Kaiyu Han

Background: Circadian rhythm disruption is increasingly recognized as a contributor to chronic inflammatory disorders; however, its specific significance and underlying mechanisms in chronic obstructive pulmonary disease (COPD) remain unclear. This study aimed to identify circadian rhythm-associated biomarkers in COPD and explore their diagnostic value, immune correlations, and therapeutic potential. Methods: This study integrated four lung transcriptomic datasets from the public GEO database (GSE151052, GSE38974, and GSE76925 as the discovery set, and GSE47460 as the validation set). Differentially expressed circadian rhythm‑related genes (DECRRGs) were identified by intersecting differentially expressed genes with circadian rhythm‑related genes. Functional enrichment analyses (GO and KEGG) were performed, and three machine learning algorithms were applied to screen for signature DECRRGs. An exploratory risk stratification model based on multivariate logistic regression was constructed and evaluated. Immune cell infiltration was assessed using CIBERSORT, and single-cell RNA sequencing analysis was conducted to localize the distribution of key circadian rhythm genes within specific lung cell populations. Finally, the expression of a core gene CHRNA1 was validated by qRT-PCR in peripheral blood samples from COPD patients and healthy controls. Results: We identified eight circadian rhythm-associated feature genes, among which CHRNA1 emerged as a consistently upregulated hub gene in COPD. An exploratory risk stratification model based on these genes exhibited good discriminatory ability in the discovery cohort (AUC = 0.856, 95% CI: 0.806–0.902). Differential expression of CHRNA1 was validated in an independent cohort and correlated significantly with pro-inflammatory immune infiltration, including increased M1 macrophages and CD8 ⁺ T cells. Single-cell transcriptomics further localized CHRNA1 expression predominantly within B cells in COPD lung tissue. In silico drug screening and ceRNA network analysis predicted potential therapeutics (e.g., amitriptyline, rocuronium bromide) and regulatory miRNAs/lncRNAs. Finally, qRT-PCR confirmed a marked upregulation of CHRNA1 in peripheral blood from COPD patients ( *p* < 0.0001). Conclusions: Our findings suggest that CHRNA1 may serve as a candidate circadian rhythm‑associated immunomodulator in COPD. It demonstrates consistent upregulation across cohorts and shows a significant association with pro‑inflammatory immune infiltration. Single-cell analysis revealed that CHRNA1 is predominantly expressed in pulmonary B cells. The exploratory risk stratification model and predicted therapeutic candidates highlight the translational potential of targeting circadian disruption in COPD, though prospective validation is needed before clinical application.

Diabetes mellitus is a common comorbidity that may increase susceptibility to severe infections and worse outcomes. We aimed to identify clinical predictors of in-hospital mortality in diabetic patients with Gram-negative Enterobacteriaceae sepsis using a data-driven feature-selection and machine learning (ML) approach, and to examine the causal relationship between diabetes and sepsis using Mendelian randomization (MR). We performed a retrospective cohort study of patients admitted to The Second Affiliated Hospital of Harbin Medical University between January 2018 and August 2025 with diabetes and Gram-negative Enterobacteriaceae sepsis. The Boruta algorithm was used to identify variables important for mortality prediction; selected features were entered into ML classifiers and evaluated using receiver operating characteristic analysis. Separately, MR analysis was conducted with diabetes as the exposure and sepsis as the outcome. The inverse-variance weighted method was used as the primary estimator, and MR-Egger, weighted median, simple mode, and weighted mode approaches were applied as sensitivity analyses to assess robustness and potential directional pleiotropy. Boruta selection retained 10 variables associated with in-hospital mortality: intensive care unit admission, concomitant shock, respiratory failure, liver abscess, coma, anemia, decreased platelet count, reduced red blood cell count, infection with extended-spectrum β-lactamase-producing organisms, and carbapenem-resistant Enterobacteriaceae infection. Among ML models, a neural network classifier achieved the highest discriminative performance on the validation set (area under the curve = 0.957; 95% confidence interval: 0.905–1.000). MR indicated a modest but statistically significant association of genetically predicted diabetes with risk of sepsis (inverse-variance weighted P < .05; odds ratio = 1.08, 95% confidence interval: 1.02–1.13). In this single-center cohort of diabetic patients with Gram-negative Enterobacteriaceae sepsis, intensive care unit admission, shock, respiratory failure, hepatic abscess, impaired consciousness, cytopenias, and infections due to extended-spectrum β-lactamase- or carbapenem-resistant Enterobacteriaceae-producing organisms were key predictors of mortality. A neural network model demonstrated excellent discrimination for in-hospital death. MR indicates a causal effect of diabetes on sepsis risk.

