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 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.

Objective:To design a management system of internet of things(IoT)smart operation room based on business process reconstruction theory,and explore the application effect of that in improving management effectiveness for operation room.Methods:Aimed at the existing problems of key points in the management for operation room,the process was reconstructed on the basis of the original information system,relevant systems,workflows and standards of operation room of Fuwai Hospital of Medical Sciences and Peking Union Medical College.Then,a management system of IoT smart operation room based on business process reconstruction theory was designed.The surgical data of 13159 patients who underwent relevant operation of cardiovascular surgery from January to December 2022 were selected,and they were divided into"before-application"group(6 483 cases)and"after-application"group(6 676 cases)according to the point before and after the system was applied.Some indicators'data,which included scheduling management situation of picking up and dropping off patients by medical auxiliaries,duration of preparing anesthesia,duration of preoperative waiting,interval duration of continuous surgery and usage amount of surgical gowns under same amount of surgery,between two groups were compared.Results:The transportation efficiency of patients who underwent surgery after system was applied was(1.38±0.09)surgeries/h,which was higher than(0.99±0.09)surgeries/h before it was applied,and the difference was statistically significant(t=6.604,P<0.001).The satisfaction score of medical auxiliaries increased from 3.83(3.33,4.5)before application to 4.50(4.33,4.83)after application,and the difference was significant(Z=2.02,P<0.05).The duration of preparing anesthesia and duration of preoperative waiting after the system was applied were respectively(62.04±2.29)and(8.09±2.46)min,both of which were less than those before the system was applied,and the differences of them between two groups were statistically significant(t=2.309,2.280,P<0.05).The usage amount of surgical gowns under same amount of surgery after the system was applied was(4.11±0.57)gowns/surgery,which was less than(5.81±0.29)gowns/surgery before the system was applied,and the difference was statistically significant(t=6.489,P<0.05).Conclusion:The application of the management system of IoT smart operation room based on business process reconstruction theory can provide more safely,high-qualitatively and efficiently medical services for patients,and improve work efficiency and management effectiveness for operation room,and reduce resource consumption and operating cost.

[This corrects the article DOI: 10.1016/j.apsb.2021.07.004.].

Objective To investigate the effects of Echinococcus multilocularis infection on levels of memory T (Tm) cells and their subsets in lymph nodes of mice at different stages of infection, so as to provide new insights into immunotherapy for alveolarechinococcosis. MethodsTwenty-four C57BL/6J mice aged 6 to 9 weeks were randomly divided into the infection group and the control group, of 12 mice in each group. Mice in the infection group were administered with 3 000 E. multilocularis protoscoleces via portal venous injection, while animals in the control group were administered with an equal volume of physiological saline. Three mice from each group were sacrificed 4, 12 weeks and 24 weeks post-infection, and lymph nodes were sampled and stained with hematoxylin and eosin (HE) to investigate the histopathological changes of mouse lymph nodes in the infection group. The expression and localization of T lymphocyte surface markers CD3, CD4, and CD8 were observed in mouse lymph nodes using immunohistochemical staining. In addition, lymphocyte suspensions were prepared from mouse lymph nodes in both groups at different time points post-infection, and the levels of Tm cell subsets and their secreted cytokines were detected using flow cytometry. Results HE staining showed diffuse structural alterations in the subcapsular cortical and paracortical regions of mouse lymph nodes in the infection group 4 weeks post-infection with E. multilocularis. Immunohistochemical staining detected CD3, CD4 and CD8 expression in mouse lymph nodes in both groups. Flow cytometry revealed higher proportions of CD4⁺ Tm cells [(55.3 ± 4.8)% vs. (38.8 ± 6.1)%; t = -4.259, P < 0.05] and CD4⁺ tissue-resident Tm (Trm) cells [(57.7 ± 3.7)% vs. (34.1 ± 11.2)%; t = -3.990, P < 0.05] in mouse lymph nodes in the infection group than in the control group 4 weeks post-infection, and higher proportions of CD4⁺ Tm cells [(34.6 ± 3.2)% vs. (23.3 ± 7.5)%; t = -2.764, P < 0.05] and CD4⁺ Trm cells [(44.0 ± 1.9)% vs. (31.2 ± 1.5)%; t = -4.039, P < 0.05] in mouse lymph nodes in the infection group than in the control group 24 weeks post-infection. The proportions of CD8⁺ Tm cells were higher in the infection group than in the control group 4 weeks [(56.8 ± 2.7)% vs. (43.9 ± 5.2)%; t = -4.416, P < 0.01] and 12 weeks post-infection [(25.4 ± 2.7)% vs. (12.0 ± 2.6)%; t = -2.552, P < 0.05], while the proportions of tumor necrosis factor (TNF)-α⁺ CD4⁺ T cells [(15.7 ± 5.0)% vs. (49.4 ± 6.4)%; t = 7.150, P < 0.01], TNF-α⁺CD8⁺ T cells [(20.7 ± 5.5)% vs. (57.5 ± 8.4)%; t = -6.694, P < 0.01], and TNF-α⁺ CD8⁺ Tm cells [7.0% (1.0%) vs. 31.0% (11.0%); Z = -2.236, P < 0.05] were lower in the infection group than in the control group 24 weeks post-infection. Conclusions Tm cells levels are consistently increased in lymph nodes of mice at different stages of E. multilocularis infection, with Trm cells as the predominantly elevated subset. The impaired capacity of CD8⁺ Tm cells to secrete the effector molecule TNF-α in mouse lymph nodes at the late-stage infection may facilitate chronic parasitism of E. multilocularis.

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.