Objective The aim of this study is to examine the epidemiological features of foodborne disease outbreaks in Nanjing between 2013 and 2023, in order to offer a scientific foundation for the prevention and management of foodborne diseases. Methods Gathering information on foodborne disease outbreaks in Nanjing from 2013 to 2023, conduct descriptive analysis on the epidemiological characteristics of outbreak events, including time distribution, location distribution, and pathogenic factor distribution. A comparison of the rates was conducted using a χ² test, with P<0.05 signifying statistical significance. Results Nanjing experienced 145 outbreaks of foodborne diseases. resulting in 21246 people being exposed, 2 488 affected and 3 fatalities from 2013 to 2023. The epidemic is primarily concentrated in the third quarter. There is a significant statistical difference in the incidence rate between different quarters (χ²=121.063, P<0.001). The number of events caused by Vibrio parahaemolyticus is the highest, accounting for 23.45%. The identification rate of problematic foods is 67.59%, with meat and meat products being the most pathogenic (18.62%). The outbreak was mainly caused by improper storage and processing. The restaurants being the most common (41.38%). Conclusions Nanjing should focus on monitoring outbreaks caused by Vibrio parahaemolyticus, especially in the third quarter; it is necessary to strengthen the health education of poisonous mushrooms; Nanjing should focus on the storage and processing of meat and meat products, and enhance food safety supervision in catering service venues, and improve on-site sampling and laboratory testing capabilities, thus drastically diminishing the risk of foodborne disease outbreaks.

Objective: To prepare the erythrocyte-liposome drug delivery system to enhance the therapeutic effect of drugs on tumors and inhibit tumor metastasis. Methods: This study prepared and characterized paclitaxel (PTX)-plerixafor (AMD3100) liposomes (Lips), developed the erythrocyte-liposome drug delivery system, and evaluated its targeting efficiency and therapeutic efficacy through a series of in vitro cellular and in vivo animal experiments. Results: The particle size of PTX-AMD-Lips was (186.4±0.83) nm. Drug encapsulation efficiency of PTX-AMD-Lips was (75.50±5.27)% for PTX and (88.31±2.45)% for AMD. The Binding efficiency between RBC and liposomes in the drug delivery system was (69.93±2.55)%. Vitro cellular experiments revealed that PTX-AMD-Lips significantly inhibited tumor cell migration. In vivo animal experiments, the erythrocyte-liposome drug delivery system significantly increased drug accumulation in the lungs. At the experimental endpoint, the quantitative fluorescence signal of tumor size measured (4.04±0.44)×10 for the PTX-Lips group, and (5.14±3.40)×10 for the RBC-PTX-AMD-Lips group. Conclusion: The erythrocyte-liposome drug delivery system could enhance the lung-specific targeting capability of liposomes, kill tumor cells and suppress further metastasis effectively.

Hepatocellular carcinoma(HCC)is one of the most prevalent malignancies worldwide,and accurate prognostic assessment and treatment planning are vital for improving patient outcomes.Conventional prognostic models,which rely on limited clinicopathological parameters,often fail to capture the profound heterogeneity of HCC.In recent years,artificial intelligence(AI)-particularly machine learning and deep learning-has driven a paradigm shift in precision oncology by leveraging its powerful capabilities in data mining and pattern recognition.This review provides a comprehensive overview of recent advances in AI for prognostic assessment and treatment optimization in HCC,with an emphasis on key methodologies such as radiomics and multi-modal data integration.It further discusses the clinical potential and challenges of AI in predicting postoperative recurrence,evaluating therapeutic response,and supporting individualized treatment decisions,while also outlining future directions in this rapidly evolving field.The review aims to inform and facilitate the clinical translation of AI technologies into the management of HCC.

Hedysari radix is the characteristic chi-nese medicine of Gansu Province,with"MiCang Hedysari radix"as the best.Modern pharmacologi-cal research has shown that it has polysaccharides and flavonoids,which have good anti-tumor effects and can inhibit the occurrence and development of various cancers,such as lung cancer,liver cancer,and breast cancer.Cancer is ranked as the second leading cause of death in the world,and the mor-bidity and mortality rates are increasing year by year,seriously affecting the quality of life.At pres-ent,with the modernization of Traditional Chinese Medicine(TCM),there has been a significant break-through in the treatment of malignant tumors with TCM.Based on this,by collating the relevant litera-ture at home and abroad in recent years,the anti-tumor effects and mechanisms of Hedysari radix and its active ingredients are summarized to pro-vide a scientific basis for the study of elucidating the material basis of the anti-tumor effects of He-dysari radix and to promote the development of the Hedysari radix industry.

