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.

It is believed that "deficiency qi retention and stagnation" is the fundamental pathogenesis of coronary microvascular dysfunction （CMD） after percutaneous coronary intervention （PCI）. Patients often have severe coronary vessel congestion before PCI， leading to emptiness in the heart's collaterals， which results in deficiency of healthy qi， poor movement of blood and body fluids， so the heart collaterals are susceptible to stagnation and stasis，then phlegm and stasis generate； after PCI， it is easy to damage the healthy qi then lead to qi deficiency， causing qi， blood， and body fluids fail to transport， thereby leading to blood stasis and phlegm turbidity retention， generating heat and wind to damage the heart and body. It is proposed that the prevention before PCI should replenish qi and collaterals， expel blood stasis and resolve phlegm， to support "deficient qi" in heart collaterals and prevent "stagnation" after PCI. Postoperative management should focus on replenishing qi and protecting the collaterals， eliminating pathogen and controlling development， so as to avoid exacerbating deficiency and stagnation by damaging healthy qi， and eliminate pathogen and unblock the collaterals to interrupt the pathogenesis， which prevent "retention and stagnation" from changes.

Based on a letrozole-induced rat model of polycystic ovary syndrome (PCOS), serum metabolomics was employed to characterize metabolic abnormalities, identify potential biomarkers, and investigate their roles in the pathogenesis and progression of PCOS. Metabolomic analyses revealed significantly decreased levels of cholesterol, pregnenolone, leucine, and citrate in the serum of model rats, accompanied by elevated levels of androsterone glucuronide (ADTG) and linoleic acid, indicating dysregulation of key pathways including steroid biosynthesis, branched-chain amino acid metabolism, tricarboxylic acid (TCA) cycle, and lipid metabolism. To elucidate the tissue origins and molecular mechanisms underlying these metabolic alterations, ovarian proteomics and qRT-PCR analyses were further integrated. The results confirmed the upregulation of key enzymes involved in the related metabolic pathways, such as 17α-hydroxylase (CYP17A1), 11β-hydroxysteroid dehydrogenase (HSD11B1), branched-chain amino acid aminotransferase (BCAT2), fatty acid desaturase 2 (FADS2), and oxoglutarate dehydrogenase (OGDH). These findings suggest that both “cholesterol precursor depletion-androgen accumulation” and “energy/lipid metabolic reprogramming” constitute core features of metabolic disturbances in PCOS. Through multi-omic cross-validation, six serum metabolites with high stability and clinical translational potential were identified as promising combinational biomarkers for the auxiliary diagnosis of PCOS. This study employed a metabolomics-guided strategy, supported by proteomic and transcriptomic validation, which has not only deepened our understanding of PCOS metabolic mechanisms but also provided us with a theoretical foundation for its auxiliary diagnosis.

Objective:To dry fresh Ginseng Radix et Rhizoma using different drying conditions; To investigate the effects of different drying conditions on the drying characteristics and medicinal quality of Ginseng Radix et Rhizoma.Methods:With moisture, powder color, extract, total polysaccharide and ginsenoside contents of Rg 1, Re, Rf, Rb 1, Rc, Rb 2 and Rd as indexes, the drying characteristics of Ginseng Radix et Rhizoma were studied based on Weibull function model, and the quality of Ginseng Radix et Rhizoma after drying was evaluated by entropy weight-TOPSIS model. Results:The drying method for Ginseng Radix et Rhizoma from its origin can be achieved by controlling the relative humidity of the drying medium to 50%, drying at 70 ℃ for 24 h, and then reducing the drying temperature to 60 ℃ until the moisture content was below 12.0%. This method could achieve high drying efficiency and produce high-quality Ginseng Radix et Rhizoma.Conclusions:The drying process of Ginseng Radix et Rhizoma is a falling rate process controlled by internal moisture diffusion. The drying rate of fresh Ginseng Radix et Rhizoma is affected by temperature and humidity. There is a certain correlation between the color of powder and the content of moisture, alcohol-soluble extractives and ginsenosides.

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.

