia group 0.93 g/cm2, osteoporosis group 0.80 g/cm2, P < 0.001). In the osteoporosis cohort, there was a significant elevation in the bone resorption marker b-CTX and the bone formation marker OC (P < 0.001), indicating a pronounced imbalance in bone turnover. Among oxidative stress factors, SOD activity and NRF2 levels progressively decreased with bone loss, while MDA levels increased (all P < 0.001), indicating declined antioxidant capacity and aggravated oxidative damage. Correlation analysis showed that NRF2 and SOD were positively correlated with BMD, whereas MDA was negatively correlated with BMD. Multivariate logistic regression further confirmed that low NRF2, low SOD, and high MDA were independent risk factors for T2DM-OP (P < 0.05). Receiver operating characteristic curve analysis demonstrated that NRF2 exhibited high sensitivity (80.36%) and specificity (77.86%) in predicting T2DM-OP with an area under the curve of 0.856 (95% CI: 0.803-0.909, P < 0.001), comparable to bone turnover indicators.

The study reveals that the NRF2-mediated antioxidant response is crucial in the pathological progression of T2DM-OP highlighting its potential as a new therapeutic target for prevention and treatment.

To investigate the therapeutic efficacy and influencing factors of metoprolol combined with trimetazidine in the treatment of heart failure secondary to coronary heart disease (CHD).

A retrospective analysis was performed on 142 patients with CHD complicated by heart failure (HF) who were treated at the First Affiliated Hospital of Xinjiang Medical University from July 2023 to July 2025. Patients were allocated into a control group (trimetazidine monotherapy, n=70) and a joint group (metoprolol combined with trimetazidine, n=72). Clinical outcomes were compared between the two groups, including overall treatment efficacy, QT dispersion (QTd) before and after treatment, heart rate (HR), hemodynamic parameters [hematocrit (Hct), plasma fibrinogen (Fib), plasma specific viscosity], vascular endothelial function markers [calcitonin gene-related peptide (CGRP), plasma endothelin (ET), nitric oxide (NO)], cardiac function indicators [left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF), left ventricular end-systolic diameter (LVESD)], and incidence of adverse reactions. Logistic multivariate regression analysis was performed to identify independent factors affecting therapeutic efficacy in CHD patients complicated by HF.

The total effective rate was significantly higher in the joint group than the control group (86.11% vs. 71.43%, P=0.032). After treatment, QTd, HR, ET, and hemodynamic parameters were significantly decreased in both groups compared with baseline, whereas CGRP, NO, and LVEF were significantly increased. Moreover, the improvement in the joint group was significantly superior to that in the control group (P<0.05). There was no significant difference in the overall incidence of adverse reactions between the two groups (13.89% vs. 12.86%, P=0.857). Multivariate logistic regression analysis identified treatment regimen, diabetes mellitus, and age as independent factors affecting therapeutic efficacy (P<0.05).

Trimetazidine combined with metoprolol demonstrates superior efficacy without increasing adverse reactions in CHD patients complicated by HF. This combination therapy may improve hemodynamic status, endothelial function, and cardiac function, supporting its clinical application.

To identify admission-based predictive indicators for in-hospital acute metabolic complications in elderly patients with type 2 diabetes mellitus (T2DM), and to develop and internally validate a simple predictive model for early risk stratification.

A total of 203 elderly inpatients with T2DM were included in this retrospective cohort study. According to the occurrence of acute metabolic complications during hospitalization, patients were divided into a complication group (n = 40) and a non-complication group (n = 163). All candidate predictive indicators were collected from admission medical records and laboratory test results obtained within 24 to 48 hours after admission. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive factors. The model's discriminatory power was evaluated using the area under the receiver operating characteristic curve (AUC), and 1000 bootstrap resamplings were adopted for internal validation.