Gram-negative Enterobacteriaceae bloodstream infection (BSI) is an increasingly recognized complication in patients with acute ischemic stroke (AIS) and is associated with substantial mortality. However, validated prognostic tools specifically targeting this high-risk population are lacking. This study aimed to identify the determinants of mortality in patients with AIS complicated by Enterobacteriaceae BSI and to construct a validated nomogram for individualized risk prediction. All adults with AIS who developed Enterobacteriaceae BSI between January 1, 2010 and August 31, 2025 at The First People’s Hospital of Jiande were retrospectively enrolled. After excluding subjects with ≥ 5% missing data, 125 patients were retained. Univariable logistic regression was first performed to identify candidate predictors of in-hospital mortality. Subsequently, the Boruta algorithm was applied to select the most informative variables, which were then incorporated into a logistic regression-based nomogram. Discrimination, calibration, and clinical utility were evaluated by the area under the receiver operating characteristic curve, calibration plots (1000 bootstrap resamples), and decision curve analysis, respectively. Overall in-hospital mortality was 12.8% (16/125). After Boruta selection, 5 variables remained independently associated with mortality: admission to the intensive care unit, concurrent shock and respiratory failure, decreased hemoglobin, and decreased platelet count. The nomogram demonstrated excellent discrimination (area under the curve = 0.936, 95% CI = 0.866–0.988), good calibration, and superior net clinical benefit across a wide range of threshold probabilities in decision curve analysis. Intensive care unit admission, concomitant shock and respiratory failure, anemia, and decreased platelet count are key determinants of death in AIS patients with Enterobacteriaceae BSI. The proposed nomogram offers clinicians a practical, bedside tool for early risk stratification and may guide individualized therapeutic decisions in this vulnerable population.

The aim of this study was to investigate the effect of lactic acid down-regulation of hexokinase 1 (HK1) on non-small cell lung cancer (NSCLC) through histone lactylation by Mendelian randomization (MR). Genes related to HK1 and NSCLC targets were analyzed using genome-wide association study summary statistics downloaded from public databases. Single-nucleotide polymorphisms associated with HK1 were selected as instrumental variables, and MR analyses were performed using inverse-variance weighted as the primary study method, and MR-Egger, weighted median, simple mode, and weighted mode as complementary methods in order to assess whether HK1 is a protective factor for NSCLC, and to provide further evidence that lactic acid is a risk factor for NSCLC. The results showed that HK1 was significantly and negatively associated with the risk of NSCLC using an inverse-variance weighted method (odds ratio = 0.93, 95% confidence interval = 0.89–0.98, P < .05). This suggests that for every 1-unit increase in genetically predicted HK1 expression, the odds of developing NSCLC decrease by approximately 7%. No significant heterogeneity or directional pleiotropy was detected (MR-Egger intercept P > .05). Leave-one-out sensitivity analysis confirmed the robustness of the findings. This MR study provides genetic evidence that lactic acid may down-regulate HK1 expression via histone lactylation, thereby increasing the risk of NSCLC. However, limitations include the restriction to European ancestry, limited number of instrumental single-nucleotide polymorphisms due to stringent thresholds, and potential residual pleiotropy not captured by current methods. These factors may affect the generalizability and causal interpretability of the findings.

Chronic bronchitis (CB), as a core precursor of Chronic Obstructive Pulmonary Disease (COPD), is crucial for global disease burden prevention and control. Although the association between heavy metal exposure and respiratory damage has been preliminarily demonstrated, traditional linear models are difficult to resolve the nonlinear interactions and dose–response heterogeneity. The aim of this study was to construct the first heavy metal exposure-chronic bronchitis risk prediction model by integrating exposureomics data through machine learning (ML). Weighted logistic regression was used to assess the association of 14 blood and urine heavy metals with CB based on nationally representative samples from the 2005–2015 National Health and Nutrition Examination Survey (NHANES). The Boruta algorithm was further applied to screen the characteristic variables and construct 10 ML models. The best model was selected by four evaluation metrics: accuracy, specificity, sensitivity, and area under the ROC curve (AUC), and the best model was visually interpreted using Shapley's additive interpretation (SHAP). The multifactorial logistic regression model showed that urinary cadmium (OR = 1.53, 95% CI = 1.17–1.98) versus blood cadmium (OR = 1.36, 1.13–1.65) was an independent risk factor for CB. The CatBoost model had the best predictive performance (AUC = 0.805), with smoking as the most significant predictor, followed by blood cadmium concentration and gender. In this research, the first risk prediction diagnostic model for heavy metal-chronic bronchitis was developed, in which CatBoost model had the best performance, and it provides a referenceable prediction model for the screening of high-risk groups.