Objective:To investigate the effects of anisodamine hydrobromide (654-1), 654-1+ norepinephrine and norepinephrine on early hemodynamic indexes of piglets with septic shock.Methods:A total of 38 healthy Bama pigs were selected as the study subjects, 32 of which were treated with lipopolysaccharide to create septic shock piglet model, and the other 6 were sham operation group. The animals were randomly divided into control group ( n=8), drug treatment group [654-1 group ( n=8), 654-1+ norepinephrine group ( n=8), norepinephrine group ( n=8)]. Hemodynamic parameters were recorded at T 0 (basic state), T 1 (successful shock modeling), T 2 (1 h after successful modeling), T 3 (2 h after successful modeling), T 4 (4 h after successful modeling), T 5 (6 h after successful modeling) and T 6 (8 h after successful modeling) respectively, including: Mean arterial pressure (MAP), cardiac index (CI), whole-heart end-diastolic volume index (GEDI), lactic acid (LAC). Results:Except for the sham operation group, MAP of all treatment groups at T 1 was significantly lower than that at T 0 (all P<0.05). MAP of all treatment groups at T 2-T 6 was significantly higher than that at T 1 (all P<0.05). T 1 MAP of all treatment groups was significantly lower than that of the sham operation group (all P<0.05). MAP at T 2-T 6 in the norepinephrine group and the 654-1+ norepinephrine group was higher than that in the control group (all P<0.05), and MAP at T 2-T 4 in the 654-1 group was significantly lower than that in the 654-1+ norepinephrine group (all P<0.05). LAC of all treatment groups at T 1-T 3 was significantly higher than that at T 0 (all P<0.05) except the sham operation group. LAC in the group 654-1 at T 4 to T 6 was significantly lower than that at T 1 (all P<0.05). LAC in the group 654-1 at T 4-T 6 was significantly lower than that in the norepinephrine group and the control group (all P<0.05). The CI of norepinephrine group at T 2, T 5 and T 6 was lower than that at T 0 (all P<0.05). There was no significant difference in CI between T 2 and T 6 compared with T 1 (all P>0.05). CI of the 654-1+ norepinephrine group at T 4 was significantly lower than that of T 0 ( P<0.05); The CI of the 654-1 group at T 2 was significantly higher than that of T 1 ( P<0.05). CI at T 1 in the 654-1+ norepinephrine group was significantly lower than that in the sham operation group (all P<0.05). The GEDI at T 1 to T 5 in the 654-1 group was significantly lower than that at T 0 in the 6541+ norepinephrine group (all P<0.05), and the GEDI at T 1 to T 2 was significantly lower than that at T 0 in the 6541+ norepinephrine group (all P<0.05), while the GEDI at T 2 and T 4 was higher than that at T 1 (all P<0.05). Conclusions:MAP decreased significantly in septic shock, LAC increased significantly in the early stage of shock. 654-1 can improve MAP in early stage of septic shock, and significantly reduce LAC level in early stage of septic shock.

