The First People's Hospital of Guiyang

This study aimed to comprehensively characterize the relationship between glycated hemoglobin (HbA1c) and intervertebral disc degeneration (IVDD) by integrating population-level epidemiological profiling, Mendelian randomization (MR) analysis, multi-omics data, and single-cell transcriptomics. The objectives included clarifying the causal effect of HbA1c on IVDD, identifying key HbA1c-related genes, elucidating their cellular and molecular roles in disc degeneration, and exploring potential therapeutic targets. Global Burden of Disease (GBD) 2021 database was used to evaluate the incidence rate of diabetes mellitus (DM) and low back pain (LBP) across the entire global population. To assess the causal effect of HbA1c on IVDD, MR approache was conducted using summary statistics from large genome-wide association studies. Subsequently, integrating cis-eQTL information, bulk RNA sequencing, and single-cell RNA sequencing were performed to identify HbA1c-related genes involved in disc degeneration, and explore their roles along pseudotime differentiation trajectories. Potential associations between hub genes and IVDD, as well as candidate therapeutic compounds targeting these genes, were further evaluated through database screening and molecular docking. The findings from GBD indicated a correlation between diabetes and low back pain in both 1990 and 2021. The results of MR analysis revealed a causal relationship between HbA1c levels and IVDD. After integrated analysis of the eQTLGen database and RNA-seq data, we identified five hub HRGs, including STAT5A, CDKN1C, TRPS1, NME4, and OASL, which have been implicated in multiple biological processes and pathways associated with IVDD. Moreover, the scRNA-seq analysis showed that hub HRGs were associated with specific cell subpopulations in human nucleus pulposus (NP) tissues, such as inflamed chondrocytes, fibrochondrocytes, chondrocytes, and calcifying chondrocytes. Additionally, hub HbA1c related genes (HRGs) have been found to be associated with several DDDs such as back pain, osteoporosis, and spinal stenosis. Meanwhile, 12 drugs, including resveratrol, dexamethasone, and simvastatin, were identified that have the potential to target hub HRGs. This study revealed a causal effect of HbA1c on IVDD and identified five key HbA1c-related genes (STAT5A, CDKN1C, TRPS1, NME4, and OASL) involved in disc degeneration, providing potential targets for future research.

The red cell distribution width-to-albumin ratio (RAR) is a composite biomarker integrating inflammatory, nutritional, and stress status; however, its association with short-term prognosis in patients with intracerebral hemorrhage (ICH) remains unclear. This study was a retrospective dual-cohort study. A total of 2,327 ICH patients from the MIMIC-IV database were included as the derivation cohort, and 428 patients from a tertiary hospital were collected as the external validation cohort. The association between RAR and outcomes was analyzed using multivariable Cox regression, with restricted cubic splines employed to examine non-linear relationships. Multiple machine learning algorithms were utilized to screen key prognostic variables, and a logistic regression-based risk prediction model was constructed. Its discriminative ability and stability were validated in both internal and external cohorts. After adjusting for multiple confounders, including demographic characteristics, comorbidities, disease severity, and treatment measures, a higher RAR level remained an independent risk factor for 28-day ICU mortality (adjusted HR = 1.17, 95% CI: 1.08-1.27) and 28-day in-hospital all-cause mortality (adjusted HR = 1.14, 95% CI: 1.05-1.23) in ICH patients. Restricted cubic spline analysis further indicated a significant non-linear dose-response relationship between RAR and these outcomes (P for non-linearity < 0.05). In addition, incorporating RAR significantly improved the predictive performance of six traditional critical illness scoring systems, including APACHE II, SOFA, and SAPS II (AUC improvement ranging from 0.016 to 0.188; all DeLong tests P < 0.01). Using five machine learning algorithms, we identified seven key variables-age, RAR, INR, total bilirubin, blood urea nitrogen, aspartate aminotransferase, and systolic blood pressure-to construct a short-term mortality risk prediction model for ICH. This model demonstrated robust discriminative ability in the internal training, internal validation, and external validation sets (AUC values of 0.761, 0.723, and 0.723, respectively), outperforming conventional scoring systems. RAR is an independent predictor of short-term mortality risk in patients with ICH. The prediction model incorporating RAR exhibits good discriminative ability and cross-cohort stability, offering a practical tool for early identification of high-risk patients and optimization of management strategies. However, this study has certain limitations, including its retrospective design, limited sample size and single-center source for external validation, and lack of neuroimaging data (e.g., hematoma location/volume). Future prospective multi-center studies are needed to further validate its clinical value.

