BACKGROUND:Neuregulin 1 has been shown to be characterized in cell proliferation,differentiation,and vascular growth.Human amniotic mesenchymal stem cells are important seed cells in the field of tissue engineering,and have been shown to be involved in tissue repair and regeneration. OBJECTIVE:To construct human amniotic mesenchymal stem cells overexpressing neuregulin 1 and investigate their proliferation and migration abilities,as well as their effects on wound healing. METHODS:(1)Human amniotic mesenchymal stem cells were in vitro isolated and cultured and identified.(2)A lentivirus overexpressing neuregulin 1 was constructed.Human amniotic mesenchymal stem cells were divided into empty group,neuregulin 1 group,and control group,and transfected with empty lentivirus and lentivirus overexpressing neuregulin 1,or not transfected,respectively.(3)Edu assay was used to detect the proliferation ability of the cells of each group,and Transwell assay was used to detect the migration ability of the cells.(4)The C57 BL/6 mouse trauma models were constructed and randomly divided into control group,empty group,neuregulin 1 group,with 8 mice in each group.Human amniotic mesenchymal stem cells transfected with empty lentivirus or lentivirus overexpressing neuregulin-1 were uniformly injected with 1 mL at multiple local wound sites.The control group was injected with an equal amount of saline.(5)The healing of the trauma was observed at 1,7,and 14 days after model establishment.Histological changes of the healing of the trauma were observed by hematoxylin-eosin staining.The expression of CD31 on the trauma was observed by immunohistochemistry. RESULTS AND CONCLUSION:(1)Human amniotic mesenchymal stem cells overexpressing neuregulin-1 were successfully constructed.The mRNA and protein expression of intracellular neuregulin 1 was significantly up-regulated compared with the empty group(P<0.05).(2)The overexpression of neuregulin 1 promoted the migratory ability(P<0.01)and proliferative ability of human amniotic mesenchymal stem cells(P<0.05).(3)Human amniotic mesenchymal stem cells overexpressing neuregulin 1 promoted wound healing in mice(P<0.05)and wound angiogenesis(P<0.05).The results showed that overexpression of neuregulin 1 resulted in an increase in the proliferative and migratory capacities of human amniotic mesenchymal stem cells,significantly promoting wound healing and angiogenesis.

The genus jujube(Ziziphus jujuba Mill.)within the Rhamnaceae family encompasses numerous varieties,such as Ziziphus jujuba Mill.var.jujuba,Ziziphus jujuba var.inermis,and var.spinosa,etc.Among these,the jujube fructus has the most abundant cultivated variants across the country,including Ziziphus jujuba cv.Hamidazao and Ziziphus jujuba cv.Huanghetanzao.Jujube plants are rich in variety and are used for both medicinal and food purposes.Polysaccharides,one of the main active ingredients of jujube,are important medicinal components that contribute to its efficacy.Jujube polysaccharides have been found to promote hematopoiesis,exhibit antioxidant and anti-tumor activities,repair liver damage,regulate the immune system,and provide anti-inflammatory effects.By comprehensively summarizing and analyzing the literature on jujube polysaccharides from different varieties and origins,this paper reviews the potential mechanisms of action of jujube polysaccharides in exerting biological activities.It also summarizes the primary structural features,such as relative molecular mass,monosaccharide composition,glycosidic linkage,and the substituent modifications of jujube polysaccharides by sulfation,phosphorylation,carboxymethylation,selenization,and acetylation.This review aims to provide a reference for the research and development of jujube in the fields of innovative polysaccharide drugs and functional foods.