Objective:To investigate the effects of anisodamine hydrobromide (654-1), 654-1+ norepinephrine and norepinephrine on early hemodynamic indexes of piglets with septic shock.Methods:A total of 38 healthy Bama pigs were selected as the study subjects, 32 of which were treated with lipopolysaccharide to create septic shock piglet model, and the other 6 were sham operation group. The animals were randomly divided into control group ( n=8), drug treatment group [654-1 group ( n=8), 654-1+ norepinephrine group ( n=8), norepinephrine group ( n=8)]. Hemodynamic parameters were recorded at T 0 (basic state), T 1 (successful shock modeling), T 2 (1 h after successful modeling), T 3 (2 h after successful modeling), T 4 (4 h after successful modeling), T 5 (6 h after successful modeling) and T 6 (8 h after successful modeling) respectively, including: Mean arterial pressure (MAP), cardiac index (CI), whole-heart end-diastolic volume index (GEDI), lactic acid (LAC). Results:Except for the sham operation group, MAP of all treatment groups at T 1 was significantly lower than that at T 0 (all P<0.05). MAP of all treatment groups at T 2-T 6 was significantly higher than that at T 1 (all P<0.05). T 1 MAP of all treatment groups was significantly lower than that of the sham operation group (all P<0.05). MAP at T 2-T 6 in the norepinephrine group and the 654-1+ norepinephrine group was higher than that in the control group (all P<0.05), and MAP at T 2-T 4 in the 654-1 group was significantly lower than that in the 654-1+ norepinephrine group (all P<0.05). LAC of all treatment groups at T 1-T 3 was significantly higher than that at T 0 (all P<0.05) except the sham operation group. LAC in the group 654-1 at T 4 to T 6 was significantly lower than that at T 1 (all P<0.05). LAC in the group 654-1 at T 4-T 6 was significantly lower than that in the norepinephrine group and the control group (all P<0.05). The CI of norepinephrine group at T 2, T 5 and T 6 was lower than that at T 0 (all P<0.05). There was no significant difference in CI between T 2 and T 6 compared with T 1 (all P>0.05). CI of the 654-1+ norepinephrine group at T 4 was significantly lower than that of T 0 ( P<0.05); The CI of the 654-1 group at T 2 was significantly higher than that of T 1 ( P<0.05). CI at T 1 in the 654-1+ norepinephrine group was significantly lower than that in the sham operation group (all P<0.05). The GEDI at T 1 to T 5 in the 654-1 group was significantly lower than that at T 0 in the 6541+ norepinephrine group (all P<0.05), and the GEDI at T 1 to T 2 was significantly lower than that at T 0 in the 6541+ norepinephrine group (all P<0.05), while the GEDI at T 2 and T 4 was higher than that at T 1 (all P<0.05). Conclusions:MAP decreased significantly in septic shock, LAC increased significantly in the early stage of shock. 654-1 can improve MAP in early stage of septic shock, and significantly reduce LAC level in early stage of septic shock.

In recent years, an increasing number of emerging environmental pollutants have been identified, garnering widespread attention. Many of these pollutants are characterized by their environmental persistence and bioaccumulation, which pose significant threats to both the ecological environment and human health. However, the molecular mechanisms underlying their effects remain unclear, limiting our ability to assess their adverse impacts and develop effective protective measures. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor traditionally known to be activated by dioxins and polycyclic aromatic hydrocarbons (PAHs) and is involved in the metabolism of exogenous chemicals. Recent research has shown that the AHR can be activated by a diverse range of exogenous and endogenous chemicals and participates in various biological processes. Studies have demonstrated that AHR mediates the toxic effects of emerging environmental pollutants such as perfluorooctane sulfonamide (PFOSA) and N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine quinone (6PPDQ). This paper provided an overview of the AHR activation and the toxic effects induced by emerging environmental pollutants, with a focus on how the AHR activation interacts with multiple signaling pathways. The significance of these interactions in environmental risk assessment and toxicological research was also discussed. We aim to provide a scientific basis for environmental protection and risk assessment.

OBJECTIVE To explore the color and odor changes of Salvia miltiorrhiza Bge.slices from different origins,and com-bine modern machine learning technology to achieve rapid differentiation of origins.METHODS Intelligent sensory technology was used to quantify the color and represent the odor of Salvia miltiorrhiza Bge.slices from different geographical origins.Various data a-nalysis methods including principal component analysis(PCA),discriminant analysis,discriminant factor analysis(DFA),component heat maps,correlation analysis,machine learning and so on,were employed to establish a discrimination function for distinguishing the origin of Salvia miltiorrhiza Bge.slices based on color data.RESULTS Classification and screening of odor information led to the i-dentification of 10 differential markers:ethanol,carbon disulfide,cyclopentane,3-methylfuran,propylene glycol,nonane,phenol,1,5-octadienone,1,8-cineole,and sotolon.It was also found that there was a significant correlation between the color and odor of the slices.Furthermore,based on the concept of data fusion,the study established classification models such as subspace clustering,and compared to single-color discriminant analysis,the classification accuracy was improved to 94.4%.CONCLUSION The feasibility and superiority of intelligent sensory technology in classifying the geographical origin of TCM is confirmed,providing new methods and insights for quality control of Salvia miltiorrhiza Bge.slices.