OBJECTIVE To investigate the effects and mechanism of Yigan huayu formula in alleviating liver fibrosis in mice. METHODS Mice were randomly divided into blank group （normal saline）， model group （normal saline）， Yigan huayu formula low- and high-dose groups （28.98， 57.96 g/kg， calculated by crude drug）， with 8 mice in each group. Except for the blank group， the liver fibrosis model was induced by intraperitoneal injection of 15%CCl 4 -olive oil solution. From the third week， the mice received the medicine/normal saline intragastrically， once a day， for 4 consecutive weeks. After the last medication， liver indexes were calculated， the activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） in serum， as well as the hydroxyproline （HYP） content in liver tissue， were measured. Liver histopathology was evaluated. Differentially expressed proteins （DEPs） in liver tissue were analyzed based on proteomics， followed by bioinfo rmatics analysis. The expressions of core DEPs were validated using Western blot （WB） and immunohistochemistry （IHC） methods. RESULTS Compared with the blank group， the model group showed significantly elevated liver indexes， serum activities of ALT and AST， and hepatic HYP content （ P ＜0.05）， along with obvious pathological damage and collagen deposition. Compared with the model group， the above indexes of mice in the Yigan huayu formula high-dose group were decreased significantly （ P ＜0.05）， with marked improvement in liver pathological damage and collagen deposition. Proteomics identified 210 DEPs between the model group and Yigan huayu formula high-dose group. DEPs were significantly enriched in extracellular matrix （ECM）-receptor interaction and lipid metabolism pathways. WB and IHC confirmed that Yigan huayu formula could significantly inhibit the abnormally elevated expressions of collagen type Ⅳ alpha1 chain （COL4A1）， secreted protein acidic and rich in cysteine （SPARC）， vitronectin （VTN） and laminin subunit alpha5 （LAMA5） in liver tissue of mice （ P ＜0.05）. CONCLUSIONS Yigan huayu formula may exert anti-hepatic fibrosis effects by inhibiting the expressions of proteins such as COL4A1， LAMA5， SPARC， and VTN， thereby blocking the ECM-receptor interaction signaling pathway， and subsequently suppressing excessive ECM deposition and basement membrane remodeling.

OBJECTIVE To predict and validate the mechanisms of Chaiqi yigan granules （CQYG） improving hepatocellular carcinoma （HCC）. METHODS The signaling pathways of CQYG intervention in HCC were predicted using network pharmacology. A mice model of transplanted hepatocellular carcinoma was established by injecting H22 hepatoma cells into the axilla. Successfully modeled mice were randomly divided into model group （normal saline）， sorafenib group （positive control， 50 mg/kg）， and CQYG low-， medium- and high-dose groups （24.83， 49.66， 99.32 g/kg）， with 10 mice in each group. Mice in each group were administered the corresponding drug solution or normal saline intragastrically， once a day， for 14 consecutive days. After last administration， pathological morphological changes in the tumor tissues of mice were observed in each group. Immunohistochemical staining was performed to detect the expression of the nuclear proliferation antigen Ki-67 in tumor tissues of mice. Western blot assay was used to measure the expression of proteins related to epithelial-mesenchymal transition （EMT） [N-cadherin， E-cadherin， Vimentin， matrix metalloproteinase 7 （MMP7）] and the mitogen-activated protein kinase （MAPK） signaling pathway [p38 MAPK， phosphorylated p38 MAPK， c-Jun N-terminal kinase （JNK）， phosphorylated JNK， extracellular regulated protein kinase 1/2 （ERK1/2）， phosphorylated ERK1/2] in tumor tissue of mice. RESULTS Network pharmacology analysis revealed that metabolic pathways， pathways in cancer， and the MAPK signaling pathway were key signaling pathways through which CQYG exert their anti-hepatocellular carcinoma effects. In animal experiments， the tumor tissues of mice in the model group exhibited dense tumor cells and vigorous growth. Compared with model group， CQYG high-dose group showed a decreased density of tumor cells in the tumor tissues of mice. Moreover， the expression levels of Ki-67， N-cadherin， MMP7 and Vimentin proteins， along with the phosphorylation levels of ERK1/2 and JNK proteins， were all significantly reduced （ P ＜0.05）. The expression level of E-cadherin protein was significantly increased （ P ＜0.05）， the phosphorylation level of p38 MAPK protein was increased， the difference was not statistically significant （ P ＞0.05）. CONCLUSIONS CQYG can inhibit EMT by regulating the MAPK signaling pathway， thereby suppressing tumor cell invasion and metastasis and ultimately exerting a therapeutic effect in improving HCC.