Air pollution has been associated with dysregulated metabolism. However, evidence linking prenatal air pollution exposure to gestational weight gain (GWG) pattern remains limited. This retrospective cohort study of 47,793 pregnant women in Guiyang (2013-2022) assessed associations between air pollutants and GWG pattern. Positive associations were observed between excessive GWG and CO (per 1 μg/m3 increase), NO2, O3, PM10, PM2.5, and SO2 (per 10 μg/m3 increase) throughout the whole pregnancy period. Specifically, early-pregnancy exposure to CO (OR = 1.377, 95% CI: 1.201, 1.578) and NO2 (OR = 1.098, 95% CI: 1.068, 1.130), along with exposure to PM10 (OR = 1.058, 95% CI: 1.043, 1.073), PM2.5 (OR = 1.095, 95% CI: 1.073, 1.118), and SO2 (OR = 1.135, 95% CI: 1.102, 1.169) during late pregnancy significantly increased excessive GWG risk. Conversely, O3 exposure was inversely associated with excessive GWG. For insufficient GWG, only early-pregnancy exposures to PM10 (OR = 1.016, 95% CI: 1.001, 1.032), PM2.5 (OR = 1.022, 95% CI: 1.001, 1.043), and SO2 (OR = 1.031, 95% CI: 1.004, 1.058) showed significant positive associations. Furthermore, the restricted cubic spline (RCS) model revealed a nonlinear relationship between pollutant exposure and the risk of excessive GWG. Stratified analyses revealed that the air pollution and GWG (continuous) association was stronger among women with pre-pregnancy BMI ≥ 24 kg/m2 and aged ≥ 30 years. This study confirms that, even at lower concentrations, exposure to air pollutants during pregnancy is significantly associated with an increased risk of abnormal GWG. Compared to previous studies focusing on high-concentration areas, this finding provides additional evidence for assessing the health risks of air pollution exposure during pregnancy, suggesting that the potential metabolic effects of low-level, long-term exposure should be considered when developing maternal health strategies.

Asthenozoospermia, characterized by reduced sperm motility, is a major contributor to male infertility and motivates improved resources for studying spermatogenesis at the genomic level. Here, we present a paired whole-genome sequencing (WGS) dataset from 53 Han Chinese men, comprising matched blood WGS per participant (target ~10×) and 3-5 low-coverage single-sperm WGS libraries per participant (target ~1×). The dataset includes 263 single-sperm libraries (79 from 16 asthenozoospermic participants and 184 from 37 normozoospermic participants) and is accompanied by rich participant-level metadata, including baseline characteristics, endocrine measurements, and semen parameters such as sperm motility and vitality. Raw reads underwent standardized quality-control filtering, and key sequencing metrics (Q20 and GC content) met commonly used thresholds; the achieved mean depth was approximately 10× for blood and ~1.7× for single sperm. By integrating sperm motility/vitality phenotypes with individual-matched genomic information, this resource provides a foundation for male reproductive genomics and for developing and benchmarking algorithms for gamete-genome dissection, and may support future translational research on male infertility evaluation.

Background: Early venous outflow dysfunction is a leading cause of early failure after finger replantation. This study evaluated whether preoperative serum markers of endothelial activation and inflammation can predict early venous outflow dysfunction and developed a serum-based risk model. Methods: This retrospective cohort included 150 patients who underwent finger replantation between January 2022 and October 2025. Peripheral venous blood was collected 2 h before surgery. Serum von Willebrand factor (vWF), intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), E-selectin, C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured. Early venous outflow dysfunction was defined as venous congestion requiring intervention within 72 h postoperatively. Independent markers were identified using multivariable logistic regression. Model performance was assessed by receiver operating characteristic analysis (AUC with 95% confidence intervals by the DeLong method and between-model comparison by the DeLong test), calibration by the Hosmer–Lemeshow test, and internal validation using bootstrap resampling (200 iterations). Results: All seven biomarkers were higher in cases (n=74) than controls (n=76) (P&lt;0.05) and were positively correlated with crisis duration (all P&lt;0.01). vWF, E-selectin, and TNF-α remained independently associated with early venous outflow dysfunction. The resulting panel achieved an AUC of 0.811 (95% CI, 0.738–0.883), with 72.97% sensitivity and 84.21% specificity. Calibration was acceptable (Hosmer–Lemeshow P=0.093), and the optimism-corrected AUC was 0.802. A clinical-variable model showed poorer discrimination (AUC 0.618; 95% CI, 0.527–0.709), and the biomarker panel improved discrimination significantly (DeLong P=0.001). Conclusions: A preoperative serum panel combining vWF, E-selectin, and TNF-α showed good discrimination for early venous outflow dysfunction after finger replantation. External validation is needed before routine clinical use.

