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 systematically investigate the discrepancies between employment preferences and actual job placements among postgraduate students in rehabilitation medicine, explore the influencing factors, and elucidate the underlying mechanisms of their employment decisions. MethodsA cross-sectional questionnaire survey was conducted among 100 full-time postgraduate students who had graduated from Capital Medical University School of Rehabilitation in the past six years. A self-designed questionnaire was used to collect information on personal background, self-assessed competencies, employment preferences, and actual job placements. Logistic regression models were employed to analyze the factors influencing employment preferences and actual job placements, respectively. ResultsEmployment preferences among rehabilitation medicine postgraduates were highly concentrated on large tertiary hospitals (79.0%), yet the actual placement rate (61.0%) showed a significant gap. Preference for tertiary hospitals showed no significant association with individual factors such as gender, degree type or self-assessed competencies (P > 0.05). Univariate analysis revealed that those who entered tertiary hospitals scored significantly higher in self-assessed clinical skills, research ability, communication and coordination, and resource integration (|t| > 3.661, P < 0.001), and demonstrated clearer career planning (P = 0.012). Multivariate logistic regression analysis did not identify any independent predictors of actual employment within the context of this study sample (P > 0.05). ConclusionEmployment among rehabilitation medicine postgraduates is characterized by highly homogeneous preferences coexisting with complexity of factors influencing actual employment. It is recommended to optimize the training system, deepen personalized career education, and establish diversified employment guidance mechanisms to promote rational talent flow and facilitate the balanced development of the rehabilitation medical service system.

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 construct and validate a prognostic prediction model for first-line immune checkpoint inhibitors （ICIs） therapy for advanced non-small cell lung cancer （NSCLC）.METHODS The clinicopathological data and peripheral blood parameters from 211 patients with advanced NSCLC who received first-line ICIs therapy at our hospital from January 2020 to December 2023 were retrospective collected. The patients were randomly divided into a training set （151 cases） and a validation set（60 cases） at a ratio of 7∶3. Using progression-free survival （PFS） as the outcome measure， LASSO regression was used to preliminarily screen predictive variables in the training set，and the preliminarily screened predictive variables were entered into a multivariable Cox regression model to construct Model 1. Meanwhile，restricted cubic splinec analysis was performed on the continuous variables preliminarily screened by LASSO regression. The variables with nonlinear relationships （ P ＜0.1） were analyzed by multivariable Cox regression in RCS form to construct Model 2. The concordance index，receiver operating characteristic curves，calibration curves，and decision curve analysis were used to compare the predictive performance of the two models； the optimal model was selected to construct a nomogram.Sensitivity analysis was performed by reconstructing the multivariable Cox regression model after excluding cases with unknown programmed death-ligand 1（PD-L1）status. Kaplan-Meier method was used to plot survival curves to analyze the survival differences among patients with different risk strata.RESULTS The variables used to construct Model 1 were sex， smoking history， PD-L1 expression status， bone metastasis， and carcinoembryonic antigen （CEA）；the variables used to construct Model 2 were sex， smoking history， PD-L1 expression status， bone metastasis， CEA， neutrophil（Neu）， and albumin（ALB）. The concordance indices of Model 2 were 0.710 in the training set and 0.672 in the validation set，both superior to those of Model 1 （0.649，0.634）. The areas under the curve （AUC） of Model 2 for 12， 18， and 24 months PFS were 0.737，0.782 and 0.788 in the training set，and 0.709，0.630 and 0.663 in the validation set，respectively，all outperforming Model 1 （0.653， 0.704， 0.697 in the training set；0.688， 0.605， 0.651 in the validation set）.The calibration curves of both models showed good consistency between the predicted probability and actual probability，and the clinical net benefit of Model 2 was higher than that of Model 1. Sensitivity analysis indicated that the prediction model was robust. The median PFS of patients in the low-risk group was significantly longer than that in the high-risk group（ P ＜0.05）.CONCLUSIONS Sex， smoking history， PD-L1 expression status， bone metastasis， Neu， ALB， and CEA were independent influencing factors for PFS in advanced NSCLC patients receiving first-line ICIs. The prediction model constructed based on the above factors has good predictive performance and can be used to predict the prognosis of advanced NSCLC patients.

The compilation explanation for the Expert Consensus on Clinical Application of Ruyi Zhenbao pills systematically expounds the formulation methods and evidence-based basis of this consensus. In response to the weak clinical application evidence and ambiguous indications of Ruyi Zhenbao pills， the Institute of Basic Research in Clinical Medicine of China Academy of Chinese Medical Sciences， Wangjing Hospital of China Academy of Chinese Medical Sciences， and Dongzhimen Hospital of Beijing University of Chinese Medicine invited 39 experts from 31 medical institutions across China to complete the compilation through multi-disciplinary collaboration. The compilation strictly adheres to the World Health Organization （WHO） guidelines for guideline development and the GRADE evidence grading system. The work process covered the construction of clinical questions （a total of 30 clinical questions were determined based on expert voting results）， evidence retrieval （covering Chinese and English databases and gray literature）， safety assessment （integrating drug monitoring data and clinical research）， and the formation of recommendation opinions and consensus suggestions （18 recommendation opinions were reached through the GRADE grid method， and 22 consensus suggestions were reached through majority voting rules）. The results show that the consensus clearly states that this medicine is suitable for knee osteoarthritis， gouty arthritis， rheumatoid arthritis， neuropathic pain， and sequelae of stroke. In terms of safety， the adverse reactions are mainly gastrointestinal reactions such as nausea， vomiting， and abdominal pain. Pregnant women and those allergic to the ingredients of this medicine are contraindicated. The compilation process underwent three rounds of expert reviews and adopted methods such as nominal group technique and Delphi method to ensure the rigor of the consensus compilation process. It provides a paradigm for the formulation of expert consensus on clinical application of Tibetan medicine through the academic promotion and dynamic revision.

