ObjectiveThis study systematically analyzed the arginine metabolic dysregulation in the rat model of heart failure （HF）， providing a modern scientific basis for elucidating the pathogenesis of HF and offering new insights for the prevention and treatment of HF with traditional Chinese medicine （TCM）. MethodsA thoracotomy was performed to ligate the left anterior descending coronary artery of rats， which induced acute myocardial ischemia and thus led to the development of post-myocardial infarction heart failure. The rats were divided into a sham surgery group and a model group， with eight rats in each group. Serum targeted metabolomics analysis was performed using ultra-performance liquid chromatography-triple quadrupole mass spectrometry （UPLC-TQ-S）， and the spatial distribution of metabolites in cardiac tissue was observed using airflow-assisted desorption electrospray ionizationmass spectrometry imaging （AFADESI-MSI）. Targets associated with HF and arginine metabolism were screened from databases including GeneCards and the Gene Expression Omnibus （GEO）， a protein-protein interaction （PPI） network was constructed， and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway and Gene Ontology （GO） was performed. Finally， molecular docking was conducted to verify the binding between core metabolic components and key targets， and potential TCMs were predicted based on the core pathways and targets. ResultsCompared with the sham surgery group， the levels of arginine and citrulline in the serum of model rats were significantly decreased （P<0.01）， while those of proline， ornithine， creatine， creatinine and glutamate were significantly increased （P<0.05， P<0.01）. Cardiac mass spectrometry imaging showed a decreased abundance of arginine in the local myocardial tissue. Bioinformatics analysis identified 24 core functional targets， such as the angiotensin-converting enzyme （ACE）， neuronal nitric oxide synthase （NOS1）， 5-hydroxytryptamine receptor 2A （HTR2A）， and epidermal growth factor receptor （EGFR）， and enrichment analysis indicated that these targets were significantly involved in the calcium signaling pathway， neuroactive ligand-receptor interactions， and phosphatidylinositol signaling pathway. Molecular docking confirmed strong binding activities between arginine， citrulline and HTR2A， as well as between creatine， creatinine and EGFR. Based on pathway-target prediction， potential TCM interventions， such as ginseng and magnolia， were identified. ConclusionThis study revealed characteristic arginine metabolic disorder in HF， and the core targets of HF were closely associated with the phosphatidylinositol signaling pathway. It provides a modern biological interpretation of the pathogenesis of HF in TCM from the perspectives of metabolites and signaling pathways， and offers valuable insights for targeted therapy of HF and the development of TCM.

ObjectiveThis study systematically analyzed the arginine metabolic dysregulation in the rat model of heart failure （HF）， providing a modern scientific basis for elucidating the pathogenesis of HF and offering new insights for the prevention and treatment of HF with traditional Chinese medicine （TCM）. MethodsA thoracotomy was performed to ligate the left anterior descending coronary artery of rats， which induced acute myocardial ischemia and thus led to the development of post-myocardial infarction heart failure. The rats were divided into a sham surgery group and a model group， with eight rats in each group. Serum targeted metabolomics analysis was performed using ultra-performance liquid chromatography-triple quadrupole mass spectrometry （UPLC-TQ-S）， and the spatial distribution of metabolites in cardiac tissue was observed using airflow-assisted desorption electrospray ionizationmass spectrometry imaging （AFADESI-MSI）. Targets associated with HF and arginine metabolism were screened from databases including GeneCards and the Gene Expression Omnibus （GEO）， a protein-protein interaction （PPI） network was constructed， and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway and Gene Ontology （GO） was performed. Finally， molecular docking was conducted to verify the binding between core metabolic components and key targets， and potential TCMs were predicted based on the core pathways and targets. ResultsCompared with the sham surgery group， the levels of arginine and citrulline in the serum of model rats were significantly decreased （P<0.01）， while those of proline， ornithine， creatine， creatinine and glutamate were significantly increased （P<0.05， P<0.01）. Cardiac mass spectrometry imaging showed a decreased abundance of arginine in the local myocardial tissue. Bioinformatics analysis identified 24 core functional targets， such as the angiotensin-converting enzyme （ACE）， neuronal nitric oxide synthase （NOS1）， 5-hydroxytryptamine receptor 2A （HTR2A）， and epidermal growth factor receptor （EGFR）， and enrichment analysis indicated that these targets were significantly involved in the calcium signaling pathway， neuroactive ligand-receptor interactions， and phosphatidylinositol signaling pathway. Molecular docking confirmed strong binding activities between arginine， citrulline and HTR2A， as well as between creatine， creatinine and EGFR. Based on pathway-target prediction， potential TCM interventions， such as ginseng and magnolia， were identified. ConclusionThis study revealed characteristic arginine metabolic disorder in HF， and the core targets of HF were closely associated with the phosphatidylinositol signaling pathway. It provides a modern biological interpretation of the pathogenesis of HF in TCM from the perspectives of metabolites and signaling pathways， and offers valuable insights for targeted therapy of HF and the development of TCM.

