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.

OBJECTIVE To explore the intervention mechanism of Bushen anzhi decoction （BSAZD） in rats with insomnia induced by kidney failing to store spirit. METHODS A total of 36 SD rats were randomly divided into normal group （normal saline）， model group （normal saline）， eszopiclone group （positive control group， 0.27 mg/kg）， and low-， medium- and high-dose BSAZD groups （5.85， 11.70， 23.40 g/kg）， with 6 rats in each group. Except for the normal group， rats in the remaining groups were subjected to combined administration of D-galactose and DL-4-chlorophenylalanine to establish the rat model of insomnia due to kidney failing to store spirit. After successful modeling， corresponding liquid medicine or normal saline was intragastrically administered once daily for 14 consecutive days. After the last administration， behavioral indicators and hippocampal histopathological morphology of rats were detected. Untargeted metabolomics was adopted to screen serum differential metabolites and enrich relevant signaling pathways. RESULTS Compared with the model group， the high-dose BSAZD group exhibited significantly increased open arm entries ， open arm time and sleep time （ P ＜0.05）， accompanied by markedly decreased closed arm entries， closed arm time and sleep latency （ P ＜0.05）. Hippocampal neurons in the high-dose BSAZD group were arranged regularly， and the size， morphology and location of cell nuclei were nearly normal. Untargeted metabolomics analysis identified oxidized glutathione， glutamate and other differential metabolites in serum of rats in the high-dose BSAZD group； these differential metabolites were mainly enriched in GABAergic synapse， glutamatergic synapse， cAMP signaling pathway and 2-oxocarboxylic acid metabolism pathway. CONCLUSIONS BSAZD can improve sleep quality， relieve anxiety-like behaviors in rats with insomnia due to kidney failing to store spirit. Its therapeutic mechanism may be associated with regulating GABAergic and glutamatergic synaptic pathways to restore neurotransmitter balance and further alleviate oxidative stress injury， modulating the cAMP signaling pathway to facilitate neural repair， and intervening the 2-oxocarboxylic acid metabolism pathway to optimize energy supply.

OBJECTIVE To explore the intervention mechanism of Bushen anzhi decoction （BSAZD） in rats with insomnia induced by kidney failing to store spirit. METHODS A total of 36 SD rats were randomly divided into normal group （normal saline）， model group （normal saline）， eszopiclone group （positive control group， 0.27 mg/kg）， and low-， medium- and high-dose BSAZD groups （5.85， 11.70， 23.40 g/kg）， with 6 rats in each group. Except for the normal group， rats in the remaining groups were subjected to combined administration of D-galactose and DL-4-chlorophenylalanine to establish the rat model of insomnia due to kidney failing to store spirit. After successful modeling， corresponding liquid medicine or normal saline was intragastrically administered once daily for 14 consecutive days. After the last administration， behavioral indicators and hippocampal histopathological morphology of rats were detected. Untargeted metabolomics was adopted to screen serum differential metabolites and enrich relevant signaling pathways. RESULTS Compared with the model group， the high-dose BSAZD group exhibited significantly increased open arm entries ， open arm time and sleep time （ P ＜0.05）， accompanied by markedly decreased closed arm entries， closed arm time and sleep latency （ P ＜0.05）. Hippocampal neurons in the high-dose BSAZD group were arranged regularly， and the size， morphology and location of cell nuclei were nearly normal. Untargeted metabolomics analysis identified oxidized glutathione， glutamate and other differential metabolites in serum of rats in the high-dose BSAZD group； these differential metabolites were mainly enriched in GABAergic synapse， glutamatergic synapse， cAMP signaling pathway and 2-oxocarboxylic acid metabolism pathway. CONCLUSIONS BSAZD can improve sleep quality， relieve anxiety-like behaviors in rats with insomnia due to kidney failing to store spirit. Its therapeutic mechanism may be associated with regulating GABAergic and glutamatergic synaptic pathways to restore neurotransmitter balance and further alleviate oxidative stress injury， modulating the cAMP signaling pathway to facilitate neural repair， and intervening the 2-oxocarboxylic acid metabolism pathway to optimize energy supply.