Objective To analyze the influencing factors of plastic bronchitis in children with Mycoplasma pneumoniae infection and put forward targeted prevention suggestions. Methods The clinical data of children with Mycoplasma pneumoniae infection who were admitted to Chengdu Third People's Hospital from September 2022 to February 2024 were retrospectively analyzed . According to whether plastic bronchitis occurred, they were divided into plastic group (n=118) and non-plastic group (n=184), and the differences between the two groups were compared and analyzed. Univariate and multivariate logistics regression analysis equations were used to analyze the independent influencing factors of plastic bronchitis in children with mycoplasma pneumoniae infection. Results Among the 302 children with Mycoplasma pneumoniae infection , 118 cases were diagnosed with plastic bronchitis. Analysis showed that the children’s age, duration of fever, hospital stay, pleural effusion rate, number of bronchoscopic lavage, allergy history, endoscopic mucosal erosion rate, WBC, NE%, LY%, CRP, LDH, PCT and D-D were the single factors influencing the occurrence of plastic bronchitis in children with mycoplasma pneumoniae infection. Binary logistics regression analysis revealed that age (OR=2.137, P=0.033, 95% CI: 1.132-16.603), allergy history （OR=3.028, P=0.014, 95% CI: 1.261-864), NE% (OR=2.395, P=0.031, 95% CI: 1.087-5.274), CRP (OR=3.864, P=0.004, 95% CI: 1.563-3.864), PCT (OR=4.125, P=0.001, 95% CI: 1.793-3.864), and D-D (OR=3.920, P=0.002, 95% CI: 1.632-3.864) were independent risk factors for plastic bronchitis in children with mycoplasma pneumoniae infection (P<0.05). Conclusion Age, allergy history, NE%, CRP, PCT and D-D are independent risk factors for plastic bronchitis in children with mycoplasma pneumoniae infection . It is necessary to take clinical intervention measures to reduce the occurrence risk.

OBJECTIVES: To explore the therapeutic mechanism of Arctium lappa extract for treatment of Post-Viral Pneumonia Pulmonary Fibrosis (PPF). METHODS: The chemical constituents of Arctium lappa extracts were identified using UHPLC-Q-TOF-MS/MS. Mouse models of pulmonary fibrosis established by tracheal instillation of bleomycin were treated with Arctium lappa extract, and body weight changes were recorded and lung tissue pathology was examined using HE and Masson staining. Metabolomics analysis was used to identify the differential metabolites and the associated metabolic pathways in the treated mice. The common targets of viral pneumonia and pulmonary fibrosis were acquired from the publicly available databases, and the core targets and active constituents were screened using the protein-protein interaction (PPI) network, GO and KEGG enrichment analyses, and molecular docking, and a "gene-metabolite" regulatory network was constructed. The expressions of the core targets were detected in the lung tissues of the treated mice using Western blotting. RESULTS: Fifty-three chemical constituents were identified from Arctium lappa extract. In the mouse models of pulmonary fibrosis, treatment with Arctium lappa extract significantly improved weight loss and ameliorated lung inflammation and fibrosis. The differential metabolites in the treated mice were enriched in energy metabolism pathways involving citrate cycle, pentose phosphate pathway, glycolysis, tryptophan metabolism, glutamate metabolism and glutathione metabolism, which regulated the production of energy metabolism intermediates. Twenty-three key active compounds (mostly lignans and phenolic acids) and 82 core targets were screened, which were associated with the non-canonical Smad signaling pathways (including PI3K/AKT, HIF-1, MAPK, and Foxo) that participated in the regulation of energy metabolism. Arctium lappa extract also regulated the expressions of epithelial-mesenchymal transition (EMT)‑related proteins (fibronectin, vimentim, and Snail, etc.) and inhibited MAPK signaling pathway activation. CONCLUSIONS Preliminary findings suggest that Arctium lappa treats fibrosis by regulating metabolism to inhibit EMT and involves the modulation of non-canonical Smad signaling pathways, such as MAPK providing theoretical support for its clinical application and further research in treating PPF.
Arctium/chemistry* ; Animals ; Pulmonary Fibrosis/metabolism* ; Mice ; Metabolomics ; Network Pharmacology ; Plant Extracts/pharmacology* ; Signal Transduction ; Drugs, Chinese Herbal/pharmacology* ; Molecular Docking Simulation