Pancreatic cancer is a highly malignant solid tumor of the digestive system with extremely poor treatment prognosis. Although its incidence rate is low， its mortality rate is extremely high. In recent years， the number of diagnosed cases worldwide has continued to rise， making pancreatic cancer the sixth leading cause of cancer-related deaths globally. Currently， clinical treatment primarily relies on operation and chemotherapy to suppress tumors. However， these approaches face challenges such as suboptimal efficacy， high postoperative recurrence rates， and severe adverse reactions. Therefore， identifying safe and effective treatment modalities remains a pressing challenge for the medical community. In recent years， research on traditional Chinese medicine （TCM） interventions for pancreatic cancer has increased significantly. Multiple studies have shown that single-herb TCM， TCM formulas， and their derived single compounds can regulate the levels of tumor cell signaling pathways through multiple action targets. They inhibit the development and progression of pancreatic cancer by inhibiting cancer cell proliferation， promoting cell apoptosis， inhibiting tumor angiogenesis， reducing cancer cell invasion and migration capabilities， regulating the cell cycle， and modulating the tumor microenvironment. Additionally， TCM has the advantages of significantly enhancing the anticancer efficacy of chemotherapy drugs and causing fewer adverse reactions. However， the specific action mechanisms by which TCM intervenes in pancreatic cancer remain unclear. Further extensive research is still needed to validate the role of regulating classical signaling pathways such as phosphoinositide 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， Wnt/β-catenin， nuclear transcription factor-κB （NF-κB）， notch， and hedgehog in the treatment of pancreatic cancer. Therefore， this paper reviewed Chinese and international studies on TCM intervention in pancreatic cancer through relevant signaling pathways in recent years， summarized the potential action mechanisms of TCM in the treatment of pancreatic cancer， and provided references for related research in the future.

AIM:To assess the longitudinal changes in ocular biometric parameters after intravitreal ranibizumab(IVR)injection for the treatment of retinopathy of prematurity(ROP)in premature infants during the first year of life.METHODS:A longitudinal observational and comparative study was conducted on premature infants diagnosed with type 1 ROP, with a one-year follow-up. In treatment group, patients were administered with ranibizumab injection. The control group was set up without any intervention. All the patients were examined by A-scan ultrasound biometry at 0(40 gestational wk), 3, 6, and 12 mo of corrected gestational age(GA). Anterior chamber depth(ACD), lens thickness(LT), vitreous chamber depth(VCD), and axial length(AL)were measured. Anterior segment(AST)was calculated by summing up the ACD and LT.RESULTS:A total of 60 premature infants(60 eyes)were enrolled in this study, with a mean postmenstrual age of 36.80±2.86 wk, including 30 cases(30 eyes)in the treatment group and 30 cases(30 eyes)in the control group. The mean birth weight was 1157.50±232.12 and 1327.00±432.92 g, and the mean GA was 28.46±1.70 and 30.25±2.12 wk in treatment and control groups, respectively. No significant differences were found in all parameters at baseline(P>0.05). However, after correcting GA by 3, 6, and 12 mo, ACD was significantly greater than that of the control group at the 3rd, 6th, and 12th mo. LT was significantly thinner in the treatment group at the 3rd and 6thmo, but no statistically significant difference was observed between the two groups by the 12th mo. Throughout the follow-up period, AST, VCD, and AL were no significant differences in two groups(all P>0.05).CONCLUSION:Ranibizumab administered for the treatment of ROP only affected on ACD and LT. No significant effects are observed on AST, VCD, and AL during the first year of life.