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 explore the molecular mechanism by which interferon regulatory factor 1 (IRF1) affects hepatic ischemia-reperfusion injury (HIRI) by regulating the polarization of Kupffer cells.Methods:Twelve male healthy C57BL/6 wild-type mice weighing 20-25 g and aged 6-8 weeks were divided into a sham operation group ( n=6) and a HIRI group ( n=6); Twelve male healthy C57BL/6 IRF1 gene knockout (IRF1 -/-) mice weighing 20-25 g and aged 6-8 weeks were divided into a sham operation IRF1 -/- group ( n=6) and a HIRI IRF1 -/- group ( n=6). The levels of serum alanine transaminase (ALT) and aspartate transaminase (AST) in mice were measured, and hematoxylin-eosin (HE) staining of liver tissues was performed for Suzuki scoring to evaluate liver injury. Fluorescence quantitative polymerase chain reaction (qRT-PCR) was used to evaluate the mRNA levels of IRF1 and tumor necrosis factor α (TNFα) in liver tissues. Flow cytometry and qRT-PCR were used to detect the proportion and functional changes of M1/M2-type Kupffer cells in liver tissues. IRF1 was overexpressed or knocked down in the mononuclear macrophage cell line ANA1, and a co-culture and hypoxia-reoxygenation system with the hepatocyte cell line AML12 was established. Flow cytometry was used to detect the apoptosis of AML12 cells. Results:At 12 hours after hepatic ischemia-reperfusion in wild-type mice, the liver tissue injury was the most severe. Compared with the sham operation group, the levels of serum ALT [(8 073±83) U/L vs. (81±19) U/L, q=13.59] and AST [(11 170±2 890) U/L vs. (412±210) U/L, q=13.77] in the HIRI group were significantly higher, and the differences were statistically significant (both P<0.001). The Suzuki score reached 5-6 points. At 12 hours after hepatic ischemia-reperfusion in IRF1 gene knockout mice, the liver tissue injury was not obvious. There were no significant differences in the levels of serum ALT [668 (514, 2 344) U/L vs. 254 (147, 285) U/L, q=2.52, P=0.348] and AST [1 936 (1 262, 2 003) U/L vs. 628 (423, 759) U/L, q=1.22, P=0.824] between the HIRI IRF1 -/- group and the sham operation IRF1 -/- group. Compared with the HIRI group, the ratio of M1/M2-type Kupffer cells in the liver of the HIRI IRF1 -/- group decreased [(0.958±0.090) vs. (2.788±0.258), q=2.06, P<0.0001], and the mRNA expression of TNFα decreased [(4.363±0.393) vs. (12.900±5.504), q=5.59, P=0.018], and the differences between the two groups were statistically significant. In the co-culture and hypoxia-reoxygenation experiment using ANA1 cells overexpressing IRF1 and AML12 cells, the proportion of AML12 hepatocytes in late apoptosis was higher than that in the control group [(14.05±4.25) vs. (3.15±1.16), t=2.85, P=0.047], and the difference was statistically significant. In contrast, when the expression of IRF1 was knocked down, the proportion of apoptotic AML12 cells decreased [(9.26±3.04) vs. (13.36±4.64), t=2.15, P=0.098], but the difference was not statistically significant. Conclusion:The IRF1 protein can regulate the polarization of Kupffer cells into M1-type macrophages, promote the inflammatory injury of the liver tissue after ischemia-reperfusion, and increase the apoptosis of hepatocytes.

Objective To clarify the active ingredients and the potential molecular mechanism of Guben Qushi Huayu Prescription in treating psoriasis recurrence.Methods An ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF/MS)was applied to analyze the whole formula and the constituents absorbed into blood of Guben Qushi Huayu Prescription,and molecular docking technology was used to study the binding affinity of the constituents absorbed into blood with psoriasis-related immunomodulatory proteins such as CD69 and CD103 proteins.Results Mass spectrometry analysis identified 21 active ingredients such as paeoniflorin in Guben Qushi Huayu Prescription,including several known anti-inflammatory and immunomodulatory compounds.Analysis of the constituents absorbed into blood identified 11 ingredients,including paeoniflorin,that may affect the course of psoriasis through blood circulation.Molecular docking studies revealed that the constituents absorbed into blood,including astilbin,isoastilbin,chlorogenic acid,neochlorogenic acid,cryptochlorogenic acid,helicine,paeoniflorin,ononin,all had high binding affinities with CD69 and CD103 proteins.Conclusion This research reveals the main active ingredients of Guben Qushi Huayu Prescription and their potential mechanism for regulating the recurrence of psoriasis by mass spectrometry and molecular docking technology,contributing to providing scientific basis for further pharmacological research and clinical application.

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.