Chronic obstructive pulmonary disease （COPD）， as one of the three major causes of death， is a complex systemic disease with high prevalence， high mortality， high disability， frequent acute exacerbations， and a variety of pulmonary complications. The pathogenesis is complex. Western medicine has no effective specificity scheme for a complete cure. However， multiple-component and multiple-target characteristics of traditional Chinese medicine （TCM） demonstrate significant advantages in COPD treatment through multi-link， multi-pathway， and multi-mechanism intervention. Therefore， exploring the essence of COPD pathogenesis and discovering effective TCM treatment drugs through the application of TCM principles and prescriptions is a key focus of modern research. Animal models are of paramount importance in medical research. It is the first consideration to select appropriate animals， adopt reasonable modeling methods to replicate stable animal models that closely resemble the clinical manifestations and pathophysiological characteristics of COPD， and use appropriate evaluation methods to determine the success of COPD animal models in experimental research. The core of experimental research lies in observing the intervention effect of TCM on COPD animal models， exploring the specific pathways and regulatory mechanisms of TCM on COPD disease， and finding TCM monomers， single herbs， and TCM formulas with definite curative effects. At present， animal model research on COPD mainly involves model establishment， model evaluation， efficacy observation， mechanism exploration， and other aspects. In recent years， there has been no systematic organization， update， and reflection on the relevant research on TCM intervention in COPD animal models. This study reviewed the selection of animals for the COPD model， methods for establishing COPD animal models， model evaluation methods， and the intervention effects of TCM on COPD animal models. It aims to grasp the current research status and identify existing problems for further improvement， in order to provide evidence and support for scientific research and clinical treatment of COPD.

Chronic obstructive pulmonary disease （COPD）， as one of the three major causes of death， is a complex systemic disease with high prevalence， high mortality， high disability， frequent acute exacerbations， and a variety of pulmonary complications. The pathogenesis is complex. Western medicine has no effective specificity scheme for a complete cure. However， multiple-component and multiple-target characteristics of traditional Chinese medicine （TCM） demonstrate significant advantages in COPD treatment through multi-link， multi-pathway， and multi-mechanism intervention. Therefore， exploring the essence of COPD pathogenesis and discovering effective TCM treatment drugs through the application of TCM principles and prescriptions is a key focus of modern research. Animal models are of paramount importance in medical research. It is the first consideration to select appropriate animals， adopt reasonable modeling methods to replicate stable animal models that closely resemble the clinical manifestations and pathophysiological characteristics of COPD， and use appropriate evaluation methods to determine the success of COPD animal models in experimental research. The core of experimental research lies in observing the intervention effect of TCM on COPD animal models， exploring the specific pathways and regulatory mechanisms of TCM on COPD disease， and finding TCM monomers， single herbs， and TCM formulas with definite curative effects. At present， animal model research on COPD mainly involves model establishment， model evaluation， efficacy observation， mechanism exploration， and other aspects. In recent years， there has been no systematic organization， update， and reflection on the relevant research on TCM intervention in COPD animal models. This study reviewed the selection of animals for the COPD model， methods for establishing COPD animal models， model evaluation methods， and the intervention effects of TCM on COPD animal models. It aims to grasp the current research status and identify existing problems for further improvement， in order to provide evidence and support for scientific research and clinical treatment of COPD.

