ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Acute lung injury (ALI) is a significant complication of sepsis, characterized by high morbidity, mortality, and poor prognosis. Neutrophils, as critical intrinsic immune cells in the lung, play a fundamental role in the development and progression of ALI. During ALI, neutrophils generate neutrophil extracellular traps (NETs), and excessive NETs can intensify inflammatory injury. Research indicates that Taohe Chengqi decoction (THCQD) can ameliorate sepsis-induced lung inflammation and modulate immune function. This study aimed to investigate the mechanisms by which THCQD improves ALI and its relationship with NETs in sepsis patients, seeking to provide novel perspectives and interventions for clinical treatment. The findings demonstrate that THCQD enhanced survival rates and reduced lung injury in the cecum ligation and puncture (CLP)-induced ALI mouse model. Furthermore, THCQD diminished neutrophil and macrophage infiltration, inflammatory responses, and the production of pro-inflammatory cytokines, including interleukin-1β (IL-1β), IL-6, and tumor necrosis factor α (TNF-α). Notably, subsequent experiments confirmed that THCQD inhibits NET formation both in vivo and in vitro. Moreover, THCQD significantly decreased the expression of peptidyl arginine deiminase 4 (PAD4) protein, and molecular docking predicted that certain active compounds in THCQD could bind tightly to PAD4. PAD4 overexpression partially reversed THCQD's inhibitory effects on PAD4. These findings strongly indicate that THCQD mitigates CLP-induced ALI by inhibiting PAD4-mediated NETs.
Extracellular Traps/immunology* ; Acute Lung Injury/immunology* ; Animals ; Sepsis/immunology* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Neutrophils/immunology* ; Male ; Protein-Arginine Deiminase Type 4/genetics* ; Mice, Inbred C57BL ; Humans ; Disease Models, Animal ; Cytokines/metabolism*

Objective To construct nucleotide-binding oligomerization domain-like receptor protein 3(NLRP3)knockout mice in order to investigate the effects of NLRP3 knockout on radiation-induced acute pneumonitis and pulmonary fibrosis.Methods Nlrp3+/+and Nlrp3-/-mice were randomly divided into the control group and irradiation group.To induce radiation-caused acute pneumonitis,the control group was exposed to sham irradiation while the irradiation group was exposed to 60Co γ-rays at a dose of 22 Gy at a dose rate of 184.30 R/min.At 14 days post-irradiation,the body weight of each mouse and the wet weight of its lung tissue were measured separately using an analytical balance to calculate the lung coefficient.Quantitative real-time PCR(qPCR)and cytometric bead array(CBA)were used to detect inflammatory responses in lung tissues and serum.Hematoxylin-eosin(HE)staining and F4/80 immunohistochemical staining were used to assess pathological changes and inflammatory cell infiltration in lung tissues.Cysteinyl aspartate specific proteinase-1(caspase-1)activation was analyzed by Western blotting.To establish a model of radiation-induced pulmonary fibrosis,mice were irradiated with 60Co γ-rays at a dose of 18 Gy at a dose rate of 174.67 R/min.At 24 weeks post-irradiation,HE staining and Masson staining were performed to evaluate pulmonary fibrosis.Results NLRP3 knockout inhibited caspase-1 activation,reduced inflammatory responses in lung tissues and serum,suppressed macrophage infiltration,alleviated pulmonary edema,and thereby protected against acute radiation-induced lung injury.Additionally,NLRP3 knockout significantly ameliorated late-stage radiation-induced pulmonary fibrosis.Conclusion NLRP3 knockout can mitigate both early radiation-induced pneumonia and lateradiation-induced pulmonary fibrosis.

Objective To explore the role of dapansutrile(OLT1177)in radiation-induced intestinal injury and the mechanism.Methods C57BL/6J mice were locally irradiated in the abdomen with 60Co to induce a model of radiation-induced intestinal injury.OLT1177 was intraperitoneally injected at a dose of 100 mg/kg 2 hours before irradiation and 6 hours after irradiation before the drug was administered once a day.At 12 hours after irradiation,intestinal tissues were taken for terminal deoxynucleotidyl transferase mediated nick end labeling(TUNEL)staining to detect apoptosis in intestinal tissues.At 4 days after irradiation,mouse serum was collected to detect the levels of inflammatory factors in the serum.Hematoxylin-eosin(HE)stainingwas used for the evaluation of the damage to the intestinal villus structure.Immunohistochemical staining was adopted to detect the changes in crypt proliferation in intestinal tissues.Finally,proteins were isolated from intestinal tissues,and Western blotting was employed to evaluate the activation of nucleotide-binding oligomerization domain-like receptor protein 3(NLRP3)inflammasome.Results After irradiation,the intestinal villi in mice were shortened.Meanwhile,there was a notable declinein the number of cells that were proliferating in the crypts,a surge in the number of apoptotic cells,and a significant spike in the overall inflammatory level.However,administration of OLT1177 inhibited the activation of the NLRP3 inflammasome,reduced apoptosis and pyroptosis,decreased the inflammatory level,and thus improved radiation-induced intestinal injury.Conclusion Administration of OLT1177 can significantly mitigate radiation-induced intestinal injury.

