Background In 2021, chronic obstructive pulmonary disease (COPD) emerged as the forth leading cause of death in the world. However, the impact of air pollutants on COPD is still inconsistent across current studies. Objective To analyze the relationship between ambient sulfur dioxide (SO₂) exposure and hospital admissions for COPD in Lanzhou, and to examine the modified effects of SO₂ across different genders, age groups, and seasons. Methods A total of 20718 hospital admission records for COPD were collected from Lanzhou between 2016 and 2020, alongside synchronized data on SO₂ concentration and meteorological variables. A generalized additive model integrated with a distributed lag nonlinear model was used to assess the relationship and the lag effects between SO₂ exposure and COPD admissions. Potential confounders, including meteorological factors, day-of-the-week effect, and holidays, were controlled for, with a maximum lag period set at 7 d. Two-pollutant models and sensitivity analyses were conducted to ensure robustness. Results are expressed as relative risks (RR) with 95% confidence intervals (95%CI). Results During the study period, the average daily number of COPD admissions was 11.34±7.03. The average mass concentration of SO₂ was (23.72±12.35) μg·m⁻³. SO₂ exposure was positively correlated with COPD admissions; specifically, each 10 μg·m⁻³ increase in SO₂ concentration was associated with an RR of 1.440 (95%CI: 1.317, 1.573). Stratified analyses found that at a cumulative lag of 7 d, the RRs were higher among females, individuals aged ≥65 years, and during the cold season. Conversely, no significant increase in COPD admission risk related to SO₂ was observed during the warm season. For every 10 μg·m⁻³ increase in SO₂ concentration, the RR was 1.483 (95%CI: 1.311, 1.678) for females, 1.441 (95%CI: 1.311, 1.583) for those aged > 65 years， and 1.595 (95%CI: 1.313, 1.937) during the cold season. Conclusion Short-term exposure to ambient SO₂ exposure in Lanzhou is associated with an increased risk of COPD admission. The association is more pronounced among females, the elderly (aged> 65 years) and during the cold season.

Objective:This study aims to develop a laboratory-based predictive model for early neurological deterioration (END) in patients with acute cerebral infarction (ACI) using baseline data collected at hospital admission.Methods:This study was a retrospective cohort study. Clinical and baseline laboratory test data from 502 patients with ACI admitted to the Department of Neurology at our hospital between January 1, 2022 and May 31, 2025. Of these patients, 313 were male and 189 were female, with a median age of 67 years (interquartile range: 58-73). Patients were classified into an END group and a non-END group according to the occurrence of END within 7 days of admission. Subsequently, using the caret package in R (version 4.4.2), the dataset was randomly divided into a training set ( n=351) and a validation set ( n=151) at a 7∶3 ratio, with END status as the stratification variable and a fixed random seed to ensure reproducibility. Following baseline characteristic comparisons between groups, these datasets were used for model development and validation, respectively. The differences in clinical indicators between the two patients groups were assessed using the chi-square test and the Wilcoxon rank sum test. In the training group, Lasso regression was utilized to identify variables significantly associated with END. Seven machine learning algorithms-decision tree (DT), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR)-were employed to develop predictive models. The optimal hyperparameters were determined via grid search integrated with 5-fold cross-validation. The final algorithm was selected based on comprehensive model performance evaluation. Additionally, clinical data of 79 patients with ACI, collected between June 1 to August 31, 2025, were compiled as an independent test set for external validation. The cohort comprised 49 males and 30 females, with a median age of 68 years (interquartile range: 57-72). The SHapley Additive exPlanations (SHAP) method was employed to access feature importance and model interpretability. SHAP dependence plots and interaction plots were utilized to emplore the nonlinear relationships and interaction effects among the featurevariables. Results:Among the 502 patients, 166 experienced END during 7 days of hospitalization. Lasso regression identified nine significant predictors: history of hyperlipidemia, admission NIHSS score, lymphocyte-to-monocyte ratio (LMR), hemoglobin, D-dimer, albumin, neuron-specific enolase (NSE), homocysteine (HCY), and vitamin B12. The area under the receiver operating characteristic curve (AUC) for the seven machine learning models ranged from 0.709 to 0.946. The XGB model achieved the highest predictive performance, with an AUC of 0.946 (95% CI 0.924-0.960) in the training cohort and 0.867 (95% CI 0.902-0.933) in the validation cohort. SHAP analysis revealed that the top five variables contributing to END prediction were admission NIHSS score, HCY, D-dimer, history of hyperlipidemia, and vitamin B12. Conclusion:This study successfully developed a laboratory-based prediction model for END using the XGB machine learning algorithm, which demonstrated strong predictive performance.

