Organoid technology， a rapidly advancing three-dimensional （3D） cell culture platform， can closely mimic the microarchitecture and functions of human digestive organs， effectively overcoming the limitations of conventional two-dimensional cell models and animal experiments. By systematically summarizing the distinctive strengths of organoid technology in simulating digestive physiological and pathological states， constructing digestive system disease models， enabling high-throughput drug screening， and facilitating personalized treatment， this review explored the potential applications of organoids in identifying active components of traditional Chinese medicine （TCM） formulas， evaluating in vitro pharmacokinetics and toxicological parameters， and investigating multi-target synergistic mechanisms. By integrating cutting-edge engineering technologies， organoids are expected to provide a more scientific research platform for TCM， accelerate the modernization of its mechanistic studies， and enhance its scientific value and global impact.

Histological staining is the basis of pathological analysis,but the traditional staining method relies on chemical reagents,which not only consumes a lot of resources,but also causes harm to the environment and human health.In recent years,with the rapid development of deep learning technology,virtual staining technology,as a new method,is expected to effectively replace and supplement the traditional histological staining methods.It uses neural networks to analyze unstained tissue images,generate digital images that are highly similar to chemical staining effects,and even realize the mutual conversion between different staining modes,reducing the laboratory's dependence on chemical reagents and providing sustainable research programs.In this paper,the basic principles of virtual staining and its potential applications in histopathology are introduced in detail,and the current challenges and future research directions are discussed.

The theory of "holism of five organs" highly encapsulates the understanding of TCM on the integrity, connectivity, and unity of the internal life functions and morphological structures of the human body. Functional gastrointestinal disorders (FGIDs) frequently overlap, and TCM has significant advantages in their prevention and treatment based on the theory of the holistic view and syndrome differentiation and treatment. Exosomes are extracellular vesicles whose secretory form and biological functions fully reflect the "holism of five organs", and they are of great value in the pathogenesis, diagnosis and treatment of overlap of symptoms of FGIDs. This article interpreted the relationship between exosomes and overlap of symptoms of FGIDs with the "holism of five organs", aiming to provide new ideas and methods for the prevention and treatment of overlap of symptoms of FGIDs, as well as partly explain the scientific connotation of the theory of "holism of five organs".

Objective:To investigate the ameliorative effects of N-acetyl-D-mannosamine(ManNAc) on glucose and lipid metabolic disorders in obese mice.Methods:In vivo experiments were conducted using 21 four-week-old C57BL/6JGpt mice, randomly divided into three groups( n=7 per group): a normal control group, a high-fat diet(HFD) control grooup, and a ManNAc treatment group(400 mg·kg -1·d -1). The intervention lasted for 20 weeks. Body weight, food intake, and fasting blood glucose levels were monitored weekly. Glucose tolerance tests(GTT), insulin sensitivity tests(ITT), and respiratory metabolism monitoring were performed in the 17th, 18th, and 19th weeks, respectively. At the end of the experiment, whole-body fat distribution was assessed, and serum lipid profiles were measured. Liver and adipose tissue weights were recorded, and histological analyses including HE staining of liver, adipose and pancreatic tissues were performed. Liver transcriptome sequencing and quantitative real-time PCR(qPCR) were conducted to evaluate hepatic gene expression. In vitro, a hepatic steatosis model was established by inducing HepG2 cell with 0.4 mmol/L oleic acid, followed by treatment with 500 μg/mL ManNAc. Lipid accumulation was assessed using BODIPY staining, and the expression of lipid metabolism-related genes was quantified by qPCR. Results:ManNAc administration attenuated HFD-induced weight gain, reduced total body fat volume, and decreased liver and adipose tissue weights as well as intracellular lipid accumulation. Pancreatic islet numbers increased, while fasting blood glucose levels, glucose tolerance, and insulin sensitivity significantly improved. Serum levels of triglycerides, total cholesterol, and low-density lipoprotein levels were decreased, accompanied by enhanced energy expenditure. Additionally, hepatic expression of Cd36, Fabp3, and Scd1 was downregulated. In vitro, ManNAc significantly reduced lipid accumulation in HepG2 cells and downregulated the expression of Cd36, Fabp3, and Scd1 genes.Conclusion:ManNAc may improve glucose and lipid metabolism by modulating the PPARs-mediated fatty acid metabolic pathway, reducing lipogenesis, promoting fatty acid oxidation and energy expenditure, and enhancing insulin sensitivity, ultimately ameliorating disorders in obese mice.