ObjectiveTo explore the mechanism of Shenmai injection in improving cisplatin resistance in non-small cell lung cancer （NSCLC） based on the endoplasmic reticulum stress through protein kinase R-like endoplasmic reticulum kinase （PERK）/activated transcription factor 4 （ATF4）/C/EBP homologous protein （CHOP） signaling pathway. MethodsBALB/c nude mice bearing cisplatin-resistant human lung cancer cell line （A549/cisplatin） were randomly divided into four groups： Blank control group （0.9% sodium chloride）， cisplatin group （5 µg·g^-1cisplatin）， Shenmai injection group （5.2 mg·g^-1 Shenmai injection）， and combination therapy group （5.2 mg·g^-1 Shenmai injection +5 µg·g^-1cisplatin）. The drug intervention lasted for 4 weeks， and the changes in body weight and tumor volume were monitored. Hematoxylin-eosin （HE） staining was performed to observe tumor tissue pathology. Transmission electron microscopy （TEM） was used to assess the morphology of the endoplasmic reticulum. Immunohistochemical assay was conducted to measure the positive expressions of PERK， ATF4， and CHOP in tumor tissues. Western blot quantified the protein expression of immunoglobulin heavy chain binding protein （BIP）， PERK， phosphorylated PERK （p-PERK）， eukaryotic translation initiation factor 2α （eIF2α）， phosphorylated eIF2α （p-eIF2α）， ATF4， CHOP， B-cell lymphoma -2 （Bcl-2）， and Bcl-2 Associated X protein （Bax）. A549/cis cells were divided into blank group： Blank control group （normal culture medium）， cisplatin group （23.3 µmol·L^-1 cisplatin）， Shenmai Injection group （20 g·L^-1 Shenmai injection）， and combination therapy group （20 g·L^-1 Shenmai injection+23.3 µmol·L^-1 cisplatin）. Cell counting kit-8 （CCK-8） method was used to detect cell viability， TEM was used to observe the morphology of endoplasmic reticulum， and Western blot was used to detect endoplasmic reticulum stress and apoptosis-related proteins. ResultsCompared with the cisplatin group， the combination therapy group showed increased body weight （P<0.05）， decreased tumor volume （P<0.05）， and expanded endoplasmic reticulum in tumor cells. The positive expressions of PERK， ATF4， and CHOP increased （P<0.05）. Western blot revealed elevated protein expression levels of BIP， p-PERK/PERK， p-eIF2α/eIF2α， ATF4， CHOP， and Bax （P<0.05）， while Bcl-2 expression decreased （P<0.05）. As shown in the in vitro experiment， compared with the cisplatin group， the combination therapy group exhibited a reduced cell survival rate （P<0.05）. TEM revealed increased endoplasmic reticulum dilation and vesicular degeneration. Western blotting showed increased protein levels of BIP， p-PERK/PERK， p-eIF2α/eIF2α， ATF4， CHOP and Bax （P<0.05）， with decreased Bcl-2 expression （P<0.05）. ConclusionShenmai injection combined with cisplatin has a synergistic antitumor effect in NSCLC， which may be attributed to the activation of endoplasmic reticulum stress response mediated by the PERK/eIF2α/ATF4/CHOP signaling pathway and the induction of tumor cell apoptosis.

Objective To improve the analysis method of the blood components of Yinchenhao decoction （YCHD） in vivo and explore its anti-hepatocellular carcinoma mechanism. Methods Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry （UPLC-Q-TOF/MS） was used to collect and analyze blood samples from mice. The mice were given a single dose of YCHD with a concentration of 0.1 g/ml and a dose of 25 ml/kg, and then the samples were collected 2 h post–administration, which was to systematically study the chemical components of YCHD in vivo. Network pharmacological methods were used to screen the components and targets of YCHD, and the targets of hepatocellular carcinoma; The common targets of YCHD and hepatocellular carcinoma were identified for GO enrichment and KEGG enrichment. Molecular docking was performed on the main targets to verify the binding ability between the active ingredients and the core targets. The relative mRNA expression levels of serine/threonine-protein kinase（AKT1） and tumor protein p53（TP53） in liver tissues were analyzed via qPCR, including the following mouse groups: mice with concanavalin A（Con-A）-induced acute liver injury without preventive administration, mice with Con-A-induced acute liver injury that received 14 d preventive oral administration of YCHD, and untreated control mice. Results ①The active ingredients of YCHD in the blood were identified by retrieving the data from the in vitro component analysis. They were chrysophanol, herniarin, aloe-emodin, and monotropein. ②The mechanism of action of the blood components against hepatocellular carcinoma （HCC） was further analyzed using network pharmacological methods, and a total of 30 components of YCHD were screened for 213 targets and 215 HCC targets. ③There were 17 intersection targets between YCHD and hepatocellular carcinoma, including AKT1, TP53, receptor tyrosine-protein kinase erbB-2 （ERBB2）, myelocytomatosis oncogene （MYC）, interleukin-1β （IL-1β）, etc. The GO enrichment results indicated that these components were primarily involved in DNA replication，chromosome segregation，leukocyte mediated immunity，leukocyte cell-cell adhesion. The KEGG enrichment results demonstrated that these components were predominantly associated with diverse cancer pathways. Additionally, the results indicated involvement in the citrate cycle （TCA cycle）, pyruvate metabolism, and p53 signaling pathway, ect. ④The results of molecular docking showed that chrysophanol, herniarin, and aloe - emodin had strong binding abilities with AKT1, TP53, ERBB2, MYC, and IL-1β. ⑤The relative expression of AKT1 and TP53 mRNA was significantly higher in the modelling group than in the control group. The relative expression of AKT1 and TP53 mRNA was significantly lower in the drug administration group than in the modelling group. Conclusion There were 4 blood components in YCHD, among which chrysophanol, herniarin, and aloe-emodin may act on AKT1, TP53, ERBB2, MYC, IL-1β and then participated in the regulation of cancer signaling pathways and p53 signaling pathway to play a role in the treatment of HCC.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