The complication group had significantly higher admission HbA1c levels than the non-complication group (P<0.001), as well as a higher prevalence of hypertension (P = 0.002). Multivariate regression analysis verified that HbA1c (OR = 1.761, 95% CI: 1.44-2.15, P<0.001) and hypertension (OR = 3.904, 95% CI: 1.60-9.53, P = 0.003) were independent risk factors for in-hospital acute metabolic complications. The predictive model based on these two indicators exhibited good discriminatory performance, with an original AUC of 0.836. After bootstrap internal validation, the optimism-corrected AUC was 0.726, indicating acceptable stability of the model.

Elevated admission HbA1c and comorbid hypertension are independent predictive factors for in-hospital acute metabolic complications in elderly T2DM patients. This novel simple model has favorable predictive efficacy and stability, and it can serve as a feasible clinical tool for early risk stratification in elderly T2DM inpatients.

Myosteatosis, defined as pathological lipid accumulation within and between skeletal muscle fibers, is increasingly recognized as a determinant of impaired muscle quality, metabolic inflexibility, and adverse clinical outcomes. Although well described in ageing, obesity, and cancer, its relevance to type 1 diabetes (T1D) remains underexplored. T1D is characterized by lifelong insulin deficiency, persistent autoimmune activation, and glycemic variability, conditions that profoundly disrupt cellular energy metabolism and substrate utilization in skeletal muscle, even in the absence of obesity or overt sarcopenia. This review integrates evidence from human imaging, metabolic phenotyping, immunological profiling, and multi-omics analyses to define myosteatosis as an immunometabolic phenotype in T1D. Central to this framework is dysregulation of the AMP-activated protein kinase (AMPK)-peroxisome proliferator-activated receptor (PPAR)-mitochondrial axis, which normally coordinates fatty-acid oxidation, mitochondrial biogenesis, and energy efficiency in skeletal muscle. In T1D, chronic immune activation and metabolic stress suppress AMPK and PPARδ signaling, impair PGC-1α-dependent mitochondrial function, and reduce oxidative capacity, promoting intramyocellular lipid accumulation despite preserved muscle mass. These defects are reinforced by persistent inflammatory signaling (IL-6, TNF-α, IL-1β; NF-κB, JNK, and NLRP3 pathways), accumulation of lipotoxic intermediates (ceramides and diacylglycerols), dysregulated myokine secretion (increased myostatin with reduced IL-15 and irisin), and infiltration of pro-inflammatory macrophages and CD8⁺ T cells. Mitochondrial stress, reflected by impaired phosphocreatine recovery, altered acylcarnitine profiles, increased oxidative damage, and reduced NAD⁺-SIRT1/3 activity, further consolidates immunometabolic dysfunction and lipid deposition. Collectively, this review positions myosteatosis as a clinically relevant and potentially modifiable consequence of immune-driven failure of cellular energy utilization in T1D. Because direct mechanistic data from T1D skeletal muscle remain scarce, the framework presented here is deliberately hypothesis-generating: it is assembled substantially by inference from type 2 diabetes (T2D), obesity and ageing models, and we map the resulting evidence gaps explicitly in order to define a research agenda rather than to assert a validated T1D-specific mechanism. Targeting the AMPK-PPAR-mitochondrial axis and its inflammatory and lipotoxic modifiers may enable earlier detection and mechanism-based interventions to preserve muscle metabolic resilience and functional capacity in autoimmune diabetes.

To evaluate the diagnostic value of combined platelet-derived growth factor (PDGF) and angiopoietin-like protein 4 (ANGPTL4) for distinguishing diabetic foot (DF) from diabetes mellitus (DM) without DF, and to explore their early changes following treatment initiation and their associations with 3-month ulcer healing.

Clinical data were retrospectively collected from January 2024 to May 2025. Before propensity score matching (PSM), 153 patients with DF and 116 non-DF DM patients met the eligibility criteria; after 1:1 PSM, 97 patients were retained in each group. Serum PDGF and ANGPTL4 were measured at baseline in both groups, with additional measurements taken on day 5 after the initiation of plantar arch angioplasty (PAA) and alprostadil in the DF group. The primary analysis evaluated the diagnostic performance of the baseline biomarkers using receiver operating characteristic (ROC) curves, while secondary exploratory analyses assessed their day-5 changes and associations with 3-month ulcer healing.