Purpose: This study aims to establish a risk prediction model for muscular calf vein thrombosis (MCVT) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods: The research sample consisted of 248 patients with AECOPD and all of them underwent vascular ultrasounds of both lower limbs in this retrospective study. Univariate analysis and multivariate logistic regression analysis were conducted on factors with significant group differences to screen for the independent risk factors of MCVT. A nomogram to predict the risk of MCVT was constructed and validated with bootstrap resampling. Results: According to the exclusion criteria, 240 patients were included for analysis, divided into the MCVT group (n = 81) and the non-MCVT group (n = 159). Multivariate logistic regression analyses showed that hypertension, elevated MPV, reduced albumin (ALB), elevated D-dimer and bed rest ≥ 3 days were independent risk factors for MCVT in AECOPD. A nomogram model for predicting AECOPD with MCVT was established based on them. The area under the curve (AUC) of receiver operating characteristic (ROC) curve for the prediction model and the simplified Wells score was 0.784 (95% CI: 0.722– 0.847) and 0.659 (95% CI: 0.583– 0.735), respectively. The cut-off value and Youden index of prediction model were 0.248 and 0.454, respectively. At the same time, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 85.9%, 59.5%, 84.6%, and 77.4%, respectively. The sensitivity and specificity of the simplified Wells score were 67.9% and 56.3%, respectively. Validation by the use of bootstrap resampling revealed optimal discrimination and calibration, and the decision analysis curve (DAC) suggested that this prediction model involved high clinical practicability. Conclusion: We developed a nomogram that can predict the risk of MCVT for AECOPD patients. This model has the potential to assist clinicians in making treatment recommendations and formulating corresponding prevention measures.

Background: Several miRNAs are now known to have clear connections to the pathogenesis of asthma. The present study focused on the potential role of miR-3934 during asthma development. Methods: miR-3934 was detected as a down-regulated miRNA in basophils by sequencing analysis. Next, the expression levels of miR-3934 in peripheral blood mononuclear cells of 50 asthma patients and 50 healthy volunteers were examined by RT-qPCR methods. The basophils were then treated with AGEs and transfected with miR-3934 mimics. The apoptosis levels were examined by flow cytometry assay; and the expression levels of cytokines were detected using the ELISA kits. Finally, the Western blot was performed to examined the expression of key molecules in the TGF-β/Smad signaling pathway. Results: miR-3934 was down-regulated in the basophils of asthmatic patients. The expression of the pro-inflammatory cytokines IL-6, IL-8 and IL-33 was enhanced in basophils from asthmatic patients, and this effect was partially reversed by transfection of miR-3934 mimics. Furthermore, receiver operating characteristics analysis showed that miR-3934 levels can be used to distinguish asthma patients from healthy individuals. miR-3934 partially inhibited advanced glycation end products-induced increases in basophil apoptosis by suppressing expression of RAGE. Conclusion: Our results indicate that miR-3934 acts to mitigate the pathogenesis of asthma by targeting RAGE and suppressing TGF-β/Smad signaling.

Purpose: Metagenomic next-generation sequencing (mNGS) is a novel technique of pathogens detection that plays an increasingly important role in clinical practice. In this study, we explored the application value of mNGS in pulmonary infection combined with pleural effusion applied to samples of pleural effusion fluid. Patients and Methods: We reviewed 80 cases of pulmonary infection with pleural effusion between August 2020 and October 2021. Among them, 40 patients were placed in the mNGS group and underwent both culture and mNGS testing; the patients in the control group were only subjected to culture test. The effectiveness of mNGS was evaluated for microbial composition and diagnostic accuracy in every pleural effusion specimen type. Results: We found that the positive rate of mNGS was 70% (28/40). The comparison between mNGS and culture method resulted that the sensitivity was 100% (95% CI: 29.2– 100%) and the specificity was 64.9% (95% CI: 47.5– 79.8%). The positive predictive value of mNGS was 18.8% (95% CI, 13.0– 26.3%), and the negative predictive value was 100%. The most commonly identified potential pathogens were bacteria, such as Streptococcus, Prevotella, Parvimonas, Porphyromonas and Gemella. The most detected fungal infection was Candida and Pneumocystis. A total of 11 patients were identified as mixed infection by mNGS. Treatment regimen adjustments were made according to mNGS results and the overall length of hospital stay in the mNGS group was shorter compared to that of the control group. Conclusion: In this study, mNGS produced higher positive rates than the culture method in detecting pathogens in the pleural effusion specimens. The technology performed satisfactorily, providing more diagnostic evidence and reducing the length of hospital stay.

Background Several miRNAs are now known to have clear connections to the pathogenesis of asthma. The present study focused on the potential role of miR-3934 during asthma development. Results miR-3934 was down-regulated in the basophils of asthmatic patients. The expression of the pro-inflammatory cytokines IL-6, IL-8 and IL-33 was enhanced in basophils from asthmatic patients, and this effect was partially reversed by transfection of miR-3934 mimics. Furthermore, receiver operating characteristics analysis showed that miR-3934 levels can be used to distinguish asthma patients from healthy individuals. miR-3934 partially inhibited advanced glycation end products-induced increases in basophil apoptosis by suppressing expression of RAGE. Conclusion Our results indicate that miR-3934 acts to mitigate the pathogenesis of asthma by targeting RAGE and suppressing TGF-β/Smad signaling.

Analysis of Risk Factors for Thromboembolic Events in 88 Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Descriptive Report - Article abstract #929708