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 investigate the mechanisms by which Renshentang treats atherosclerosis （AS） in mice， focusing on the regulation of endothelial inflammatory responses mediated by transient receptor potential vanilloid subtype 1 （TRPV1）. MethodsAn AS model was established in apolipoprotein E knockout （ApoE^-/-） mice fed a high-fat diet. The mice were randomly divided into a simvastatin group （0.02 g·kg^-1·d^-1） and low-， medium-， and high-dose Renshentang groups （1.77， 3.54， 7.08 g·kg^-1·d^-1）， with 12 mice in each group. ApoE^-/- mice were fed a high-fat diet and treated simultaneously. C57BL/6J mice fed a normal diet served as the normal group （n=9）. After continuous administration for 12 weeks， mice were anesthetized and the aortas were collected. Oil Red O staining was used to observe lipid plaque formation in the aorta. Hematoxylin-eosin （HE） staining was performed to examine pathological changes in the aortic root. Immunohistochemistry was used to analyze the levels of pro-inflammatory factors tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）， as well as the expression of TRPV1， phosphorylated phosphoinositide 3-kinase （p-PI3K）， and phosphorylated protein kinase B （p-Akt） in the aortic root. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect endothelial nitric oxide synthase （eNOS） mRNA expression in the aorta， and Western blot was used to detect TRPV1 protein expression. ResultsCompared with the normal group， the model group showed a significant increase in aortic plaque formation （P<0.01） and significantly elevated levels of TNF-α and IL-1β in the aortic root （P<0.01）. The expression levels of TRPV1， p-PI3K， and p-Akt were decreased （P<0.05， P<0.01）， and eNOS mRNA expression was reduced （P<0.05， P<0.01）. Compared with the model group， all Renshentang groups significantly reduced aortic plaque formation （P<0.01）， significantly decreased TNF-α and IL-1β levels （P<0.01）， and markedly increased the expression levels of TRPV1， p-PI3K， p-Akt， and eNOS mRNA （P<0.05， P<0.01）. ConclusionRenshentang may inhibit endothelial inflammation and suppress the formation of AS by increasing TRPV1 protein expression and up-regulating the PI3K/Akt/eNOS signaling pathway， which may be one of the molecular mechanisms underlying its therapeutic effect against AS.

ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective:To investigate the longitudinal relationships among resilience, social support, and negative emotions in college students.Methods:Questionnaire surveys were administered to 1 739 college students from a university in Hebei Province born in the same year in November 2020 and November 2021 respectively, and 1 183 valid responses were finally obtained. The survey battery comprised the 11-item resilience scale, the social support questionnaire, and the 21-item depression anxiety stress scales. Cross-lagged panel analysis was conducted using Mplus 8.3 software. Mediating effects were tested via the Bootstrap method.Results:The scores of resilience, social support and negative emotion at T1 were 58 (51, 65), 57 (51, 66), and 3 (0, 10), respectively. The scores of resilience, social support and negative emotion at T2 were 59 (48, 66), 56 (44, 65), and 11 (1, 24), respectively. Resilience at T1 and T2 were significantly and positively correlated with social support at T1 and T2 ( r=0.66, 0.75, 0.40, 0.33, all P<0.01), while negatively correlated with negative emotion at T1 and T2( r=-0.45, -0.23, -0.26, -0.24, all P<0.01). Social support at T1 and T2 were negatively correlated with negative emotion at T1 and T2( r=-0.44, -0.28, -0.28, -0.28, all P<0.01). T1 resilience significantly and positively predicted T2 resilience ( β=0.35) and T2 social support ( β=0.15). T1 social support significantly and positively predicted T2 resilience ( β=0.07) and T2 social support ( β=0.35), while negatively predicted T2 negative emotion ( β=-0.15). T1 negative emotion significantly and negatively predicted T2 resilience ( β=-0.06) and T2 social support ( β=-0.07). The total effect of T1 resilience on T2 negative emotion was -0.35, and the mediating effect of T2 social support was -0.09, accounting for 25.71% of the total effect. Conclusion:T2 social support partially mediates the relationship between T1 resilience and T2 negative emotions.