Objective:This study aims to develop a laboratory-based predictive model for early neurological deterioration (END) in patients with acute cerebral infarction (ACI) using baseline data collected at hospital admission.Methods:This study was a retrospective cohort study. Clinical and baseline laboratory test data from 502 patients with ACI admitted to the Department of Neurology at our hospital between January 1, 2022 and May 31, 2025. Of these patients, 313 were male and 189 were female, with a median age of 67 years (interquartile range: 58-73). Patients were classified into an END group and a non-END group according to the occurrence of END within 7 days of admission. Subsequently, using the caret package in R (version 4.4.2), the dataset was randomly divided into a training set ( n=351) and a validation set ( n=151) at a 7∶3 ratio, with END status as the stratification variable and a fixed random seed to ensure reproducibility. Following baseline characteristic comparisons between groups, these datasets were used for model development and validation, respectively. The differences in clinical indicators between the two patients groups were assessed using the chi-square test and the Wilcoxon rank sum test. In the training group, Lasso regression was utilized to identify variables significantly associated with END. Seven machine learning algorithms-decision tree (DT), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR)-were employed to develop predictive models. The optimal hyperparameters were determined via grid search integrated with 5-fold cross-validation. The final algorithm was selected based on comprehensive model performance evaluation. Additionally, clinical data of 79 patients with ACI, collected between June 1 to August 31, 2025, were compiled as an independent test set for external validation. The cohort comprised 49 males and 30 females, with a median age of 68 years (interquartile range: 57-72). The SHapley Additive exPlanations (SHAP) method was employed to access feature importance and model interpretability. SHAP dependence plots and interaction plots were utilized to emplore the nonlinear relationships and interaction effects among the featurevariables. Results:Among the 502 patients, 166 experienced END during 7 days of hospitalization. Lasso regression identified nine significant predictors: history of hyperlipidemia, admission NIHSS score, lymphocyte-to-monocyte ratio (LMR), hemoglobin, D-dimer, albumin, neuron-specific enolase (NSE), homocysteine (HCY), and vitamin B12. The area under the receiver operating characteristic curve (AUC) for the seven machine learning models ranged from 0.709 to 0.946. The XGB model achieved the highest predictive performance, with an AUC of 0.946 (95% CI 0.924-0.960) in the training cohort and 0.867 (95% CI 0.902-0.933) in the validation cohort. SHAP analysis revealed that the top five variables contributing to END prediction were admission NIHSS score, HCY, D-dimer, history of hyperlipidemia, and vitamin B12. Conclusion:This study successfully developed a laboratory-based prediction model for END using the XGB machine learning algorithm, which demonstrated strong predictive performance.

Talents are the core driving force to promote the high-quality development of public hospitals,and the talent team building is an important factor for hospitals to get sustainable development and improve comprehensive competitiveness.In view of the shortage of high-level talents,the imperfect talent reserve system,and the lagging personnel management mechanism of public hospitals and the resulting unreasonable talent team structure and talent gap,public hospitals should take talent team building as the starting point,pay equal attention to the introduction and training of talents,interact with discipline construction and talent construction,vigorously promote the construction of personnel management system and mechanism innovation,form a standardized,scientific,refined and efficient modern hospital management system,so that the effectiveness of talents can be max-imized,and provide intellectual support for the high-quality development of public hospitals.

