ObjectiveTo investigate the independent risk factors for concurrent myocardial injury before intervention in patients with primary hepatic carcinoma （PHC）， to construct and validate a nomogram prediction model for the early identification of such patients， and to provide a basis for early clinical prevention and management. MethodsA retrospective analysis was performed for the clinical data of 1 822 patients with PHC in two medical institutions from January 2019 to June 2025， among whom the 1 518 patients from The Third People’s Hospital of Kunming were used as the training set to construct the nomogram prediction model， and the 304 patients from The Second People’s Hospital of Baoshan were used as the validation set. According to the result of high-sensitivity cardiac troponin T measurement before intervention， the patients with PHC were divided into myocardial injury group and non-myocardial injury group. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis were used to identify the risk factors of concurrent myocardial injury in PHC patients before intervention， and then a nomogram prediction model was constructed based on these findings. The independent data of the 304 patients in the validation set were used for external validation， and the area under the receiver operating characteristic curve （AUC）， the Hosmer-Lemeshow test， the calibration curve， decision curve analysis， and clinical impact curve were used to assess model performance. ResultsIn the training set， there were 346 patients in the myocardial injury group and 1 172 patients in the non-myocardial injury group， and the prevalence rate of concurrent myocardial injury was 22.79%. The mean age was 56.44±9.45 years， and the male patients accounted for 81.49%. The patients with drinking， smoking， chronic hepatitis B， chronic hepatitis C， hypertension， diabetes， Child-Pugh class A liver disease， BCLC stage B liver disease， or abnormal electrocardiographic findings accounted for 49.34%， 57.44%， 61.73%， 32.48%， 22.66%， 17.19%， 50.53%， 33.86%， and 62.12%， respectively. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis showed that age （odds ratio ［OR］=1.051， 95% confidence interval ［CI］： 1.036 — 1.067， P<0.001）， history of diabetes （OR=1.862， 95%CI： 1.346 — 2.574， P<0.001）， electrocardiographic findings （OR=1.446， 95%CI： 1.077 — 1.940， P=0.014）， hemoglobin （HGB） （OR=0.993， 95%CI： 0.988 — 0.998， P=0.003）， albumin （Alb） （OR=0.951， 95%CI： 0.930 — 0.972， P<0.001）， and creatinine （Cr） （OR=1.021， 95%CI： 1.016 — 1.026， P<0.001） were independent influencing factors for myocardial injury in PHC patients. A regression equation was established as Logit（Y）=-3.406+0.050×age+0.621×history of diabetes+0.369×electrocardiogram－0.007×HGB－0.051×Alb+0.021×Cr， and a nomogram prediction model was constructed based on these factors. The Bootstrap internal validation of the model yielded an AUC of 0.743 （95%CI： 0.707 — 0.778）， and the model had an AUC of 0.766 （95%CI： 0.738 — 0.794） in the training set and 0.730 （95%CI： 0.688 — 0.793） in the validation set. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.716 for the training set and 0.408 for the validation set， suggesting that the model had a good degree of fitting. Calibration curves， decision curve analysis， and clinical impact curves in both the training set and the validation set showed that the model had a good clinical application value. ConclusionAge， history of diabetes， electrocardiographic findings， HGB， Alb， and Cr are independent influencing factors for myocardial injury in patients with PHC， and the nomogram prediction model constructed based on these factors has good discriminatory ability， accuracy， and clinical practicability.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

To analyze the epidemiological characteristics of COVID-19 in Fujian Province from the 49th week of 2022 to the 5th week of 2023,after further optimization of China's COVID-19 prevention and control measures on December 7,2022(the 49th week of 2022),this study used multi-dimensional surveillance data to dynamically assess population infection levels and their changing trends.The aim of the study was to provide a scientific basis for early warning of epidemic risk,medical resource allocation,and evalu-ation of socio-economic impact.A multi-source data surveillance system was constructed,encompassing surveillance of fever clinics at medical institutions(weekly collection of visits,positive nucleic acid and antigen test results,inpatients,and severe cases in sec-ondary or above hospitals),population nucleic acid test monitoring(weekly person-times and positivity rates of single-tube tests from the provincial system),sentinel hospital monitoring(weekly proportion of influenza-like illness visits at 18 sentinel hospitals and re-lated viral testing data),and monitoring of novel coronavirus variants(weekly systematic collection of genomic sequences of local and imported cases).Line charts were plotted weekly,and time series analysis,molecular epidemiological methods,and an improved SEIAR model were used to simulate epidemic spread.During the study period,the COVID-19 epidemic in Fujian Province exhibited three distinct stages.In the infection peak stage(52nd week of 2022),the provincial fever clinic visits reached 606 893 person-times,and a 49.2%positivity rate in population single-tube nucleic acid tests and 63.8%positivity rate in sentinel hospital monitoring were observed.In the medical load peak stage(2nd week of 2023),274 460 inpatients and 28 487 severe cases were recorded.In the epidemic decline stage(4th to 5th weeks of 2023),fever clinic visits decreased by 96.3%with respect to the peak,the single-tube nucleic acid test positivity rate decreased to 6.3%,and the sentinel hospital COVID-19 nucleic acid test positivity rate was 6.4%.All 508 sequenced local cases were Omicron variants,predominantly BA.5.2 and its sub-lineages(67.4%).Among 56 imported se-quenced cases,BA.5.2 and its sub-lineages accounted for 50.0%,and 16.1%comprised nine variants of interest including XBB and BQ.The model predicted the infection peak in the 52nd week of 2022,whereas the hospitalization peak lagged by approximately 10.6 days.Multi-source data monitoring revealed a three-stage development of the COVID-19 epidemic in Fujian.The BA.5.2 strain was dominant during the epidemic.The combination of multi-source monitoring data and modeling provides important references for epi-demic prevention and control,and highlights the need to improve the monitoring system in follow-up.