In recent years, with the rapid development of artificial intelligence technology, the application of deep learning in the field of pathology has been continuously expanding. Particularly, the rise of multimodal data fusion methods has opened up new technical paths for the precise diagnosis, prognosis assessment, and individualized treatment of tumors. By integrating multi-level and multi-source data such as clinical information, pathological omics, molecular omics, and imaging omics, deep learning models can identify potential associated features and key biological mechanisms that are difficult to reveal by a single modality, thereby significantly improving the accuracy of disease classification and the scientific nature of risk stratification. This article systematically reviews the research progress of multimodal data fusion methods based on deep learning in the field of pathology in recent years, focuses on sorting out different types of fusion strategies, evaluates their advantages and challenges in practical clinical applications, and looks forward to future development trends.

To rapidly detect and differentiate Mycoplasma gallisepticum(MG)and Mycoplasma synovialis(MS),two sets of specific primers and TaqMan probes were designed in this study based on the conserved regions of the 16S rRNA gene of two pathogens in NCBI.A dual TaqMan fluorescence quantitative PCR method for simultaneous detection of MG and MS was established by optimizing the reaction conditions,and the specificity,sensitivity,repeatability,and reliability of the method were verified.The results showed that this method could specifically amplify MG and MS without cross reactivity with 21 pathogens.The sensitivity experiment results showed that the detection limits of this method for MG and MS were 5.40×10 1 copies/μL and 6.60 × 10 1 copies/μL,and the sensitivity was 10 to 100 times higher than that of known methods.In addition,the re-sults of repetitive experiments showed that the coefficient of variation within and between groups was less than 1％.Compared with the single PCR amplification method in NY/T 553-2015,the positive sample detection coincidence rate,negative sample detection coincidence rate,and total sample detection coincidence rate were all 100.00％,indicating the strong reliability of this method.Using this method to detect 84 suspected Mycoplasma infected chicken samples,the results showed that the MG positivity rate was 32.14％(27/84),the MS positivity rate was 22.62％(19/84),and the positivity rate of samples infected with MG and MS was 16.67％(14/84).Concurrent-ly,182 healthy chicken cloacal swab samples,118 healthy chicken nasal swab samples,and 74 chicken farm environmental samples were detected,and the results showed that all samples were positive for Mycoplasma.The above results indicate that this method can be applied to the detec-tion of various clinical samples.In summary,the method established in this study had the advanta-ges of high specificity,high sensitivity,and good reproducibility.It could be used for clinical differ-ential diagnosis,epidemiological investigation,and pathogen purification of MG and MS infections.

Histological staining is the basis of pathological analysis,but the traditional staining method relies on chemical reagents,which not only consumes a lot of resources,but also causes harm to the environment and human health.In recent years,with the rapid development of deep learning technology,virtual staining technology,as a new method,is expected to effectively replace and supplement the traditional histological staining methods.It uses neural networks to analyze unstained tissue images,generate digital images that are highly similar to chemical staining effects,and even realize the mutual conversion between different staining modes,reducing the laboratory's dependence on chemical reagents and providing sustainable research programs.In this paper,the basic principles of virtual staining and its potential applications in histopathology are introduced in detail,and the current challenges and future research directions are discussed.