ObjectiveIn the treatment of acute gouty arthritis （AGA）， western medicine is mostly used for anti-inflammatory and analgesic purposes to control the blood uric acid level， but some patients are still at risk of poor control and recurrent attacks. Chinese medicinal prescriptions， potent in resisting inflammation and relieving pain， are able to stabilize the blood uric acid level， reduce acute attacks， and improve the clinical efficacy of western medicine. However， there is a lack of evidence to support their use as evidence-based medicine. This study employed network Meta-analysis （NMA） to evaluate the efficacy and safety of common Chinese medicinal prescriptions in the treatment of AGA， aiming to provide evidence-based medical evidence for the clinical use of Chinese medicinal prescriptions in the treatment of AGA. MethodChinese and English databases were searched for prospective cohort studies and randomized controlled trials （RCTs） on Chinese medicinal prescriptions against AGA from database inception to December 1， 2022. Stata software and Review Manager were used for statistical analysis. ResultForty-four papers with 3 564 cases involved were included in the current NMA. In terms of reducing blood uric acid， the cumulative probability results showed that Mahuang Lianyao Chixiaodou Tang showed optimal efficacy （87.60%）. In terms of relieving joint pain， Danggui Niantongtang and Guizhi Shaoyao Zhimutang showed optimal efficacy （92.00% and 82.30%）. In terms of improving erythrocyte sedimentation rate （ESR）， Simiaowan was superior to other prescriptions （87.00%）. In terms of reducing C-reactive protein （CRP）， Simiaowan and Baihutang modified with Guizhitang showed superior efficacy （76.00% and 66.10%）. In terms of safety， except for the basic treatment group， Mahuang Lianyao Chixiaodou Tang had the lowest probability of adverse events， and Danggui Niantongtang had the highest probability of adverse reactions during treatment. According to the results of cluster analysis， Mahuang Lianyao Chixiaodou Tang and Simiaowan are effective and safe. ConclusionAccording to the results of NMA， Chinese medicinal prescriptions can assist in the treatment of AGA and improve the effectiveness of western medicine. For patients with AGA， clinicians can choose Mahuang Lianyao Chixiaodou Tang or Simiaowan as an auxiliary drug for routine western medicine treatment.

Background Occupational and environmental particulate matter may cause fibrosis, accompanied by RNA m⁶A modification changes. Neodymium oxide (Nd₂O₃) can cause mouse lung fibrosis, which contains a large number of fibroblasts. Objective To investigate m⁶A modification of tumor necrosis factor receptor-associated protein 6/nuclear factor-κB (TRAF6/NF-κB) signaling pathway in fibrosis of human embryonic lung fibroblasts induced by Nd₂O₃, and identify the key m⁶A modification sites of TRAF6. Methods Designed concentrations of Nd₂O₃ (0, 1.563, 3.125, 6.25, 12.5, 25, 50, 100, and 200 mg∙L⁻¹) were infected with HELF cells for 24 and 48 h, and cell viability was detected to determine exposure time and dose. Measurements included indicators of fibrosis [hydroxyproline (HYP) and transforming growth factor-β1 (TGF-β1)], m⁶A methylation level, methyltransferases (METTL3 and METTL14), demethylases (FTO and ALKBH5), reading proteins (YTHDC2 and YTHDF2), fibrosis-associated genes (collagen-І, vimentin, and α-SMA), and proteins related to signaling pathway (TRAF6, NFKB1, P65, and P-P65). The enrichment of m⁶A in TRAF6 mRNA was measured by methylated RNA immunoprecipitation-quantitative real-time PCR (MeRIP-qPCR). Results The results of cell viability indicated that 6.25, 12.5, 25 mg∙L⁻¹ Nd₂O₃ and 48 h exposure time were used for subsequent experiments. After 48 h exposure, compared with the control group, the HYP level in the 25 mg∙L⁻¹ Nd₂O₃ group was increased, and the levels of TGF-β1 in the 6.25, 12.5, and 25 mg∙L⁻¹ Nd₂O₃ groups were increased (P<0.05); the overall m⁶A methylation levels of HELF cells in the 12.5 and 25 mg∙L⁻¹ Nd₂O₃ groups were increased (P<0.05). At mRNA level, compared with the control group, the mRNA expression levels of methyltransferases METTL3 and METTL14 (6.25, 12.5, and 25 mg∙L⁻¹ Nd₂O₃) were increased (P<0.05); the mRNA expression level of reading protein YTHDF2 (6.25, 12.5, and 25 mg∙L⁻¹ Nd₂O₃) was increased (P<0.05), while the mRNA expression level of YTHDC2 (25 mg∙L⁻¹ Nd₂O₃) was decreased (P<0.05); the mRNA expression levels of demethylases FTO (12.5 and 25 mg∙L⁻¹ Nd₂O₃) and ALKBH5 (25 mg∙L⁻¹ Nd₂O₃) were decreased (P<0.05); the mRNA expression levels of fibrosis-related genes vimentin, α-SMA, and collagen-Ⅰ (6.25, 12.5, and 25 mg∙L⁻¹ Nd₂O₃) were increased (P<0.05); the mRNA expression levels of pathway-related genes TRAF6 (25 mg∙L⁻¹ Nd₂O₃) and NFKB1 (12.5 and 25 mg∙L⁻¹ Nd₂O₃) were increased (P<0.05). At protein level, compared with the control group, the expression levels of methyltransferases METTL3 (25 mg∙L⁻¹ Nd₂O₃) and METTL14 (12.5 and 25 mg∙L⁻¹ Nd₂O₃) were increased (P<0.05); the expression level of reading protein YTHDF2 (12.5 and 25 mg∙L⁻¹ Nd₂O₃) was increased, while the expression level of YTHDC2 (25 mg∙L⁻¹ Nd₂O₃) was decreased (P<0.05); the expression level of demethylase FTO (25 mg∙L⁻¹ Nd₂O₃) was decreased (P<0.05); the expression level of fibrosis-associated protein vimentin was increased at 25 mg∙L⁻¹ Nd₂O₃, and the expression levels of α-SMA and collagen-Ⅰ were increased at 12.5 and 25 mg∙L⁻¹ Nd₂O₃ (P<0.05); the expression levels of TRAF6 and P-P65 were increased at 25 mg∙L⁻¹ Nd₂O₃ (P<0.05). The MeRIP-qPCR results showed that compared with the control group, the concentrations of m⁶A in all Nd₂O₃ groups were significantly increased (P<0.05). Conclusions Upon exposure of HELF cells to Nd₂O₃, the alterations in fibrosis-related indexes increase the expression of some m⁶A methylases and decrease the expression of demethylases, thereby increasing the m⁶A methylase level, and may promote the progression of fibrosis by activating the TRAF6/NF-κB signaling pathway.