Objective To develop and validate a deep learning-based segmentation model for low-dose cervical spine X-ray,aiming to address the insufficient segmentation accuracy in low-dose protocols while balancing radiation protection and diagnostic accuracy.Methods A total of 1 363 patients cervical spine X-ray images data were collected.A dose-attenuation mathematical simulation sys-tem was constructed to generate 14 122 dynamic low-dose cervical spine images incorporating quantum noise,contrast degradation,and blur artifacts.A neural network model was developed for automated segmentation of low-dose cervical spine X-ray using this dataset.Results Within the dose range of 50%to 7.5%,the average reults of automatic segmentation by the neural network model and manual segmentation for each group were as follows:50%dose group,intersection over union(IoU)=0.98 vs 0.93(P=0.707)and Dice coefficient(Dice)=0.99 vs 0.96(P=0.749);10%dose group,IoU=0.97 vs 0.87(P=0.201)and Dice=0.99 vs 0.93(P=0.219);7.5%dose group,IoU=0.97 vs 0.67(P<0.01)and Dice=0.98 vs 0.80(P<0.01).Conclusion The developed deep learning model achieved robust cervical spine segmentation(IoU>0.96,Dice>0.98)below diagnostic dose thresholds[peak signal-to-noise ratio(PSNR)<38 dB,structural similarity index(SSIM)<0.65].Under ultra-low-dose conditions(PSNR=27.710 dB,SSIM=0.274),it demonstrated a 44.78%IoU improvement and 22.5%Dice improvement over manual segmentation.This model enables minimal radia-tion exposure while preserving diagnostic performance,confirming its theoretical feasibility for low-dose X-ray image analysis and clinical research potential.

Objective:To investigate the current status of clinical leadership among Interventional Operating Room nurses in Shandong Province and to analyze its influencing factors.Methods:In December 2024, 220 Interventional Operating Room nurses from 46 ClassⅢ Grade A general hospitals in Shandong Province were selected for a cross-sectional survey using convenience sampling. General Information Questionnaire, Clinical Leadership Survey, Chinese version of Wong and Law Emotional Intelligence Scale (WLEIS-C) , and Jefferson Scale of Empathy-Health Professionals (JSE-HP) were used as survey instruments. Multiple linear regression was used to analyze the factors influencing clinical leadership among Interventional Operating Room nurses. Pearson correlation analysis was used to explore the relationship between emotional intelligence, empathy, and clinical leadership.Results:A total of 220 questionnaires were distributed and 204 valid questionnaires were recovered, with a valid recovery rate of 92.73% (204/220) . The clinical leadership scores of the 204 Interventional Operating Room nurses were (66.35±8.74) , and the dimensions scored, in descending order, as encouraging the heart, enabling others to act, modeling the way, inspiring a shared vision, and challenging the process. The mean scores of WLEIS-C and JSE-HP items were (5.88±0.90) and (4.61±0.63) , respectively. Multiple linear regression analysis showed that the nature of employment and whether or not they participated in leadership training were influencing factors for clinical leadership among Interventional Operating Room nurses (partial regression coefficients of -2.831 and -2.999, respectively; P<0.05) . Emotional intelligence and empathy ability among Interventional Operating Room nurses were positively correlated with clinical leadership ( P<0.05) . Conclusions:Clinical leadership of nurses in the Interventional Operating Room of ClassⅢ Grade A general hospitals in Shandong Province is at a moderate to high level. Nurses who participate in leadership training, have staffing, have greater empathy, and have higher emotional intelligence have greater clinical leadership. It is recommended that nursing administrators add leadership-related courses to the training of nurses in the Interventional Operating Room, and take steps to improve nurses' emotional intelligence and empathy ability, thereby promoting clinical leadership among Interventional Operating Room nurses.

