Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

Objective:To explore the correlation between serum ferritin (SF) and risk of lean non-alcoholic fatty liver disease (NAFLD), so as to provide the basis for the prevention and treatment of lean NAFLD.Methods:A total of 7 187 people without NAFLD at baseline who took at least 2 physical examinations in the Health Management Center of the Second Hospital of Dalian Medical University from January 2014 to December 2023 and met the selection criteria were selected as the research subjects, and all the subjects had no NAFLD at baseline. Subjects were divided into four groups according to baseline SF quartiles: 1 797 cases in the first quartile ( Q1) group, 1 797 cases in the second quartile ( Q2) group, 1 797 cases in the third quartile ( Q3) group, and 1 796 cases in the fourth quartile ( Q4) group. The incidence of lean NAFLD in each group were observed. Kaplan-Meier curve was plotted to calculate the cumulative incidence of lean NAFLD which compared by log-rank test. Cox proportional hazard regression model was used to analyze the correlation between SF and new-onset lean NAFLD, Q1, Q2, Q3 and Q4 of SF were taken as continuous variables into the model for trend test.The stability of the results was verified by two item sensitivity analyses. Time-dependent receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of SF for the onset of lean NAFLD. Results:The cumulative follow-up were 25 076 person-years. There were 230 new cases of lean NAFLD, and the incidence density was 9.172/1 000 person-years. The incidence densities of lean NAFLD in Q1, Q2, Q3 and Q4 groups were 6.915/1 000 person-years, 8.552/1 000 person-years, 9.641/1 000 person-years, 12.003/1 000 person-years, respectively. Kaplan-Meier curve indicated that the incidence of lean NAFLD was increased with the increment of SF, and the difference was statistically significant (log-rank test, χ2=9.92, P=0.019). Cox proportional hazard regression model results showed that the risk of developing lean NAFLD in Q4 group increased by 72.8% ( HR=1.728, 95% confidence interval (95% CI): 1.059 to 2.820) compared with Q1 group. Trend analysis revealed that the risk of lean NAFLD increased by 18.9% for each one-quartile increase of SF( HR=1.189, 95% CI: 1.012 to 1.396). Two sensitivity analyses indicated that the risk of NAFLD in Q4 group was 1.795 times ( HR=1.795, 95% CI: 1.083 to 2.975) or 1.654 times ( HR=1.654, 95% CI: 1.022 to 2.678) higher than that in Q1 group. The area under the curve (95% CI) of SF for predicting the incidence of lean NAFLD at 2-, 3-, 7- and 8-year follow-up based on time-dependent ROC were 0.645 (0.593 to 0.698), 0.652 (0.603 to 0.700), 0.605 (0.539 to 0.672) and 0.716 (0.597 to 0.836), respectively. Conclusion:SF is an independent risk factor for lean NAFLD and has predictive value for the new-onset of lean NAFLD.

Objective:To explore the correlation of cumulative atherogenic index of plasma (cumAIP) and cumulative atherosclerosis index (cumAI) with new-onset non-alcoholic fatty liver disease (NAFLD).Methods:From January 2017 to December 2023, 2 472 subjects who underwent health checkups at the Second Affiliated Hospital of Dalian Medical University for 3 consecutive years were enrolled. Triglyceride, total cholesterol, high density lipoprotein cholesterol and their measurement time intervals were used to calculate cumAIP and cumAI. The subjects were divided into Q1, Q2, Q3 and Q4 groups with the threshold values of 25th percentile, median and 75th percentile of the baseline atherogenic index of plasma (AIP) and atherosclerotic index (AI) subjects. Cox regression model was used to analyze the effects of cumAIP and cumAI on the new-onset NAFLD, restricted cubic spline was performed to analyze the nonlinear association between cumAIP and cumAI and new-onset NAFLD, and the clinical decision curve was used to compare the decision value of different indicators for NAFLD. Results:The risk of NAFLD gradually increased along with the increasing of cumAIP and cumAI. In the quartile groups of cumAIP, the incidence of Q1 to Q4 groups was 6.15%, 8.74%, 15.05%, and 25.08%, respectively. In the quartile groups of cumAI, the incidence of Q1 to Q4 groups was 5.99%, 11.17%, 15.21%, and 22.65%, respectively. After adjusting the confounding factors, the risk of new-onset NAFLD in the high-level group ( Q4) was higher than that in the low-level cumAIP group ( Q1) ( HR=3.15, 95% confidence interval (95% CI): 2.15 to 4.63, P<0.001) and the high-level cumAI group ( Q4) ( HR=2.74, 95% CI: 1.82 to 4.10, P<0.001). cumAIP and cumAI showed a significant nonlinear association with new-onset NAFLD ( χ2=119.15, 94.53; both P<0.001). The cumAIP had higher predictive value for NAFLD than the other cumulative lipid metrics and baseline AIP or AI. Conclusion:CumAIP and cumAI can be served as new predictive indicators of NAFLD, with a particular focus on the dynamic cumulative changes of AIP, which can achieve effective early screening for NAFLD.