At baseline, PDGF was 26.23% lower and ANGPTL4 was 32.22% higher in the DF group than in the matched DM group (both P<0.05). The combined baseline model yielded an area under the curve (AUC) of 0.805, surpassing the performance of either biomarker alone. Both biomarkers were also associated with ulcer size, Wagner grade, fasting plasma glucose, and hemoglobin A1c. On day 5 after PAA and alprostadil initiation, PDGF significantly increased and ANGPTL4 significantly decreased relative to baseline (both P<0.05). At 3 months, 66 of 97 patients achieved complete ulcer healing; the combined day-5 model discriminated healing status with an AUC of 0.764, a sensitivity of 59.09%, and a specificity of 87.10%.

The combination of baseline PDGF and ANGPTL4 improved DF discrimination compared with either biomarker alone. Their day-5 changes and associations with 3-month ulcer healing were exploratory and should not be interpreted as evidence of treatment efficacy or validated prognostic utility.

To investigate the risk factors for catheter-related infections (CRI) in patients undergoing chemotherapy through tunneled peripherally inserted central catheters (PICCs) following neck tumor lymph node dissection. To provide evidence for clinical intervention strategies.

A retrospective review was conducted on the medical records from 495 patients who underwent tunneled PICC placement for chemotherapy after neck tumor lymph node dissection. Patients were divided into a CRI group (n=55) and a non-CRI group (n=440). The incidence of CRI, infection type, and pathogen distribution were statistically analyzed. Multivariate logistic regression was employed to identify independent risk factors. A logistic regression-based predictive model was established and its performance was evaluated.

Among the 495 patients, the incidence of CRI was 11.11% (55/495), with exit-site infections being the predominant type (25 cases, 45.45%). A total of 11 pathogenic strains were isolated. Multivariate logistic regression analysis revealed that PICC indwelling duration >30 days, catheter displacement, concomitant diabetes mellitus, and white blood cell count ≤3.0×10⁹/L were independent risk factors for CRI (all P<0.05). The area under the curve (AUC) for the logistic regression model for predicting CRI efficacy was 0.896 [95% CI, 0.845, 0.947], with a sensitivity of 91.0% and a specificity of 79.0%. Calibration curves demonstrated good agreement with the ideal predictions.

Patients undergoing chemotherapy through tunneled PICC after neck tumor lymph node dissection exhibit a relatively high incidence of CRI. Clinicians should implement targeted interventions for patients with prolonged catheterization, catheter displacement, diabetes mellitus, and leukopenia to reduce the incidence of CRI and improve patient outcome.

To study the correlation between sleep duration and glucolipid metabolism in patients with new-onset type 2 diabetes mellitus (NOT2DM).

The participants were 168 NOT2DM patients. Glucolipid metabolism indices were compared among the short (n=59), middle (n=56), and long sleep groups (n=53), including fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), total cholesterol (TC), and triglyceride (TG). The correlation of sleep duration with glucolipid metabolism, glycemic control, and body fat distribution (abdominal total fat area [TFA] and visceral fat area [VFA]) was assessed. Finally, the contributors to poor glycemic control were explored.

Glucolipid metabolism indices tended to decrease with increasing sleep duration in NOT2DM patients, suggesting a significant negative correlation between sleep duration and glucolipid metabolism. The sleep duration of the good glycemic control group was significantly longer. A significant positive correlation was identified between sleep duration and good glycemic control, while an inverse association was determined between sleep duration and TFA. Body mass index (BMI), sleep duration, FBG, TG, and TFA were all independent contributors to poor glycemic control.

Sleep duration in NOT2DM patients was inversely related to glucolipid metabolism and TFA and positively correlated with good glycemic control. High BMI, FBG, TG, and TFA, as well as short sleep duration, may increase the risk of poor glycemic control.