This study investigated the effects and underlying mechanisms of vanillic acid(VA) against cardiac fibrosis(CF) induced by isoproterenol(ISO) in mice. Male C57BL/6J mice were randomly divided into control group, VA group(100 mg·kg~(-1), ig), ISO group(10 mg·kg~(-1), sc), ISO + VA group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig), ISO + dynamin-related protein 1(Drp1) inhibitor(Mdivi-1) group(10 mg·kg~(-1), sc + 50 mg·kg~(-1), ip), and ISO + VA + Mdivi-1 group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig + 50 mg·kg~(-1), ip). The treatment groups received the corresponding medications once daily for 14 consecutive days. On the day after the last administration, cardiac functions were evaluated, and serum and cardiac tissue samples were collected. These samples were analyzed for serum aspartate aminotransferase(AST), lactate dehydrogenase(LDH), creatine kinase-MB(CK-MB), cardiac troponin I(cTnI), reactive oxygen species(ROS), interleukin(IL)-1β, IL-4, IL-6, IL-10, IL-18, and tumor necrosis factor-α(TNF-α) levels, as well as cardiac tissue catalase(CAT), glutathione(GSH), malondialdehyde(MDA), myeloperoxidase(MPO), superoxide dismutase(SOD), total antioxidant capacity(T-AOC) activities, and cytochrome C levels in mitochondria and cytoplasm. Hematoxylin-eosin, Masson, uranium acetate and lead citrate staining were used to observe morphological and mitochondrial ultrastructural changes in the cardiac tissues, and myocardial injury area and collagen volume fraction were calculated. Flow cytometry was applied to detect the relative content and M1/M2 polarization of cardiac macrophages. The mRNA expression levels of macrophage polarization markers [CD86, CD206, arginase 1(Arg-1), inducible nitric oxide synthase(iNOS)], CF markers [type Ⅰ collagen(Coll Ⅰ), Coll Ⅲ, α-smooth muscle actin(α-SMA)], and cytokines(IL-1β, IL-4, IL-6, IL-10, IL-18, TNF-α) in cardiac tissues were determined by quantitative real-time PCR. Western blot was used to detect the protein expression levels of Coll Ⅰ, Coll Ⅲ, α-SMA, Drp1, p-Drp1, voltage-dependent anion channel(VDAC), hexokinase 1(HK1), NOD-like receptor protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), caspase-1, cleaved-caspase-1, gasdermin D(GSDMD), cleaved N-terminal gasdermin D(GSDMD-N), IL-1β, IL-18, B-cell lymphoma-2(Bcl-2), B-cell lymphoma-xl(Bcl-xl), Bcl-2-associated death promoter(Bad), Bcl-2-associated X protein(Bax), apoptotic protease activating factor-1(Apaf-1), pro-caspase-3, cleaved-caspase-3, pro-caspase-9, cleaved-caspase-9, poly(ADP-ribose) polymerase-1(PARP-1), and cleaved-PARP-1 in cardiac tissues. The results showed that VA significantly improved cardiac function in mice with CF, reduced myocardial injury area and cardiac index, and decreased serum levels of AST, CK-MB, cTnI, LDH, ROS, IL-1β, IL-6, IL-18, and TNF-α. VA also lowered MDA and MPO levels, mRNA expressions of IL-1β, IL-6, IL-18, and TNF-α, and mRNA and protein expressions of Coll Ⅰ, Coll Ⅲ, and α-SMA in cardiac tissues, and increased serum levels of IL-4 and IL-10, cardiac tissue levels of CAT, GSH, SOD, and T-AOC, and mRNA expressions of IL-4 and IL-10. Additionally, VA ameliorated cardiac pathological damage, inhibited myocardial cell apoptosis, inflammatory infiltration, and collagen fiber deposition, reduced collagen volume fraction, and alleviated mitochondrial damage. VA decreased the ratio of F4/80~+CD86~+ M1 cells and the mRNA expressions of CD86 and iNOS in cardiac tissue, and increased the ratio of F4/80~+CD206~+ M2 cells and the mRNA expressions of CD206 and Arg-1. VA also reduced protein expressions of p-Drp1, VDAC, NLRP3, ASC, caspase-1, cleaved-caspase-1, GSDMD, GSDMD-N, IL-1β, IL-18, Bad, Bax, Apaf-1, cleaved-caspase-3, cleaved-caspase-9, cleaved-PARP-1, and cytoplasmic cytochrome C, and increased the expressions of HK1, Bcl-2, Bcl-xl, pro-caspase-3, pro-caspase-9 proteins, as well as the Bcl-2/Bax and Bcl-xl/Bad ratios and mitochondrial cytochrome C content. These results indicate that VA has a significant ameliorative effect on ISO-induced CF in mice, alleviates ISO-induced oxidative damage and inflammatory response, and its mechanism may be closely related to the inhibition of Drp1/HK1/NLRP3 and mitochondrial apoptosis signaling pathways, suppression of myocardial cell inflammatory infiltration and collagen fiber deposition, reduction of collagen volume fraction and CollⅠ, Coll Ⅲ, and α-SMA expressions, thus mitigating CF.
Animals ; Isoproterenol/adverse effects* ; Male ; Mice ; Signal Transduction/drug effects* ; Vanillic Acid/administration & dosage* ; Dynamins/genetics* ; Mice, Inbred C57BL ; Fibrosis/genetics* ; Apoptosis/drug effects* ; Mitochondria/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Myocardium/metabolism* ; Humans