Objective:To construct a VP8-mRNA vaccine using human rotavirus spike protein VP8 domain as the immunogen and analyze its immunogenicity in mice.Methods:The VP8-mRNA sequence was designed, optimized, and synthesized. The VP8 gene of rotavirus G1P[8] type was used to construct the plasmid pUC57-VP8-Kan-SapⅠ, which was then sequenced. The plasmid confirmed by sequencing was subjected to large-scale amplification and extraction, followed by linearization, in vitro transcription, and capping. The purified capped products were encapsulated with lipid nanoparticles using a microfluidic control apparatus. The encapsulated VP8-mRNA vaccine was administered intramuscularly to mice at 10, 15, and 20 μg. Serum samples were collected for antibody detection by ELISA. Cellular immune responses were detected by flow cytometry and ELISPOT. Statistical analysis was performed using one-way or two-way analysis of variance and Tukey-Kramer test. Results:The encapsulated VP8-mRNA vaccine was rounded and spherical, with a particle size of about 100 nm, a polymer dispersion index of 0.088, and an encapsulation rate of 92.3%. Two doses of VP8-mRNA vaccine immunization could induce a good immune response in mice. The level of IgG antibody induced after immunization in the 15 μg group was comparable to that of the 20 μg group, and there was no statistical difference ( P>0.05), but the antibody levels in the two groups were significantly higher than that in the 10 μg group ( P<0.000 1). VP8-mRNA vaccine could induce neutralizing antibodies against rotavirus G1 and G9 types. The highest level of neutralizing antibodies against rotavirus type G1 was observed in the 15 μg group, which was significantly higher than that in the 10 μg group ( P<0.05). All immunization groups exhibited good neutralizing ability against rotavirus G9 type. The results of ELISPOT showed that lymphocytes from mice in each vaccine group were able to secrete IFN-γ when stimulated with VP8 peptide. Flow cytometry showed that the proportions of CD8 + T cell subsets in the vaccine groups were higher than that in the control group. Conclusion:The VP8-mRNA vaccine has good immunogenicity in mice and can induce good humoral and T-cell immune responses.

Objective:Investigating the association of the systemic immune-inflammation index and the pan-inflammation index with incident non-alcoholic fatty liver disease (NAFLD).Methods:This retrospective cohort study included 42 891 participants who underwent at least two health examinations at the Second Affiliated Hospital of Dalian Medical University between 2014 and 2023. Based on their levels of the systemic immune inflammation index (SII) and the pan immune inflammation value (PIV), participants were respectively divided into four quartile groups (Q1 to Q4). Cox proportional hazards models were employed to analyze the association of different SII and PIV levels, as well as their quartile groups, with new onset NAFLD in the total population and across various subgroups. Restricted cubic splines were used to examine the dose response relationship between these inflammatory indices and incident NAFLD. Additionally, sensitivity analyses were conducted to confirm the robustness of the findings.Results:After adjusting for confounding factors, the natural logarithm-transformed lnSII ( HR=1.247, 95% CI: 1.184-1.314, P<0.001) and lnPIV ( HR=1.192, 95% CI: 1.148-1.238, P<0.001) were significantly positively associated with the risk of NAFLD. When the subjects were grouped by SII quartiles (Q1-Q4), compared with those in Q1, participants in Q2, Q3, and Q4 exhibited progressively higher risks of incident NAFLD:11.9% ( HR=1.119, 95% CI: 1.051-1.192, P<0.001), 17.1% ( HR=1.171, 95% CI: 1.100-1.248, P<0.001), and 29.1% ( HR=1.291, 95% CI: 1.211-1.377, P<0.001), respectively. Quartile analysis of PIV yielded similar trends: the risk of incident NAFLD increased for 10.4% ( HR=1.104, 95% CI: 1.034-1.179, P=0.003), 18.7% ( HR=1.187, 95% CI: 1.112-1.266, P<0.001), and 30.5% ( HR=1.305, 95% CI: 1.223-1.393, P<0.001) in Q2, Q3, and Q4 group respectively when compared to that in Q1 group. Subgroup analysis confirmed consistent associations of SII and PIV with elevated NAFLD risk across all subgroups. Conclusion:Elevated levels of SII and PIV are significantly associated with increased risk of NAFLD.