Objective To establish an efficient and stable model of pelvic inflammatory disease in rats via a non-surgical method,and to evaluate its application in pharmacodynamic testing.Methods Female Sprague-Dawley rats were divided randomly into the following groups:control group;model group with phenol for 7 d;model group with phenol for 10 d;treatment group modeled with phenol;model group with low concentration of bacteria;model group with high concentration of bacteria;and treatment group modeled with bacteria.Rats in the model and treatment groups were injected with 25%phenol gel and 2×107 or 2×108 Escherichia coli and Staphylococcus aureus mixture via a non-surgical method,to construct a rat model of pelvic inflammatory disease.Rats in the treatment groups received the Chinese patent medicine Jingangteng capsules by gavage,and rats in the control group received the same volume of solvent solution.The health status,weight changes,and uterine appearance were monitored and the uterine coefficient was calculated.Pathological changes in the uterus and fallopian tubes,endometrial thickness,and number of glands were detected by hematoxylin and eosin staining.Serum levels of interleukin(IL)-1β,IL-6,and tumor necrosis factor(TNF)-α were detected by enzyme-linked immunosorbent assay.Protein expression of the macrophage marker CD68 was detected by immunofluorescence.Expression of Toll-like receptor 4(TLR4)/nuclear factor(NF)-κB pathway-related proteins in the uterus was detected by Western blot.Results The mortality rate in the model group was only 5%.Compared with the control group,model rats showed decreased body weight,increased uterine coefficient,pathological changes in the uterus and Fallopian tubes,thinner endometrium,fewer glands,significantly higher serum levels of IL-1β,IL-6,and TNF-α and more macrophages in the uterine tissue,and activation of the TLR4/NF-κB signaling pathway.The 7 d phenol and low-concentration bacterial solution models were judged to be mild pelvic inflammatory disease models,and the 10 d phenol and high-concentration bacterial solution models were considered severe pelvic inflammatory disease models.Treatment with Jingangteng capsules relieved the pathological symptoms in the uterus and fallopian tubes,in line with the efficacy evaluation of clinical pelvic inflammatory disease.Conclusions We established rat models of pelvic inflammatory disease using phenol and a mixed bacterial solution via a non-surgical method,to simulate the different pathological states of pelvic inflammatory disease caused by different factors.These models will be suitable for evaluating drug efficacy and elucidating the pathological mechanism of pelvic inflammatory disease.

Objective To investigate the clinical value of serum leucine-rich α-2-glycoprotein 1(LRG1),uncoupling protein 2(UCP2)and sestrin2 in evaluating the cognitive status and prognosis of patients with acute ischemic stroke(AIS).Methods A total of 164 AIS patients who were treated and hospitalized in the Department of Neurology of our hospital from April 2020 to May 2023 were selected as the study subjects.According to the Chinese version of the Montreal cognitive assessment scale(MoCA),the patients were divided into cognitive impairment group and non-cognitive impairment group.The levels of serum LRG1,UCP2 and Sestrin2 were compared between the two groups.Pearson correlation analysis was used to test the correlation between serum LRG1,UCP2 and Sestrin2 and MoCA score.After discharge,the patients were followed up for 3 months.The mRS was used to divide the patients into poor prognosis group and good prognosis group.The data and serum levels of LRG1,UCP2 and Sestrin2 were compared between the two groups.Multivariate Logistic regression analysis was used to explore the prognostic factors of AIS patients.ROC curve was constructed to evaluate the predictive efficacy of serum LRG1,UCP2 and Sestrin2 alone and in combination on cognitive status and prognosis of AIS patients.Results There were 115 cases in the non-cognitive impairment group and 49 cases in the cognitive impairment group.The serum levels of LRG1 and UCP2 in the non-cognitive impairment group were significantly higher than those in the cognitive impairment group,while the Sestrin2 level was significantly lower than that in the cognitive impairment group(all P＜0.05).Serum LRG1 and UCP2 levels were positively correlated with MoCA score,while Sestrin2 level was negatively correlated with MoCA score(all P＜0.05).The area under the curve(AUC)of serum LRG1,UCP2 and Sestrin2 in cognitive status of AIS patients was 0.723,0.689 and 0.789,respectively,while the AUC of the combined detection of the three reached 0.892,and the 95％CI of the AUC of all indicators did not contain 0.5(all P＜0.05).There were 134 cases in the good prognosis group and 30 cases in the poor prognosis group.The NIHSS score,mRS score and Sestrin2 level in the good prognosis group were significantly lower than those in the poor prognosis group,while the MoCA score,LRG1 and UCP2 levels were significantly higher than those in the poor prognosis group(all P＜0.05).The increase of NIHSS score(OR=1.419),mRS score(OR=1.153)and serum Sestrin2 level(OR=1.462)were risk factors affecting the prognosis of AIS patients,while the increase of MoCA score(OR=0.657),serum LRG1 level(OR=0.756)and UCP2 level(OR=0.733)were protective factors affecting the prognosis of AIS patients(all P＜0.05).From the AUC value,the combination of the three(0.899)was significantly higher than Sestrin2(0.755),LRG1(0.730)and UCP2(0.712).The combination of sensitivity,specificity and Youden index was also significantly higher than other single indicators(all P＜0.05).Conclusion The combined detection of serum LRG1,UCP2 and Sestrin2 shows higher efficiency than single detection in the evaluation of cognitive status and prognosis of AIS patients,and its AUC,sensitivity,specificity and other indicators are better,providing more accurate and reliable evaluation basis for clinical practice.