ObjectiveThis paper aims to systematically collect and organize ancient and modern clauses and studies containing Xieqingwan， excavate and analyze the key information of Xieqingwan， and provide a reference for facilitating the development of the classic formula Xieqingwan. MethodsThe composition， dosage， decocting methods， usage， and other key information of Xieqingwan in ancient traditional Chinese medicine books were collected and analyzed by means of literature research and metrological methods. The modern clinical application of Xieqingwan was summarized. ResultsA total of 42 pieces of effective data involving 32 ancient traditional Chinese medicine books were collected. Xieqingwan was first recorded in Xiaoer Yaozheng Zhijue. The drug origin of this formula is basically clear in the ancient traditional Chinese medicine books. The modern drug usage and decocting method were as follows： Angelicae Sinensis Radix， Gentianae Radix et Rhizoma， Chuanxiong Rhizoma， Gardenia seeds， Radix et Rhizoma Rhei， Notopterygii Rhizoma et Radix， and Saposhnikoviae Radix were grounded to fine powder， decocted with honey， and finally formed into pills with the size of a chicken head （1.5 g）. It was suggested that half a pill or one pill were taken for one dose with warm Lophatheri decoction and sugar. The indications and clinical application had developed from the recordings in Xiaoer Yaozheng Zhijue and evolved from pediatrics to ophthalmic otolaryngology， neurology， dermatology， digestion， and respiratory diseases. The main pathogenesis of these diseases is heat in the liver meridian and is treated. The effect of Xieqingwan is "clearing away heat and toxicity， removing fire and relaxing the bowels， and dispersing swelling and relieving pain". It is recommended to use the corresponding preparation methods in the 2020 Edition of Pharmacopoeia of the People's Republic of China. Modern clinical studies are centered around the clinical application of Xieqingwan， which is often modified and used in treating Tourette syndrome， herpes， febrile convulsion， sleepwalking， and insomnia. ConclusionThis paper conducts a thorough textual research of the key information of Xieqingwan， induces its historic evolution， and confirms its key information， so as to provide a reference for the future development of Xieqingwan.

OBJECTIVE To compare the predictive accuracy and clinical applicability of four vancomycin individualized dosing tools （SmartDose， ClinCalc， Gulou， Pharmado） and provide a basis for rational clinical medication use. METHODS A retrospective cohort study was conducted， enrolling 479 adult patients who received vancomycin therapy and underwent steady-state trough concentration monitoring in Zhongshan Hospital， Fudan University （Xiamen Branch） from January 1， 2022， to June 30， 2024. The predictive accuracy of each tool was evaluated using indicators， such as mean error （ME）, mean absolute error （MAE）， mean percentage error （MPE）， mean absolute percentage error （MAPE）， the proportion of patients with an absolute percentage error （APE） of less than 30%， the 95% limits of agreement， and the overall relative percentage difference between predicted and measured values. Using indicators such as accessibility， patient management， and recommendation of multiple treatment options， the clinical panxso@163.com applicability of the tools for all patients was evaluated； using the discrepancy in accuracy between the predicted and actual measured blood drug concentrations as an indicator， the clinical applicability was assessed for patients in different renal function subgroups （hyperfunction， normal， mild impairment， moderate impairment， and severe impairment）. RESULTS In terms of accuracy， SmartDose demonstrated the best overall performance with an MAPE of 46.40% and a proportion of APE ＜30% （46.56%）. Bland-Altman analysis indicated that SmartDose had the smallest overall relative percentage difference （－7.25%）， although the 95% limits of agreement were broad for all tools， with differences between the upper and lower limits exceeding 200%. In terms of applicability， all four dosing tools were freely accessible and demonstrated good availability； SmartDose and Pharmado provided the most comprehensive solutions， offering features such as patient management， multiple regimen recommendations， and drug concentration-time curve plotting. Stratified analysis based on renal function revealed that Pharmado showed optimal prediction for hyperfiltration patients （mean difference： 0.11 mg/L）. SmartDose and ClinCalc showed relatively better performance in normal and mild renal impaiment （mean difference： 0.37， 0.51 mg/L and －1.13， －1.33 mg/L，respectively）. SmartDose performed best in moderate renal impairment （mean difference： －2.60 mg/L）. Pharmado and Gulou had smaller prediction biases in severe renal impairment （mean differences： 1.52 mg/L and －0.23 mg/L， respectively）. CONCLUSIONS The four individualized dosing tools demonstrated limited accuracy in the initial prediction of vancomycin concentrations. Among them， SmartDose demonstrates the highest overall prediction accuracy and possesses comprehensive clinical management features. It is recommended that Pharmado be preferred for patients with renal hyperfiltration； SmartDose or ClinCalc can be used for patients with normal or mildly impaired renal function； SmartDose is recommended for patients with moderately impaired renal function； Pharmado or Gulou may be considered for patients with severely impaired renal function.