Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.

Objective:To investigate the characteristics of fatty acid metabolism in the hepatic steatosis (HS) population and to explore the role of inflammation in the relationship between fatty acid metabolism and HS.Methods:A total of 3 112 subjects from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) database were included in this retrospective study, among whom 1 552 were in the HS group (weighted percentage: 49.87%) and 1 560 in the control group (weighted percentage: 50.13%). The differences in dietary fatty acid and serum free fatty acid (FFA) levels and metabolic characteristics were compared. Logistic and weighted quantile sum (WQS) regression models were used to evaluate the impacts of various FFA indexes on HS. Mediation analysis was conducted to examine whether inflammation served as a mediator in the relationship between FFA and HS.Results:Dietary fatty acid intake did not differ significantly between the HS and control groups. However, serum FFA metabolism characteristics differed between these two groups, with the concentrations of 28 FFAs in the HS group significantly higher than those in the control group. The risk of HS was correlated negatively with serum unsaturated fatty acids/saturated fatty acids ( OR=0.69, 95% CI: 0.64–0.75, P<0.001), polyunsaturated fatty acids/saturated fatty acids ( OR=0.63, 95% CI: 0.99–0.69, P<0.001), and Health Promotion Index ( OR=0.78, 95% CI: 0.74–0.82, P<0.001) but positively with thrombosis index ( OR=2.20, 95% CI: 1.89–2.55, P<0.001). The WQS model showed that behenic acid had the greatest contribution weight to increase the risk of HS among the 30 FFAs. After adjusting for confounding factors, the risk of HS increased by 167% (95% CI: 2.14–3.34, P<0.001) for each unit increase in the WQS index. Further mediation analysis showed that albumin/globulin ratio, white blood cell count, and neutrophil to platelet ratio significantly mediated the relationship between serum FFA and HS ( P<0.05). Conclusion:People with similar dietary fatty acid structures may present different serum FFA metabolism characteristics. Serum FFA level correlates with the risk of HS, and inflammation plays an important role in mediating the relationship between fatty acid metabolism and HS.

Objective:To investigate the characteristics of fatty acid metabolism in the hepatic steatosis (HS) population and to explore the role of inflammation in the relationship between fatty acid metabolism and HS.Methods:A total of 3 112 subjects from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) database were included in this retrospective study, among whom 1 552 were in the HS group (weighted percentage: 49.87%) and 1 560 in the control group (weighted percentage: 50.13%). The differences in dietary fatty acid and serum free fatty acid (FFA) levels and metabolic characteristics were compared. Logistic and weighted quantile sum (WQS) regression models were used to evaluate the impacts of various FFA indexes on HS. Mediation analysis was conducted to examine whether inflammation served as a mediator in the relationship between FFA and HS.Results:Dietary fatty acid intake did not differ significantly between the HS and control groups. However, serum FFA metabolism characteristics differed between these two groups, with the concentrations of 28 FFAs in the HS group significantly higher than those in the control group. The risk of HS was correlated negatively with serum unsaturated fatty acids/saturated fatty acids ( OR=0.69, 95% CI: 0.64–0.75, P<0.001), polyunsaturated fatty acids/saturated fatty acids ( OR=0.63, 95% CI: 0.99–0.69, P<0.001), and Health Promotion Index ( OR=0.78, 95% CI: 0.74–0.82, P<0.001) but positively with thrombosis index ( OR=2.20, 95% CI: 1.89–2.55, P<0.001). The WQS model showed that behenic acid had the greatest contribution weight to increase the risk of HS among the 30 FFAs. After adjusting for confounding factors, the risk of HS increased by 167% (95% CI: 2.14–3.34, P<0.001) for each unit increase in the WQS index. Further mediation analysis showed that albumin/globulin ratio, white blood cell count, and neutrophil to platelet ratio significantly mediated the relationship between serum FFA and HS ( P<0.05). Conclusion:People with similar dietary fatty acid structures may present different serum FFA metabolism characteristics. Serum FFA level correlates with the risk of HS, and inflammation plays an important role in mediating the relationship between fatty acid metabolism and HS.

Objective:To explore the application of dental scenario-based clinical thinking teaching and examination system in oral education.Methods:A total of 70 resident physicians from the 2022 class at the dental base of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine were selected and randomly divided into a control group (30 participants) and an experimental group (40 participants). The control group was taught using traditional teaching methods, and the experimental group was taught and trained using the dental scenario-based clinical thinking teaching and examination system. After one year of teaching, both groups participated in a unified annual assessment. Finally, the teaching effectiveness was evaluated through a questionnaire survey on the experimental group. The questionnaire data were analyzed using the Chi-square test in IBM SPSS 24.0 statistical software, and the t-test was conducted using GraphPad Prism 8.0.2 software. Results:The average score before teaching was (78.83±2.61) in the control group and (78.28±3.29) in the experimental group, with no statistical significant difference between the two groups ( t=0.77, P=0.447). The average score in the annual assessment after teaching was (78.57±3.50) in the control group and (81.75±4.63) in the experimental group, with statistical significant difference between the two groups ( t=3.15, P=0.002). The questionnaire survey indicated that students highly recognized the dental scenario-based clinical thinking teaching and examination system, noting its advantages of innovative and interesting lectures, the ability to improve clinical thinking skills, and not being limited by location or time. Conclusions:The dental scenario-based clinical thinking teaching and examination system can stimulate students' interest and enthusiasm for independent learning. The system has received high levels of acceptance among students. The application and development of the dental scenario-based clinical thinking teaching and examination system hold significant value.

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.

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.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.