Objective To establish an epirubicin (EPI)-resistant murine triple-negative breast cancer (TNBC) (4T1/EPI) cell line and evaluate its biological characteristics and drug resistance. Methods The EPI-resistant cell line 4T1/EPI was developed through intermittent induction with gradually increasing EPI concentrations in vitro. Morphological changes were observed under an inverted microscope. Drug resistance index (MTT assay), cell doubling time (CCK-8 assay), and migration ability (wound healing assay) were evaluated. Western blot was used to detect the expression of drug resistance-related proteins. Transcriptome sequencing and KEGG pathway enrichment analysis were performed to identify the pathways and targets involved in EPI resistance, followed by experimental validation. Results The 4T1 cells eventually grew normally in a medium containing 100 ng/mL EPI, confirming the establishment of the 4T1/EPI resistant cell line. After stable resistance was acquired, morphological alterations were observed. Compared with their parental 4T1 cells, 4T1/EPI cells showed significantly prolonged doubling time (P<0.01) and enhanced migration ability (P<0.05). Expression levels of drug resistance-related proteins MDR1, MRP1 (P<0.01), and ABCG2 (P<0.05) were elevated in 4T1/EPI cells. In vivo models also demonstrated significant EPI resistance in 4T1/EPI tumors in terms of tumor weight and volume. Transcriptome sequencing highlighted the involvement of the PI3K/Akt signaling pathway and ABC transporter pathway. Validation experiments showed the upregulation of Erbb3, Egfr, PI3K, and Akt (P<0.05) and significant downregulation of Fgfr1 (P<0.01) in 4T1/EPI cells. Conclusion The EPI-resistant TNBC cell line 4T1/EPI was successfully established, exhibiting significant resistance in vitro and in vivo. The mechanism may involve the EPI-induced upregulation of Egfr and Erbb3, activating the PI3K/Akt pathway and subsequently enhancing ABC transporter expression.

This paper presents an automatic acquisition and analytic procedure of acupuncture manipulation based on optical navigation, aiming at solving the shortcomings of existing acquisition methods of acupuncture manipulation. An acquisition holder installed at the handle tail of filiform needle was designed to display the movement trajectory of the needle during acupuncture delivery by collecting the movement trajectory of holder. The 3-month old male Bama miniature pig was selected as the experimental subject, and 6 points, "Bojian" "Qiangfeng" "Housanli" "Xiaokua" "Huiyang" (BL35) and "Baihui" (GV20), were selected during acupuncture manipulation. The optical navigation system was used to collect the real-time data, and these data were per-processed and analyzed using mean filtering and Fourier transform. The acupuncture procedure was divided into 3 stages, inserting, lifting-thrusting, and twisting. The results showed that the accuracy was 96.3% at lifting-thrusting stage, and that was 100.0% at twisting stage. The decomposition effect of the entire procedure was satisfactory. This study provides a new approach to the quantitative analysis of acupuncture manipulation. In the future, it needs to further optimize the algorithm and expand the sample size so as to improve the accuracy of this analytic technique.
Acupuncture Therapy/methods* ; Male ; Animals ; Swine ; Acupuncture Points ; Humans ; Swine, Miniature ; Needles