Objective:To develop a scale for assessing science popularization competency in traditional Chinese medicine (TCM) and to evaluate its reliability and validity.Methods:Based on Lasswell's "5W" communication theory, the scale was constructed through literature review, expert consultation, and a pilot survey. Using convenient sampling, 450 TCM-related professionals were selected from two ClassⅢ Grade ATCM hospitals and their affiliated medical alliance community health institutions in Hunan Province. A total of 450 questionnaires were distributed and 446 valid responses were collected (valid response rate: 99.11%). Of these, 222 questionnaires were used for item analysis, reliability testing, and exploratory factor analysis (EFA) ; the remaining 224 were used for confirmatory factor analysis (CFA) .Results:The final TCM Science Popularization Competency Scale comprised 26 items under 6 dimensions: namely popular science cognition; science content creation ability; communication ability; professional values and personality traits; quality of science communication products; evaluation and feedback of communication effectiveness. EFA extracted six common factors, explaining 86.85% of total variance, with all item factor loadings > 0.500. The overall Cronbach's α coefficient for the scale was 0.972, with subscale values all > 0.800. Split-half reliability was 0.948. CFA showed good model fit across indices, indicating high reliability and validity.Conclusions:The TCM Science Popularization Competency Scale demonstrates good psychometric properties and can be used to assess the science popularization competency of TCM professionals. It offers a scientific tool to support the dissemination of TCM knowledge and contribute to the goals of the Healthy China initiative.

Hepatocellular carcinoma(HCC)is one of the most prevalent malignancies worldwide,and accurate prognostic assessment and treatment planning are vital for improving patient outcomes.Conventional prognostic models,which rely on limited clinicopathological parameters,often fail to capture the profound heterogeneity of HCC.In recent years,artificial intelligence(AI)-particularly machine learning and deep learning-has driven a paradigm shift in precision oncology by leveraging its powerful capabilities in data mining and pattern recognition.This review provides a comprehensive overview of recent advances in AI for prognostic assessment and treatment optimization in HCC,with an emphasis on key methodologies such as radiomics and multi-modal data integration.It further discusses the clinical potential and challenges of AI in predicting postoperative recurrence,evaluating therapeutic response,and supporting individualized treatment decisions,while also outlining future directions in this rapidly evolving field.The review aims to inform and facilitate the clinical translation of AI technologies into the management of HCC.

Objective:To develop a scale for assessing science popularization competency in traditional Chinese medicine (TCM) and to evaluate its reliability and validity.Methods:Based on Lasswell's "5W" communication theory, the scale was constructed through literature review, expert consultation, and a pilot survey. Using convenient sampling, 450 TCM-related professionals were selected from two ClassⅢ Grade ATCM hospitals and their affiliated medical alliance community health institutions in Hunan Province. A total of 450 questionnaires were distributed and 446 valid responses were collected (valid response rate: 99.11%). Of these, 222 questionnaires were used for item analysis, reliability testing, and exploratory factor analysis (EFA) ; the remaining 224 were used for confirmatory factor analysis (CFA) .Results:The final TCM Science Popularization Competency Scale comprised 26 items under 6 dimensions: namely popular science cognition; science content creation ability; communication ability; professional values and personality traits; quality of science communication products; evaluation and feedback of communication effectiveness. EFA extracted six common factors, explaining 86.85% of total variance, with all item factor loadings > 0.500. The overall Cronbach's α coefficient for the scale was 0.972, with subscale values all > 0.800. Split-half reliability was 0.948. CFA showed good model fit across indices, indicating high reliability and validity.Conclusions:The TCM Science Popularization Competency Scale demonstrates good psychometric properties and can be used to assess the science popularization competency of TCM professionals. It offers a scientific tool to support the dissemination of TCM knowledge and contribute to the goals of the Healthy China initiative.