Traditional Chinese medicine(TCM) is the pillar for the development of motherland medicine, and animal medicine has a long history of application in China, characterized by wide resources, strong activity, definite efficacy, and great benefits. It has significant potential and important status in the consumption market of raw materials of TCM. In the context of global climate change, farming system alterations, and low renewability, the depletion of wild medicinal animal resources has accelerated. Accordingly, the conservation and sustainable utilization of wild resources of animal medicinal materials has become a problem that garners increasing attention and urgently needs to be solved. This paper summarizes the current situation of domestic and foreign medicinal animal breeding and research progress in industrial application in recent years and points out the issues related to standardized breeding, germplasm selection and breeding, and quality evaluation standards for medicinal animals. Furthermore, this paper discusses standardized breeding, quality standards, resource protection and utilization, and the search for alternative resources for rare and endangered medicinal animals. It proposes that researchers should systematically carry out in-depth basic research on animal medicine, improve the breeding scale and level of medicinal animals, employ modern technology to enhance the quality standards of medicinal materials, and strengthen the research and development of alternative resources. This approach aims to effectively address the relationship between protection and utilization and make a significant contribution to the sustainable development of medicinal animal resources and the animal-based Chinese medicinal material industry.
Animals ; Breeding ; China ; Medicine, Chinese Traditional ; Conservation of Natural Resources

OBJECTIVE: To investigate the effect of Tongmai Hypoglycemic Capsule (THC) on myocardium injury in diabetic cardiomyopathy (DCM) rats. METHODS: A total of 24 Sprague Dawley rats were fed for 4 weeks with high-fat and high-sugar food and then injected with streptozotocin intraperitoneally for the establishment of the DCM model. In addition, 6 rats with normal diets were used as the control group. After modeling, 24 DCM rats were randomly divided into the model, L-THC, M-THC, and H-THC groups by computer generated random numbers, and 0, 0.16, 0.32, 0.64 g/kg of THC were adopted respectively by gavage, with 6 rats in each group. After 12 weeks of THC administration, echocardiography, histopathological staining, biochemical analysis, and Western blot were used to detect the changes in myocardial structure, oxidative stress (OS), biochemical indexes, protein expressions of myocardial fibrosis, and nuclear factor erythroid 2-related faactor 2 (Nrf2) element, respectively. RESULTS: Treatment with THC significantly decreased cardiac markers such as creatine kinase, lactate dehydrogenase, and creatine kinase-MB, etc., (P<0.01); enhanced cardiac function indicators including heart rate, ejection fraction, cardiac output, interventricular septal thickness at diastole, and others (P<0.05 or P<0.01); decreased levels of biochemical indicators such as fasting blood glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol, aspartate transaminase, (P<0.05 or P<0.01); and decreased the levels of myocardial fibrosis markers α-smooth muscle actin (α-SMA), and collagen I (Col-1) protein (P<0.01), improved myocardial morphology and the status of myocardial interstitial fibrosis. THC significantly reduced malondialdehyde levels in model rats (P<0.01), increased levels of catalase, superoxide dismutase, and glutathione (P<0.01), and significantly increased the expression of Nrf2, NAD(P)H:quinone oxidoreductase 1, heme oxygenase-1, and superoxide dismutase 2 proteins in the left ventricle of rats (P<0.01). CONCLUSION THC activates the Nrf2 signaling pathway and plays a protective role in reducing OS injury and cardiac fibrosis in DCM rats.
Animals ; Diabetic Cardiomyopathies/physiopathology* ; Oxidative Stress/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Rats, Sprague-Dawley ; Myocardium/metabolism* ; Fibrosis ; Male ; Capsules ; Hypoglycemic Agents/therapeutic use* ; NF-E2-Related Factor 2/metabolism* ; Rats ; Diabetes Mellitus, Experimental/drug therapy*

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.