Objective:To investigate the indications for prenatal diagnosis using copy number variation-sequencing (CNV-seq) and the abnormalities detected by the method.Methods:This retrospective analysis involved 17 994 singleton pregnant women who underwent prenatal CNV-seq at the First Affiliated Hospital of Zhengzhou University from January 2019 to December 2022. These cases were divided into five groups based on the following indications for CNV-seq: abnormal fetal ultrasound findings, high-risk results indicated by non-invasive prenatal testing (NIPT) or Down's syndrome serological screening (Down's screening), adverse pregnancy history, and advanced maternal age. The proportions of cases with the indications for prenatal CNV-seq, the detection rates of abnormalities (numerical abnormalities of chromosomes, pathogenic/likely pathogenic CNV in structural abnormalities) in the five groups, and the distribution of these abnormalities were analyzed. Statistical analysis was performed using Chi-square test. Results:Among the 17 994 pregnant women, the women with abnormal fetal ultrasound findings, high-risk NIPT results, high-risk Down's screening results, adverse pregnancy history, and advanced maternal age accounted for 32.65% (5 875/17 994), 11.90% (2 142/17 994), 31.62% (5 690/17 994), 11.70% (2 105/17 994), and 12.13% (2 182/17 994), respectively. The detection rates of abnormalities in the five groups were 10.60% (623/5 875), 34.64% (742/2 142), 4.69% (267/5 690), 2.99% (63/2 105), and 3.67% (80/2 182), respectively. The overall detection rate of abnormalities was 9.86% (1 775/17 994). The cases with numerical abnormalities of chromosomes accounted for 68.79% (1 221/1 775), trisomy 21 was predominant (49.30%, 602/1 221). Chromosomal structural abnormalities were detected in 31.21% (554/1 775) of the cases with abnormalities, with 57.76% (320/554) harboring pathogenic CNVs and 42.24% (234/554) harboring likely pathogenic CNVs. The detection rate of chromosomal numerical abnormalities was higher than that of structural abnormalities in the abnormal fetal ultrasound group, NIPT high-risk group, and advanced maternal age group [6.81% (400/5 875) vs. 3.80% (223/5 875), χ2=53.10; 27.96% (599/2 142) vs. 6.68% (143/2 142), χ2=338.40; 2.43% (53/2 182) vs. 1.24% (27/2 182), χ2=8.61; all P<0.01]. A total of 416 microdeletions and 255 microduplications were detected in the 554 cases. The top three regions with the highest frequencies in microdeletions were Xp22.31 (12.74%, 53/416), 22q11.21 (7.93%, 33/416), and 17q12 (5.77%, 24/416); in microduplications, they were 22q11.21 (14.90%, 38/255), 17q12 (3.53%, 9/255), and 7q11.23 (3.53%, 9/255). Conclusions:Abnormal fetal ultrasound findings accounted for the highest proportion of prenatal diagnostic indications. The overall detection rate of abnormalities by CNV-seq is relatively high, especially in those with high-risk NIPT results as an indication for prenatal diagnosis. Among the chromosomal structural abnormalities detected in this study, the frequencies of Xp22.31 microdeletion and 22q11.21 microduplication are higher.

This study summarizes the preoperative and intraoperative nursing experience in 5 cases of bridge-to-transplant heart transplantation with left ventricular assist device(LVAD)explant.Key points of nursing include:preoperative care and assessment of LVAD patients,preoperative discussion of the multidisciplinary team,safe transfer of patients to surgical rooms and other preoperative preparation,cardiomyocardial protection and multidisciplinary team cooperation during bridging transplantation,and intra-operative patient safety management.All 5 patients in this group successfully completed the surgery and were discharged.Pressure sores,wound infections,and other postoperative complications have not occurred.Postoperative cardiac function of 5 patients in this group were classified as New York Heart Association class Ⅰ～Ⅱ.The follow-up period for the 5 patients in this group ranged from 6 months to 6 years.The results of the most recent echocardiography follow-up showed that the left ventricular ejection fraction of all patients was all above 65％,with well prognosis.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Integrated traditional Chinese and Western medicine refers to a clinical diagnosis and treatment model that incorporates the essence of traditional Chinese medicine in regard to treatment decisions based on syndrome differentiation and a reliance on modern western medicine as the basis of diagnosis and treatment.It is an organic combination of the two,with each bringing its own strengths into full play and complementing those of the other.After the founding of the People's Republic of China,the development of the integrated traditional Chinese and Western medicine has been increasingly valued.The unique advantages of the integration of traditional Chinese and Western medicine have been continuously utilized,resulting in fruitful achievements in theoretical inheritance and innovation and in the prevention and treatment of major diseases.Furthermore,integrated traditional Chinese and Western medicine has become a unique system of medicine of China.This article provides a review and elaboration of the entire process of the integration of traditional Chinese and Western medicine,including its significance,status of development,and achievements.It is suggested that,in the field of integrated traditional Chinese and Western medicine,efforts should made to delve into classic theories,identify the right entry points for integration,reconstruct and improve the theoretical system of integrated traditional Chinese and Western medicine by combining the cutting-edge achievements of modern medicine,and focus on enhancing clinical efficacy.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.