ObjectiveTo investigate the intervention effects and mechanisms of the flavonoid hyperoside （Hyp） on lipopolysaccharide （LPS）-induced inflammation in the zebrafish model. MethodsZebrafish larvae were either microinjected with 0.5 g·L^-1 LPS or immersed in 1 g·L^-1 LPS for the modeling of inflammation. The larvae were then treated with Hyp at 25， 50， and 100 mg·L^-1 through immersion for four consecutive days. The inflammatory phenotypes were assessed by analyzing the mortality rate， malformation rate， body length， and yolk sac area ratio. Behavioral tests were conducted to evaluate the inflammatory stress responses， and macrophage migration was observed by fluorescence microscopy. Additionally， the mRNA levels of inflammation-related genes， including interleukin-1β （IL-1β）， interleukin-6 （IL-6）， chemokine C-C motif ligand 2 （CCL2）， chemokine C-X3-C motif receptor 1 （CX3CR1）， chemokine C-C motif receptor 2 （CCR2）， and genes associated with the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-kappa B （NF-κB） signaling pathway， were measured by Real-time quantitative polymerase chain reaction（Real-time PCR）. ResultsCompared with the pure water injection group， the model group exhibited increased mortality， malformation rates and yolk sac area ratio （P0.01）， reduced body length （P0.01）， increased total swimming distance and high-speed swimming duration （P0.01）， and up-regulated mRNA levels of TLR4， MyD88， NF-κB， IL-1β， IL-6， CCL2， CX3CR1， and CCR2 （P0.01）. Hyp at low， medium and high doses， as well as aspirin， reduced the mortality and malformation rates （P0.05，P0.01）， increased the body length （P0.05，P0.01）， decreased the yolk sac area ratio （P0.01）， reduced the high-speed swimming duration （P0.01）， and down-regulated the mRNA levels of TLR4， MyD88， NF-κB， IL-1β， IL-6， CCL2， CX3CR1， and CCR2 （P0.05，P0.01） compared with the model group. ConclusionHyp may modulate the TLR4/MyD88/NF-κB pathway to ameliorate inflammatory phenotypes and alleviate stress conditions in zebrafish， thereby exerting the anti-inflammatory effect.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

Human infection with Orf virus is a rare zoonotic disease in clinical practice,mainly caused by human contact with infected sheep or its pollutants.It is commonly seen in shepherds and slaughterhouse workers.The le-sion mainly involves the skin.Since it is rare in clinic and difficult to diagnose and treat,it is easy to be misdiag-nosed and underdiagnosed.This paper reports a case of human infection with Orf virus,with locally dense skin le-sions.The clinical diagnosis and treatment processes of this case are analyzed,and relevant literatures are reviewed retrospectively,so as to improve clinical understanding on this disease.

Human infection with Orf virus is a rare zoonotic disease in clinical practice,mainly caused by human contact with infected sheep or its pollutants.It is commonly seen in shepherds and slaughterhouse workers.The le-sion mainly involves the skin.Since it is rare in clinic and difficult to diagnose and treat,it is easy to be misdiag-nosed and underdiagnosed.This paper reports a case of human infection with Orf virus,with locally dense skin le-sions.The clinical diagnosis and treatment processes of this case are analyzed,and relevant literatures are reviewed retrospectively,so as to improve clinical understanding on this disease.