Hedysari radix is the characteristic chi-nese medicine of Gansu Province,with"MiCang Hedysari radix"as the best.Modern pharmacologi-cal research has shown that it has polysaccharides and flavonoids,which have good anti-tumor effects and can inhibit the occurrence and development of various cancers,such as lung cancer,liver cancer,and breast cancer.Cancer is ranked as the second leading cause of death in the world,and the mor-bidity and mortality rates are increasing year by year,seriously affecting the quality of life.At pres-ent,with the modernization of Traditional Chinese Medicine(TCM),there has been a significant break-through in the treatment of malignant tumors with TCM.Based on this,by collating the relevant litera-ture at home and abroad in recent years,the anti-tumor effects and mechanisms of Hedysari radix and its active ingredients are summarized to pro-vide a scientific basis for the study of elucidating the material basis of the anti-tumor effects of He-dysari radix and to promote the development of the Hedysari radix industry.

Objective To predict the core targets and action pathways of Hedysari Radix based on UPLC-MS/MS and network pharmacology methods,and to verify the results of network pharmacology by molecular docking and molecular dynamics techniques.This article aims to investigate immune regulation mechanism of effective components absorbed into blood from Hedysari Radix.Methods Qualitative quantification of effective components absorbed into blood from Hedysari Radix were operated by using UPLC-MS/MS technique.The corresponding targets of effective components absorbed into blood from Hedysari Radix were screened by TCMSP and HERB databases.Targets of immune-related disease were obtained through DisGeNET,OMIM,TTD,and MalaCards databases.The network of"components absorbed into blood from Hedysari Radix-immune-related diseases"was then constructed.GO and KEGG enrichment analysis and mapped the PPI network were performed.Molecular docking and molecular dynamics techniques were applied for validation.Results A total of 8 prototype components absorbed into blood,synergistically acting on 101 targets,were identified by UPLC-MS/MS.They mediated 538 biological processes including immune response,positive regulation of gene expression,receptor binding,and cytokine activity.Meanuhile,116 signaling pathways,such as HIF-1,Toll-like receptor,JAK-STAT,T cell receptor,PI3K-Akt,and FoxO etc.were involved.The core targets were MAPK14,PTGS2,MMP9,PPARG,CCND1,etc..The results of molecular docking showed that formononetin and calycosin had strong docking binding activity with MAPK14.And molecular dynamics simulations further demonstrated that the binding between MAPK14 and formononetin or calycosin had good structural stability and binding affinity.Conclusion The results of serum pharmacochemistry,network pharmacology and molecular dynamics were verified to reveal the material basis and mechanism of Hedysari Radix in regulating immunity.The aim of this study is to provide scientific basis for its immunomodulatory mechanism.

End-stage liver diseases,such as cirrhosis and liver cancer caused by hepatitis B,are often combined with hepatic encephalopathy(HE);ammonia poisoning is posited as one of its main pathogenesis mechanisms.Ammonia is closely related to autophagy,but the molecular mechanism of ammonia's regulatory effect on autophagy in HE remains unclear.Sialylation is an essential form of glycosylation.In the nervous system,abnormal sialylation affects various physiological processes,such as neural development and synapse formation.ST3 β-galactoside α2,3-sialyltransferase 6(ST3GAL6)is one of the significant glycosyltransferases responsible for adding α2,3-linked sialic acid to substrates and generating glycan structures.We found that the expression of ST3GAL6 was upregulated in the brains of mice with HE and in astrocytes after ammonia induction,and the expression levels of α2,3-sialylated glycans and autophagy-related proteins microtubule-associated protein light chain 3(LC3)and Beclin-1 were upregulated in ammonia-induced astrocytes.These findings suggest that ST3GAL6 is related to autophagy in HE.Therefore,we aimed to determine the regulatory relationship between ST3GAL6 and autophagy.We found that silencing ST3GAL6 and blocking or degrading α2,3-sialylated glycans by way of Maackia amurensis lectin-Ⅱ(MAL-Ⅱ)and neuraminidase can inhibit autophagy.In addition,silencing the expression of ST3GAL6 can downregulate the expression of heat shock protein β8(HSPB8)and Bcl2-associated athanogene 3(BAG3).Notably,the overexpression of HSPB8 partially restored the reduced autophagy levels caused by silencing ST3GAL6 expression.Our results indicate that ST3GAL6 regulates autophagy through the HSPB8-BAG3 complex.