Background: Salvia miltiorrhiza Bunge, commonly known as “Danshen” in China due to the distinctive red color of its roots, is one of the most widely used traditional Chinese medicines. It is cultivated in various regions across China, and environmental differences among these regions can affect the secondary metabolites of plants, thereby influencing the quality of S. miltiorrhiza. In recent years, increasing demand for S. miltiorrhiza has exacerbated the problem of origin fraud. Therefore, ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry. Objective: The red coloration of S. miltiorrhiza is closely associated with the content of its primary active compounds, particularly tanshinones. Therefore, both its internal chemical composition and external color characteristics serve as key indicators for quality assessment. This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S. miltiorrhiza. Methods: Spectral data reflecting the internal chemical properties of S. miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion. These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification. Results: The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72% by integrating image color and short-wave infrared spectral data. Conclusion: This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S. miltiorrhiza.

Aim To perform high-throughput screen-ing of immunomodulatory traditional Chinese medicine(TCMs)based on an in vitro B cell differentiation-antibody secretion model,identifying active herbal candidates with immune-enhancing properties to pro-vide novel therapeutic options and theoretical support for influenza virus treatment in immunocompromised in-dividuals.Methods B cells were stimulated with dif-ferent concentrations of cytosine-phosphate-guanine oli-godeoxynucleotide 2006(CpG)and nterleukin-2(IL-2)to promote proliferation,differentiation,and anti-body secretion,and the effects of varying concentra-tions of the solvent DMSO were also evaluated.The op-timal conditions for the B cell differentiation-anti-body secretion model were determined based on the se-cretion levels of three antibody isotypes.The feasibility of the model was further validated using rapamycin,a known B cell function inhibitor.On this basis,a high-throughput screening platform for immunomodulatory a-gents was optimized and established.Subsequently,the immune-enhancing activity of 465 polarity extract from TCMs was evaluated.Results The optimal con-ditions for the model were determined as 2 mg·L-1 CpG,1.67 × 106 nkat·L-1 IL-2,and DMSO with a volume fraction of 0.1％.Rapamycin effectively inhib-ited B cell differentiation into plasmablast and signifi-cantly reduced antibody production,indicating the reli-ability of the model.Multiple rounds of screening re-vealed that the dichloromethane extract of licorice,the dichloromethane extract of Vinegar-processed Curcumae Rhizoma,the cyclohexane extract of Honey-prepared Radix Asteris,and the aqueous extract of Siphonostegia chinensis Benth were identified to significantly promote both B cell proliferation and differentiation and anti-body secretion at a concentration of 600 μg·L-1.Conclusion This study successfully optimizes an in vitro B cell differentiation-antibody secretion model and identifies several TCM extracts,including licorice,with potential immune-enhancing activity.

Aim To perform high-throughput screen-ing of immunomodulatory traditional Chinese medicine(TCMs)based on an in vitro B cell differentiation-antibody secretion model,identifying active herbal candidates with immune-enhancing properties to pro-vide novel therapeutic options and theoretical support for influenza virus treatment in immunocompromised in-dividuals.Methods B cells were stimulated with dif-ferent concentrations of cytosine-phosphate-guanine oli-godeoxynucleotide 2006(CpG)and nterleukin-2(IL-2)to promote proliferation,differentiation,and anti-body secretion,and the effects of varying concentra-tions of the solvent DMSO were also evaluated.The op-timal conditions for the B cell differentiation-anti-body secretion model were determined based on the se-cretion levels of three antibody isotypes.The feasibility of the model was further validated using rapamycin,a known B cell function inhibitor.On this basis,a high-throughput screening platform for immunomodulatory a-gents was optimized and established.Subsequently,the immune-enhancing activity of 465 polarity extract from TCMs was evaluated.Results The optimal con-ditions for the model were determined as 2 mg·L-1 CpG,1.67 × 106 nkat·L-1 IL-2,and DMSO with a volume fraction of 0.1％.Rapamycin effectively inhib-ited B cell differentiation into plasmablast and signifi-cantly reduced antibody production,indicating the reli-ability of the model.Multiple rounds of screening re-vealed that the dichloromethane extract of licorice,the dichloromethane extract of Vinegar-processed Curcumae Rhizoma,the cyclohexane extract of Honey-prepared Radix Asteris,and the aqueous extract of Siphonostegia chinensis Benth were identified to significantly promote both B cell proliferation and differentiation and anti-body secretion at a concentration of 600 μg·L-1.Conclusion This study successfully optimizes an in vitro B cell differentiation-antibody secretion model and identifies several TCM extracts,including licorice,with potential immune-enhancing activity.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.