To rapidly detect and differentiate Mycoplasma gallisepticum(MG)and Mycoplasma synovialis(MS),two sets of specific primers and TaqMan probes were designed in this study based on the conserved regions of the 16S rRNA gene of two pathogens in NCBI.A dual TaqMan fluorescence quantitative PCR method for simultaneous detection of MG and MS was established by optimizing the reaction conditions,and the specificity,sensitivity,repeatability,and reliability of the method were verified.The results showed that this method could specifically amplify MG and MS without cross reactivity with 21 pathogens.The sensitivity experiment results showed that the detection limits of this method for MG and MS were 5.40×10 1 copies/μL and 6.60 × 10 1 copies/μL,and the sensitivity was 10 to 100 times higher than that of known methods.In addition,the re-sults of repetitive experiments showed that the coefficient of variation within and between groups was less than 1％.Compared with the single PCR amplification method in NY/T 553-2015,the positive sample detection coincidence rate,negative sample detection coincidence rate,and total sample detection coincidence rate were all 100.00％,indicating the strong reliability of this method.Using this method to detect 84 suspected Mycoplasma infected chicken samples,the results showed that the MG positivity rate was 32.14％(27/84),the MS positivity rate was 22.62％(19/84),and the positivity rate of samples infected with MG and MS was 16.67％(14/84).Concurrent-ly,182 healthy chicken cloacal swab samples,118 healthy chicken nasal swab samples,and 74 chicken farm environmental samples were detected,and the results showed that all samples were positive for Mycoplasma.The above results indicate that this method can be applied to the detec-tion of various clinical samples.In summary,the method established in this study had the advanta-ges of high specificity,high sensitivity,and good reproducibility.It could be used for clinical differ-ential diagnosis,epidemiological investigation,and pathogen purification of MG and MS infections.

In recent years, with the rapid development of artificial intelligence technology, the application of deep learning in the field of pathology has been continuously expanding. Particularly, the rise of multimodal data fusion methods has opened up new technical paths for the precise diagnosis, prognosis assessment, and individualized treatment of tumors. By integrating multi-level and multi-source data such as clinical information, pathological omics, molecular omics, and imaging omics, deep learning models can identify potential associated features and key biological mechanisms that are difficult to reveal by a single modality, thereby significantly improving the accuracy of disease classification and the scientific nature of risk stratification. This article systematically reviews the research progress of multimodal data fusion methods based on deep learning in the field of pathology in recent years, focuses on sorting out different types of fusion strategies, evaluates their advantages and challenges in practical clinical applications, and looks forward to future development trends.

Objective:To investigate the ameliorative effects of N-acetyl-D-mannosamine(ManNAc) on glucose and lipid metabolic disorders in obese mice.Methods:In vivo experiments were conducted using 21 four-week-old C57BL/6JGpt mice, randomly divided into three groups( n=7 per group): a normal control group, a high-fat diet(HFD) control grooup, and a ManNAc treatment group(400 mg·kg -1·d -1). The intervention lasted for 20 weeks. Body weight, food intake, and fasting blood glucose levels were monitored weekly. Glucose tolerance tests(GTT), insulin sensitivity tests(ITT), and respiratory metabolism monitoring were performed in the 17th, 18th, and 19th weeks, respectively. At the end of the experiment, whole-body fat distribution was assessed, and serum lipid profiles were measured. Liver and adipose tissue weights were recorded, and histological analyses including HE staining of liver, adipose and pancreatic tissues were performed. Liver transcriptome sequencing and quantitative real-time PCR(qPCR) were conducted to evaluate hepatic gene expression. In vitro, a hepatic steatosis model was established by inducing HepG2 cell with 0.4 mmol/L oleic acid, followed by treatment with 500 μg/mL ManNAc. Lipid accumulation was assessed using BODIPY staining, and the expression of lipid metabolism-related genes was quantified by qPCR. Results:ManNAc administration attenuated HFD-induced weight gain, reduced total body fat volume, and decreased liver and adipose tissue weights as well as intracellular lipid accumulation. Pancreatic islet numbers increased, while fasting blood glucose levels, glucose tolerance, and insulin sensitivity significantly improved. Serum levels of triglycerides, total cholesterol, and low-density lipoprotein levels were decreased, accompanied by enhanced energy expenditure. Additionally, hepatic expression of Cd36, Fabp3, and Scd1 was downregulated. In vitro, ManNAc significantly reduced lipid accumulation in HepG2 cells and downregulated the expression of Cd36, Fabp3, and Scd1 genes.Conclusion:ManNAc may improve glucose and lipid metabolism by modulating the PPARs-mediated fatty acid metabolic pathway, reducing lipogenesis, promoting fatty acid oxidation and energy expenditure, and enhancing insulin sensitivity, ultimately ameliorating disorders in obese mice.