Objective:To investigate transition pattern of health status among middle-aged and elderly population in China based on frailty index.Methods:In this retrospective cohort study, middle-aged and elderly people were selected from the China Health and Retirement Longitudinal Study (CHARLS) in 2011; and 1 434 subjects were followed up to 2015. The frailty index was calculated from the prevalence of chronic diseases, daily activity ability and blood biomarkers, and the frailty state was divided by quartiles of the frailty index. Markov models were constructed to determine the transition probabilities of different frailty states.Results:The mean age of the 1 434 subjects was (59.0±9.4) years and the mean frailty index was 0.11±0.05. In the healthy individuals, 63.0% remained healthy after a four-year follow-up; during the same follow-up period, 40.9% of the mildly frail individuals and 23.0% of the moderately frail individuals remained in their baseline frailty status. Increasing age leaded to a gradual increase in the probability of the population shifting to a severely frailty state. Women were more likely to shift to severe frailty status than men (0.029 vs 0.019, Z=3.03, P=0.002). Conclusion:Among middle-aged and elderly population in China, the transition of health states follows a pattern where higher frailty levels are associated with lower stability. Advanced age and female gender are identified as risk factors for progression to severe frailty.

Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.

Objective To elucidate the causal relationship between specific immune cells and autoimmune hepatitis(AIH)using a two-sample Mendelian randomization(MR)approach.Methods A bidirectional MR analysis was conducted using data from large publicly accessible Genome-Wide Association Study(GWAS)databases.The inverse variance weighted(IVW)method was employed as the primary method to evaluate the relationship between 731 immune cell traits and AIH.The false discovery rate(FDR)was controlled using the Benjamini-Hochberg correction.Additionally,pleiotropy and heterogeneity tests were performed,and a leave-one-out sensitivity analysis was conducted to further validate the robustness of the results.Results At a significance level of 0.20,it was found that the absolute count of CD28-CD8+regulatory T-cells(IVW:odds ratio[OR]=1.486;95％confidence interval[CI],1.189-1.859;P＜0.001;PFDR=0.185),the level of CD28 on CD39+secreting regulatory T-cells(IVW:OR=1.194;95％CI,1.074-1.328;P=0.001;PFDR=0.185),and the level of CD45 on mononuclear myeloid-derived suppressor cells(IVW:OR=1.243;95％CI,1.108-1.394;P＜0.001;PFDR=0.143)were associated with an increased risk of AIH.The level of programmed death-ligand 1 on CD14+CD16+monocytes(IVW:OR=0.849;95％CI,0.771-0.935;P＜0.001;PFDR=0.185)was associated with a reduced risk of AIH.Conclusion Four immune cell phenotypes associated with AIH risk are identified.Further investigation is needed to validate these findings and explore new therapeutic avenues.

Objective:To investigate the effect of changes in metabolic syndrome status and persistence on carotid plaque risk.Methods:This retrospective cohort study analyzed individuals who underwent routine health check-ups at the health management center of the Second Affiliated Hospital of Dalian Medical University from 2014 to 2023. Participants with at least three carotid ultrasound records meeting the inclusion criteria were classified into 4 groups based on changes in metabolic status: persistently metabolic health, transitioning from metabolic health to unhealth, transitioning from metabolic unhealth to health, and persistently metabolic unhealth. The cumulative incidence of carotid plaque in these groups was compared. A Cox proportional risk model was used to evaluate the relationship between changes in metabolic syndrome status, the number of metabolic syndrome components, and the risk of carotid plaque development. Restricted cubic spline analysis was applied to explore the association between changes in individual metabolic syndrome components and carotid plaque risk.Results:Compared to the persistently metabolic health group, the persistent unhealth group had the highest risk of developing carotid plaque( HR=1.35, 95% CI 1.05-1.74, P=0.021), followed by those who transitioned from metabolic health to unhealth and those who improved from metabolic unhealth to health. Furthermore, the risk of carotid plaque increased progressively with the number of metabolic syndrome components. Restricted cubic spline analysis revealed a nonlinear relationship between fasting blood glucose change and carotid plaque risk, while systolic blood pressure, diastolic blood pressure, waist circumference, triglycerides, and high-density lipoprotein-cholesterol showed a linear dose-response relationship with carotid plaque. Conclusions:The change of metabolic syndrome is associated with the risk of developing carotid plaque, and maintaining metabolic health, recovering from metabolic syndrome, or minimizing the number of metabolic syndrome components may be effective strategies to prevent carotid plaque formation.