As a reversible and dynamic epigenetic marker, N⁶-adenylate methylation (m⁶A) modification is the most common mRNA modification in eukaryotes. This paper briefly described how m⁶A can influence RNA splicing, stability, and translation after transcription, and then participate in a variety of signaling pathways and biological and pathological processes, regulating cell proliferation, apoptosis, epithelial mesenchymal transformation (EMT) processes, and tumor invasion and metastasis. In addition, according to current studies, m⁶A methyltransferases (writers) are believed to promote EMT and tumor development, and readers and erasers both promote and inhibit EMT in different research objects. In this review, we summarized the mechanism of m⁶A modification and its role in cell transformation, and pointed out the direction of disease treatment.

Objective: To investigate the ameliorative effects of Mushroom on adipose tissue inflammation and glucolipid metabolism in mice fed a high-fat diet, and to provide a theoretical basis for the mechanisms of Mushroom regulating glucolipid metabolism and inflammatory responses. Methods: C57 BL/6 J mice were fed with normal diet(LF) group, high-fat diet(HF)group and high-fat diet + Mushroom(HF+Mushroom) group for 15 weeks.Then, body weight subcutaneous and epididymal white adipose tissue weight were measured. The morphological changes of adipose tissues were compared by HE staining, and the expression of genes related to inflamation, glycolysis and fatty acid oxidation pathways were detected by RT-PCR and Western blot. Results: Compared with the LF group, the HF group had increased body weight, increased subcutaneous and epididymal white fat weight and adipocyte size, and upregulated expression of tumor necrosis factor-α(TNF-α), interleukin-6(IL-6), monocyte chemoattractant protein-1(MCP-1), CD68, inducible nitric oxide synthase(iNOS), pyruvate kinase(PK), phosphofructokinase(PFK), hypoxia inducible factor-1α(HIF-1α) and peroxisome proliferator activated receptor alpha(PPARα) in adipose tissues, while the expression of carnitine palmitoyl transferase-1 A(CPT-1 A), cytochrome P450 4 a10(CYP4 a10) and medium-chain acyl-coenzyme a dehydrogenase(MCAD) were downregulated(P<0.05). Compared with the HF group, Mushroom supplementation reduced body weight, adipose tissue weight and adipocyte size, and downregulated the expression of pro-inflammatory factors and glycolytic pathway-related factors in adipose tissues, while the expression of fatty acid oxidation pathway-related factors were upregulated(P<0.05). Conclusion Mushroom can ameliorate inflammation and disorders of glycolipid metabolism in adipose tissues of obese mice.