Objective To determine the content and distribution characteristics of the artificial radionuclide ¹³⁷Cs and the natural radionuclides ²¹⁰Pb, ²²⁶Ra, ²²⁸Ra, and ⁴⁰K in wild edible fungi, and calculate the committed effective dose due to ¹³⁷Cs and ²¹⁰Pb in wild edible fungi. Methods Thirty samples of wild edible fungi were collected and their caps and stems were separated. A total of 60 samples were measured for ¹³⁷Cs, ²¹⁰Pb, ²²⁶Ra, ²²⁸Ra, and ⁴⁰K using a BE5030 wide-energy, low-background, high-purity germanium γ spectrometer. The paired analysis of the four radionuclides ²²⁶Ra, ²¹⁰Pb, ¹³⁷Cs, and ⁴⁰K was performed using SPSS 11.5. Results Among the 60 samples, the detection rates and dry weight specific activity ranges of ¹³⁷Cs, ²¹⁰Pb, ²²⁶Ra, ²²⁸Ra, and ⁴⁰K were 97% and 0.62-384 Bq/kg, 73% and 6.4-159 Bq/kg, 52% and 0.7-28.8 Bq/kg, 5% and 0.43-2.18 Bq/kg and 100% and （77.4-264) × 10 Bq/kg, respectively. Conclusion Based on the analysis of the 60 samples, the detection rate of radionuclides is in the order of ⁴⁰K, ¹³⁷Cs, ²¹⁰Pb, ²²⁶Ra, and ²²⁸Ra. In terms of the specific activity, the distribution of ⁴⁰K and ²²⁶Ra in wild edible fungi in the same region is basically uniform, while the content of ²¹⁰Pb and ¹³⁷Cs fluctuates in different samples. Although ¹³⁷Cs and ²¹⁰Pb can be detected in most of the wild edible fungi, the annual committed effective dose due to ingestion of wild edible fungi is negligible.

Objective:To investigate the relationship between artificial radionuclide 137Cs and stable cesium (Cs) in wild edible fungi and seek potential correlations. Methods:A total of 30 samples, including the caps (with gills) and stipes of wild edible fungi, were collected from the northeastern region of China. The measurement and analysis of 137Cs were conducted following recommended procedures in GB/T 16145-2022, and stable Cs was determined using inductively coupled plasma mass spectrometry (ICP-MS). Then, the correlation analysis of data on 137Cs and stable Cs was performed using SPSS 11.5 software, and scatter plots were prepared using the Origin 21.0 software. Results:The fungi caps exhibited a specific activity of 137Cs ranging from 0.52 to 55.9 Bq/kg (dry weight) and a stable Cs content from 0.069 mg/kg to 16.2 mg/kg (dry weight). The stipes showed a specific activity of 137Cs ranging from 0.53 Bq/kg to 101 Bq/kg (dry weight) and a stable Cs content from 0.075 to 11.5 mg/kg (dry weight). These data revealed a significant correlation between the specific activity of 137Cs and the stable cesium content in all samples including caps and stipes, with correlation coefficients r of 0.956, 0.912, and 0.931, respectively, and all significant levels P < 0.01. The ratios of the specific activity of 137Cs to stable Cs content varied from 2.09 Bq/kg to 20.1 Bq/kg (dry weight), with an average of 10.7 Bq/kg (dry weight). Conclusions:Wild edible fungi fail to distinguish between 137Cs and stable cesium when absorbing Cs elements from their growing substrates. Furthermore, there is a strong correlation between the specific activity of 137Cs and stable Cs content. In the case of exogenous 137Cs contamination, the ratio of the specific activity of 137Cs and stable Cs content will significantly change. Therefore, an increase in the ratio can be used as a reference for identifying 137Cs contamination events.