To analyze the epidemiological characteristics of COVID-19 in Fujian Province from the 49th week of 2022 to the 5th week of 2023,after further optimization of China's COVID-19 prevention and control measures on December 7,2022(the 49th week of 2022),this study used multi-dimensional surveillance data to dynamically assess population infection levels and their changing trends.The aim of the study was to provide a scientific basis for early warning of epidemic risk,medical resource allocation,and evalu-ation of socio-economic impact.A multi-source data surveillance system was constructed,encompassing surveillance of fever clinics at medical institutions(weekly collection of visits,positive nucleic acid and antigen test results,inpatients,and severe cases in sec-ondary or above hospitals),population nucleic acid test monitoring(weekly person-times and positivity rates of single-tube tests from the provincial system),sentinel hospital monitoring(weekly proportion of influenza-like illness visits at 18 sentinel hospitals and re-lated viral testing data),and monitoring of novel coronavirus variants(weekly systematic collection of genomic sequences of local and imported cases).Line charts were plotted weekly,and time series analysis,molecular epidemiological methods,and an improved SEIAR model were used to simulate epidemic spread.During the study period,the COVID-19 epidemic in Fujian Province exhibited three distinct stages.In the infection peak stage(52nd week of 2022),the provincial fever clinic visits reached 606 893 person-times,and a 49.2%positivity rate in population single-tube nucleic acid tests and 63.8%positivity rate in sentinel hospital monitoring were observed.In the medical load peak stage(2nd week of 2023),274 460 inpatients and 28 487 severe cases were recorded.In the epidemic decline stage(4th to 5th weeks of 2023),fever clinic visits decreased by 96.3%with respect to the peak,the single-tube nucleic acid test positivity rate decreased to 6.3%,and the sentinel hospital COVID-19 nucleic acid test positivity rate was 6.4%.All 508 sequenced local cases were Omicron variants,predominantly BA.5.2 and its sub-lineages(67.4%).Among 56 imported se-quenced cases,BA.5.2 and its sub-lineages accounted for 50.0%,and 16.1%comprised nine variants of interest including XBB and BQ.The model predicted the infection peak in the 52nd week of 2022,whereas the hospitalization peak lagged by approximately 10.6 days.Multi-source data monitoring revealed a three-stage development of the COVID-19 epidemic in Fujian.The BA.5.2 strain was dominant during the epidemic.The combination of multi-source monitoring data and modeling provides important references for epi-demic prevention and control,and highlights the need to improve the monitoring system in follow-up.

[摘 要] 目的：研究芳香烃受体（AHR）在乳腺癌中的表达及其对乳腺癌细胞增殖、凋亡和药物敏感性的调控机制。方法：通过GEPIA数据库数据分析乳腺癌组织及癌旁组织中AHR的表达水平，探讨其与患者生存期的关联。利用基因敲低和过表达技术构建AHR表达变化的乳腺癌细胞，采用CCK-8实验、细胞计数和流式细胞分析等方法评估AHR对细胞增殖、凋亡和药物敏感性的影响，通过免疫印迹法验证相关分子机制。此外，利用AHR激动剂6-甲酰基吲哚并[3,2-B]咔唑（FICZ）研究外源性激活AHR对乳腺癌细胞多柔比星（DOX）敏感性的影响。结果：GEPIA数据库数据分析结果显示，乳腺癌组织中AHR呈明显低表达（P < 0.05）；对155例乳腺癌患者的生存期进行统计分析也显示AHR低表达与不良预后呈正相关（P < 0.05）。敲低AHR促进细胞增殖（P < 0.05），过表达则能抑制其增殖（P < 0.05）并促进其凋亡（P < 0.05）。外源激活AHR能增强乳腺癌细胞对DOX的敏感性（P < 0.05）。AHR可与MYC基因启动子结合，抑制MYC表达（P < 0.05），从而影响乳腺癌的进展。结论：AHR在乳腺癌中通过调控MYC表达影响细胞增殖和凋亡，外源激活AHR可能成为提高乳腺癌细胞对DOX敏感性的治疗策略。

Objective To evaluate the postoperative survival quality in the patients with heart valve re-placement (HVR) in Three Gorges Reservoir area,and to analyze its main influencing factors.Methods A to-tal of 343 valvular heart disease patients from Three Gorges Reservoir area who received HVR treatment for the first time in this hospital from January 2019 to December 2021 were selected by the convenience sampling method.The general data questionnaire and the MOS 36-item short form health survey (SF-36) were adopted to conduct the survey.The main influencing factors affecting the survival quality were analyzed.Results Af-ter HVR,the physical components summary (PCS) score of SF-36 was 238.0±73.6,and the mental compo-nents summary (MCS) score was 254.8±83.6,and the scores in each dimension were significantly lower than those of the Chinese norm (P<0.05).The multiple stepwise linear regression analysis results showed that the age,gender,place of residence,education level,postoperative time,complications and readmission were the influencing factors of PCS and MCS scores (P<0.05).Conclusion The survival quality of the patients af-ter HVR is different from that of healthy population.The targeted intervention could be carried out according to the influencing factors of the survival quality of the patients,so as to improve their survival quality.