Hyaluronic acid (HA) is a cornerstone of aesthetic skin treatments due to its vital role in skin hydration and combating aging signs. This trial evaluated the efficacy and safety of a novel non-cross-linked HA gel for improving skin quality in a Chinese population. In this multicenter, randomized, controlled trial, 420 subjects were enrolled (2:1, treatment: control). The treatment group received three full-face intradermal injections of the HA gel at 3-week intervals, whereas the control group received no intervention. Primary end points were blinded evaluations of the Global Aesthetic Improvement Scale and a composite improvement rate for skin dryness/dullness. The treatment group showed significantly higher Global Aesthetic Improvement Scale improvement (90.63% vs 3.65%) and composite improvement rates (83.20% vs 3.65%) than control group (p < .0001), with effects sustained for 6 months. Skin hydration and elasticity significantly increased from baseline (p < .05). Peak efficacy rates for fine lines and roughness reached 51.59% and 50.00%, respectively. Adverse events were mild and transient, with decreasing incidence. This non-cross-linked HA gel effectively enhances skin hydration, elasticity, and texture with sustained 6-month benefits and a favorable safety profile, supporting its use for facial rejuvenation.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease with a survival rate comparable to or worse than that of many cancers. Proper TGF-β signaling is essential for normal lung function, but its disruption plays a key role in pulmonary fibrosis and cancer progression. This study aims to elucidate the role of TGF-β signaling-related genes in the prognosis and treatment of IPF through multi-omics analysis. We obtained datasets from the GEO database and identified differentially expressed genes, followed by enrichment analyses. Core genes were identified using machine learning algorithms. Next, we evaluated the expression of core genes and their predictive ability for IPF, as well as their relationship with lung function and survival time. Then, mendelian randomization revealed core genes causally associated with IPF. Subsequently, pseudotime analysis, cell communication analysis and metabolic analysis were performed using single-cell data. Furthermore, we performed immune infiltration analysis to reveal the immune microenvironment of IPF. Finally, in vivo experiments validated the mRNA expression of the core genes. Two core genes (ACVRL1 and LTBP1) were identified through differential expression analysis and machine learning algorithms. Validation using multiple external datasets confirmed that these core genes exhibit stable expression patterns and have strong predictive ability for IPF patients. Further analysis revealed that the expression of these core genes correlates with lung function and survival time in IPF patients. Mendelian randomization analysis provided evidence of a causal link between ACVRL1 and IPF. Using eQTLGen data, our summary data-based mendelian randomization (SMR) analysis revealed a possible causal link between ACVRL1 and IPF. Similarly, using GTEx eQTL data, our SMR analysis revealed a potential causal link between ACVRL1 and IPF. Furthermore, single-cell data analysis highlighted differences in cell communication and metabolism between ACVRL1 + endothelial cell (EC) and ACVRL1-EC. Finally, RT-qPCR results support the potential role of core genes in IPF. This study provides new perspectives on the development of IPF and may help identify novel therapeutic targets. Further research may reveal how core genes influence cellular function and disease progression, providing novel insights into the intricate mechanisms underlying IPF.

The early prediction of neoadjuvant chemotherapy (NAC) efficacy is essential for the timely modification of treatment regimens. This study investigates the clinical utility of integrating ultrasound examination with the nutritional risk index (NRI) to predict pathological complete response (pCR) in breast cancer patients following NAC. Utilizing a retrospective analysis, we examined data from 85 breast cancer patients who underwent NAC between January 2020 and December 2023. All participants received routine ultrasound evaluations and NRI assessments both prior to NAC initiation and after the completion of 2 treatment cycles. Based on the Miller–Payne grading system, patients were categorized into the pCR group (39 cases) and the non-pCR group (46 cases). Significant variables (P < .05) were identified through univariate logistic regression analysis and subsequently incorporated into a multivariate binary logistic regression model (inclusion criterion: P < .20). The diagnostic performance was assessed using the receiver operating characteristic curve, while the calibration curve was utilized to further evaluate the predictive value. The multivariate analysis identified 3 independent predictors of pathological complete response (pCR): N stage (N2: odds ratio [OR] = 3.68, 95% confidence interval [CI]: 1.08–12.53, P = .037), change in tumor maximum diameter after the second neoadjuvant chemotherapy (NAC) cycle (ΔD2; OR = 0.95, 95% CI: 0.92–0.99, P = .024), and change in nutritional risk index after the second NAC cycle (ΔNRI2; OR = 1.10, 95% CI: 1.01–1.19, P = .032). The variance inflation factor values (ranging from 1.070 to 1.163) indicated an absence of multicollinearity. The combined diagnostic model achieved an area under the receiver operating characteristic curve of 0.847 (95% CI: 0.765–0.930), with a sensitivity of 69.2% and a specificity of 89.1%. The calibration curve demonstrated strong agreement between predicted and actual outcomes (Hosmer–Lemeshow test, P = .747). The integration of N stage, ΔD2, and ΔNRI2 exhibits significant clinical utility for the early prediction of NAC efficacy in breast cancer, potentially informing treatment adjustments and enhancing patient outcomes. However, the study’s small sample size (n = 85) and single-center retrospective design constrain its generalizability. Therefore, large-scale, multicenter prospective validation is necessary prior to clinical implementation.