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

To understand the current epidemiological status and biological characteristics of avian paramyxoviruses(APMV)in wild birds in China,a total of 1 384 fecal samples of wild birds were collected in eight provinces(autonomous regions),including Ningxia,in 2023,to detect avian pa-ramyxovirus infections by viral isolation and RT-PCR.Positive samples were subjected to F gene sequence amplification and genetic evolutionary analyses.The results showed that 10 strains of APMV were isolated and identified from 1 384 wild bird feces samples with a positive rate of 0.72％.Out of the 10 strains,4 strains were APMV-1,which was in the same branch to the Ameri-can goose APMV-1 strain and had the homology ranging from 93％to 97.3％.Three strains of APMV-4 were in the same branch with the Russian duck APMV-4 strain and the Russian pintail APMV-4 strain,with homology ranging from 99.1％to 99.5％.Three strains were APMV-6,they were in the same branch with the Russian ruddy bladdered duck APMV-6 strain,with homology ranging from 98.7％to 99.20％.The intracerebral inoculatable pathogenicity index(ICPI)of the four strains for 1-day-old chicks ranged from 0 to 0.48,which was low in pathogenicity for chick-ens.The above results enriches the epidemiological information and the biological characteristics of avian paramyxovirus in wild birds in China,which provides a reference for the early warning,scien-tific prevention and control of this disease.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Malignant hyperthermia(MH)is a rare perioperative disease with autosomal dominant inheritance,and its pathogenesis involves specific gene mutations.Its clinical feature is that conventional anesthetics can trigger abnormally high metabolic reactions in skeletal muscles.Although the incidence of this disease is low,the condition is dangerous,progresses rapidly,and has a high mortality rate;Its treatment relies on early diagnosis,timely application of the specific drug Dantrolene Sodium,and rapid and orderly comprehensive symptomatic supportive treatment.MH is a critical perioperative emergency that can occur during surgery.It presents with symptoms such as hyperpyrexia,metabolic acidosis,rhabdomyolysis,and dysfunction of multiple organ systems.If not treated promptly,it can quickly lead to life-threatening arrhythmias and cardiac arrest.This condition serves as an essential teaching example in anesthesia crisis resource management.As an effective teaching method,scenario simulation exercises can comprehensively enhance medical staff's personal technical,non-technical,and teamwork abilities through simulating emergency scenarios,teaching assessments,and retrospective discussions,especially suitable for comprehensive management training of fatal diseases.Many countries internationally have incorporated simulation exercises for MH into their routine teaching and training systems.The effectiveness of teaching and training for anesthesiologists in MH and their ability to handle anesthesia crisis events have been continuously improved through a periodic training model.This article systematically reviews the research progress and practical experience of scenario simulation exercises in emergency training for MH,with a focus on exploring how to establish a scenario simulation exercise plan for emergency application and comprehensive symptomatic support treatment of Dantrolene Sodium based on the actual situation in China,providing reference for improving the teaching and training quality of MH and other clinical crisis events.

Objective:To summarize the clinical features of Chlamydia pneumoniae pneumonia (CPP) in children. Methods:Case series study. Clinical data of 10 children with CPP hospitalized in Department No.2 of Respiratory Medicine of Beijing Children′s Hospital, Capital Medical University from January 2019 to August 2024 were retrospectively collected, including general information, clinical manifestations, chest imaging, laboratory examination and treatment. The clinical features and prognosis were summarized.Results:Among the 10 children with CPP, 7 were male and 3 were female. The age of onset was 11.2 (10.3, 13.1) years. The course were 17 (7, 23) days. Cough occurred in 9 cases with wet cough in 7 cases, while moderate and high fever occurred in 6 cases. Besides, chest pain occurred in 4 cases, rash and hemoptysis occurred in 1 case respectively. High density mass shadow was found in 7 cases chest CT imaging, accompanied by air bronchogram sign, surrounded by halo sign, 6 cases of which were distributed under the pleura, while patchy consolidation in the remaining 3 cases. Pulmonary embolism was present in 2 cases. Among the 10 children with CPP, bilateral lung involvement was found in 3 cases and unilateral lung involvement in 7 cases. The white blood cell count was 10.21 (7.45, 11.64)×10 9/L and the proportion of neutrophils was 0.69 (0.63, 0.71). C-reactive protein increased in 7 cases, with the level of 33 (16, 77) mg/L. D-dimer increased slightly in 3 cases (0.393, 0.396, 0.739 mg/L). Serum Chlamydia pneumoniae-IgM antibody test was positive in 6 cases. Chlamydia pneumoniae nucleic acid test by bronchoalveolar lavage fluid (BALF) next-generation sequencing was positive in 6 cases. Both serum IgM antibody and BALF nucleic acid tests were positive in 2 cases. Among the 10 children with CPP, azithromycin alone was used in 5 cases, while glucocorticoid was added in 1 case. Due to poor response to azithromycin in 4 cases, doxycycline was replaced in 3 cases and minocycline was replaced in 1 case, while glucocorticoid was added in 2 cases. Moxifloxacin combined with glucocorticoid therapy was adopted in 1 case with long course after the poor response to azithromycin and doxycycline. All patients were cured finally. Conclusions:CPP mostly occurs in elderly children. The main clinical manifestations include cough, fever and chest pain. The common chest imaging feature is subpleural high-density mass shadow with halo sign. Pulmonary embolism is present in a few cases. Nucleic acid detection and (or) serology is helpful for etiological diagnosis. Some cases need glucocorticoid therapy.