OBJECTIVE: To explore the effects of different endotracheal tube cuff pressure management modes on cuff sealing and the pressure exerted on the tracheal wall. METHODS: A prospective self-controlled study was conducted. Eleven patients undergoing endotracheal intubation and mechanical ventilation with an automatic airway management system (AGs) admitted to the Second Medical Centre of the Chinese People's Liberation Army General Hospital from October 1, 2020, to April 1, 2022, were enrolled as the study subjects. Within 24 hours after the establishment of artificial airway and mechanical ventilation, four cuff pressure management modes were randomly applied to each patient for 24 hours in sequence: automatic cuff pressure management mode [modeI: the safe range of cuff pressure was set at 20-35 cmH₂O (1 cmH₂O≈0.098 kPa), and the CO₂ pressure above the endotracheal tube cuff was automatically detected by AGs every 5 minutes to determine the cuff sealing status, and the cuff pressure was automatically adjusted], constant cuff pressure (25 cmH₂O) management mode (mode II: the cuff pressure was monitored by AGs through a pressure sensor, and the cuff pressure was maintained at 25 cmH₂O via a pressure pump), constant cuff pressure (30 cmH₂O) management mode (mode III: the cuff pressure was monitored by AGs through a pressure sensor, and the cuff pressure was maintained at 30 cmH₂O via a pressure pump), and manual cuff pressure management mode (mode IV: the cuff pressure was manually measured by nurses every 6-8 hours using a cuff pressure gauge to keep the cuff pressure at 25-30 cmH₂O after inflation). The CO₂ pressure above the endotracheal tube cuff (at 60-minute intervals) and the cuff pressure changes (at 50-ms intervals) were recorded to compare the differences in number of cuff leaks [no leak was defined as CO₂ pressure = 0, small leak as 0 < CO₂ pressure < 2 mmHg (1 mmHg≈0.133 kPa), and large leak as CO₂ pressure ≥ 2 mmHg] and cuff pressure among modesI-IV. RESULTS: A total of 24 CO₂ pressure measurements were taken per patient across the four modes, resulting in a total of 264 detections for each mode. Regarding the cuff leak, the total number of leak and large leak in modeIwas significantly lower than that in modes II-IV [total leak: 30 cases (11.36%) vs. 81 cases (30.68%), 70 cases (26.52%), 103 cases (39.02%); large leak: 15 cases (5.68%) vs. 50 cases (18.94%), 48 cases (18.18%), 66 cases (25.00%), all P < 0.05]. There was no significant difference in the number of cuff leak between modes II and III, and mode IV had the most severe cuff leak. In terms of cuff pressure, since mode IV required blocking the cuff tube from the AGs tube and the AGs cuff pressure management module did not actually work, real-time monitoring of cuff pressure was not possible. Therefore, cuff pressure changes were only analyzed in modes I-III. Each of the 11 patients underwent 24-hour cuff pressure monitoring under modes I-III, with 19 008 000 monitoring times for each mode. The cuff pressure in mode I was between that in modes II and III [cmH₂O: 27.09 (26.10, 28.14) vs. 26.60 (25.92, 27.47), 31.01 (30.33, 31.88), both P < 0.01]. Moreover, the number of extreme values of cuff pressure > 50 cmH₂O in mode I was significantly lower than that in modes II and III [19 900 cases (0.105%) vs. 22 297 cases (0.117%), 27 618 cases (0.145%), both P < 0.05]. CONCLUSION Dynamically monitoring the CO₂ pressure above the cuff to guide the adjustment of endotracheal tube cuff pressure can achieve better cuff sealing with a relatively lower cuff pressure load.
Humans ; Intubation, Intratracheal/instrumentation* ; Pressure ; Prospective Studies ; Respiration, Artificial ; Male ; Airway Management/methods* ; Female ; Middle Aged

Liver diseases have become a major challenge threating the global health, posing a heavy burden on both social and personal well-being. In recent years, the development of the gut-liver axis theory has provided new research perspectives and intervention strategies for the prevention and treatment of liver diseases. Natural products, recognized as biological molecules with diverse sources, rich activities, and minimal side effects, demonstrate great potential in regulating intestinal flora and improving liver health. Studies have shown that natural products such as saponins, polyphenols, polysaccharides, and alkaloids can regulate the composition and metabolites of intestinal flora, thereby intervening in liver diseases. In this paper, we systematically review the role of natural products in the regulation of the intestinal flora-gut-liver axis and summarize recent research progress in the prevention and treatment of liver diseases. Furthermore, we outline the challenges and limitations currently facing the study in this field. Finally, this paper makes an outlook on the clinical application of natural products in treating liver diseases and discusses future research directions, aiming to give new insights into the mechanisms by which natural products regulate the intestinal flora-gut-liver axis and the applications of these products in the prevention and treatment of liver diseases.
Humans ; Gastrointestinal Microbiome/drug effects* ; Liver Diseases/prevention & control* ; Biological Products/pharmacology* ; Polyphenols/pharmacology* ; Saponins/pharmacology* ; Intestines/microbiology* ; Alkaloids/pharmacology* ; Polysaccharides/pharmacology* ; Liver