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.

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.

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 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.

Objective:To explore the prognosis and influencing factors of early-onset neonatal sepsis (EONS), and provide the guidance for early clinical prevention and treatment.Methods:The clinical data of 147 children with EONS in Shijiazhuang Maternal and Child Health Hospital from January 2019 to December 2023 were retrospectively analyzed. The baseline data and prognosis data (good prognosis and poor prognosis) were recorded. Multivariate Logistic regression analysis was used to analyze the risk factors of poor prognosis in children with EONS. R3.5.3 software package was used to build a nomogram model for predicting poor prognosis in children with EONS, and the rms package was used to calculate the consistency index ( C- index), and Bootstrap self-sampling was used for internal verification. Results:Among the 147 children with EONS, 41 children (27.89%) had poor prognosis, and 106 children (72.11%) had good prognosis. The incidence of thrombocytopenia, incidence of bacterial meningitis, procalcitonin and lactate in children with poor prognosis were significantly higher than those in children with good prognosis: 68.29% (28/41) vs. 17.92% (19/106), 43.9% (18/41) vs. 1.89% (2/106), (70.36 ± 13.45) ng/L vs. (42.76 ± 10.37) ng/L and (6.18 ± 2.05) mmol/L vs. (4.22 ± 1.05) mmol/L, and there were statistical differences ( P<0.01); there were no statistical difference in gender composition, gestational age, birth weight, 1 min Apgar score, delivery mode, white blood cell, C-reactive protein, creatinine and the incidences of placental abruption, amniotic fluid contamination, maternal infection, necrotizing enterocolitis between the two groups ( P>0.05). Multivariate Logistic analysis result showed that thrombocytopenia, bacterial meningitis, high procalcitonin and high lactate were the independent risk factors for poor prognosis in children with EONS ( OR = 9.595, 22.657, 1.213 and 2.614; 95% CI 1.094 to 20.055, 1.833 to 41.328, 0.745 to 0.937 and 0.990 to 1.209; P<0.05 or <0.01). A nomogram model was constructed to predict poor prognosis in children with EONS, using thrombocytopenia, bacterial meningitis, procalcitonin and lactate as predictive factors. The nomogram model predicted that the correction curve for poor prognosis in children with EONS tended towards the ideal curve ( C- index = 0.987, 95% CI 0.975 to 0.998). Conclusions:The children with EONS have a higher risk of adverse prognosis, which may be related to their comorbidities with thrombocytopenia, bacterial meningitis, high procalcitonin and high lactate at diagnosis. The nomogram model established based on the indexes has a good predictive effect on the disease prognosis.

Objective To explore the influence factors of timed up and go test(TUG)in patients with maintenance hemodialysis(MHD).Methods Cross-sectional investigation method was used to select patients who underwent regular hemodialysis in Beijing Luhe Hospital,Capital Medical University from June 1,2023 to January 31,2024,with complete data and cooperation.The patients were respectively treated with a simple mental state checklist and 5 times sit to stand test(STS-5)and TUG scale were used to assess cognitive function,lower limb muscle strength,balance and gait function.Grouping was carried out according to the results of TUG.If TUG 12s,it is judged as the TUG normal group;if TUG＞12s,it is judged as the TUG prolonged group.Multi-factor Logistic regression was used to analyze the influencing factors in the TUG prolonged group,and the predictive value of influencing factors to TUG prolonged patients was analyzed by receiver operating characteristic(ROC)curve.Results A total of 146 patients with MHD were included in this study,including 86 patients in TUG prolonged group and 60 patients in TUG normal group.Gender,age,combined hypertension,combined diabetes,blood glucose,education level,Minimum Mental State Examination(MMSE)score,C-reactive protein were compared between groups.Serum albumin,serum creatinine,serum sodium,whole parathyroid hormone,low density lipoprotein-cholesterol,serum uric acid,STS-5 results,and single urea clearance index(Kt/V)had statistical differences(P＜0.05).Multivariate Logistic regression showed that old age,female,low MMSE score,low Kt/V,and STS-5 duration were independent risk factors for TUG prolonged(P＜0.05).ROC curve showed that the area under the curve(AUC)of age,gender,total MMSE score and STS-5 time in predicting TUG prolongation in MHD patients were 0.825,0.678,0.777 and 0.836,respectively(P＜0.01),which had high predictive value.The AUC of Kt/V in predicting TUG prolongation was 0.602(P=0.037),has a certain predictive value;AUC of TUG prolongation in MHD patients predicted by the above 5 indicators as a global model was 0.923(P＜0.01).Conclusions The age,gender,MMSE score,STS-5 prolongation and Kt/V level of MHD patients are independent risk factors for TUG prolongation,and may be biological indicators for predicting TUG prolongation.

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.

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.