Objective: To investigate the mechanism of electroacupuncture (EA) for treating depression and to explore the role of brevican in the medial prefrontal cortex (mPFC) in modulating stress susceptibility and the antidepressant effects of EA in rats. Methods: Twenty-four Sprague–Dawley (SD) rats were equally divided into three groups: green fluorescent protein (GFP) + control, GFP + chronic unpredicted mild stress (CUMS), and short-hairpin RNA targeting on brevican (shBcan) + CUMS. Another 24 SD rats were equally divided into CUMS + GFP, CUMS + GFP + EA, and CUMS + shBcan + EA groups. Behavioral tests were conducted to assess depression-like behavior. Western blot analysis was used to evaluate the expression of brevican, aggrecan, GLuA1, and PSD95 in mPFC subregions. Results: Behavioral parameter evaluation show that rats in the shBcan + CUMS group exhibited a significantly reduced sucrose preference (P = .0002) and increased immobility time (P = .0011) compared to those in rats in the GFP + CUMS group. Western blotting showed that brevican expression was significantly downregulated in the PrL of the shBcan + CUMS group compared with that in the GFP + CUMS group (P = .0192). Furthermore, compared to the CUMS + GFP + EA group, the CUMS + shBcan + EA group exhibited a significantly decreased sucrose preference (P = .0334), increased immobility time (P = .0465), and increased latency to food (P = .0261). In the CUMS + shBcan + EA group, the EA-induced brevican and PSD95 overexpression was reversed, compared with that in the CUMS + GFP + EA group (P = .0454 and P = .0198, respectively). Conclusion EA exerts its antidepressant effects through the modulation of brevican expression in rats. Our findings highlight the important role for brevican in stress susceptibility, which could be a potential target for treating depression.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To investigate the current status of off-label drug use(OLDU)management in tertiary hospitals of Guizhou province and to provide baseline evidence for developing a unified administration regulation for OLDU in Guizhou province.Methods In line with the relevant policies and regulations,a questionnaire including basic information about the person filling out the form,basic information about the hospitals,and information about OLDU was developed.The questionnaire was sent to 84 tertiary hospitals in Guizhou province through the Wenjuanxing.Results A total of 84 questionnaires were distributed and recovered,with a response rate of 100.00%.Of the 84 hospitals,77 had OLDU,of which 68(88.31%)had established a management system for OLDU.Among the 77 hospitals with OLDU,65(84.42%),42(54.55%),58(75.32%),36(46.75%),15(19.48%),and 21(27.27%)hospitals respectively,required approval from the Committee on Drug Administration and Pharmacotherapy before OLDU,restricted the qualifications of doctors prescribing OLDU,required informed consent from patients or their families before OLDU,recorded the matters and reasons in the medical records of patients treated with OLDU,followed up patients in their files and evaluated the reasonableness of the OLDU,and carried out special reviews for OLDU.Only 30(38.96%)hospitals have set up a catalogue of OLDUs,and 58(75.32%)hospitals have urgent needs to set up a unified provincial catalogue of OLDUs.Conclusion The pharmacy administration level of OLDU in tertiary hospitals of Guizhou province is relatively low,so there is an urgent need to establish a unified OLDU management system and medication catalog.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To investigate the efficacy of Fuzheng Hejie Prescription(composed of Scutellariae Radix,Lonicerae Japonicae Flos,Agastachis Herba,Bupleuri Radix,Atractylodis Rhizoma,Glycyrrhizae Radix et Rhizoma,etc.)in the treatment of respiratory viral infections in children and to observe its effect on inflammatory factors and immune function.Methods A total of 203 children with respiratory viral infection of H1N1 virus were randomly divided into 101 cases in the observation group and 102 cases in the control group.Both groups were given the routine treatment for subsiding fever,maintaining water-electrolyte balance,and ensuring enough sleep.And additionally,the control group was given Ribavirin Granules and Ibuprofen Granules,and the observation group was given Fuzheng Hejie Prescription based on the treatment for the control group.The course of treatment covered 7 days.The changes of traditional Chinese medicine(TCM)syndrome scores and the levels of immunological indicators and inflammatory factors in the two groups were observed before and after the treatment.Moreover,the clinical efficacy,symptom resolution time and the incidence of adverse reactions were compared between the two groups of children.Results(1)In the course of the trial,one case fell off in the observation group and 2 cases fell off in the control group,and eventually 100 children in each group were included in the trial.(2)After 7 days of treatment,the total effective rate of the observation group was 93.00%(93/100),and that of the control group was 88.00%(88/100),and the intergroup comparison showed that the therapeutic effect of the observation group was superior to that of the control group,but the difference was not statistically significant(χ2= 1.454,P = 0.228).(3)After treatment,the scores of primary symptoms and secondary symptoms as well as the total TCM syndrome scores in the two groups were decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the time for the resolution of clinical symptoms such as fever,cough,expectoration and sore throat in the observation group was significantly shorter than that in the control group(P＜0.01).(5)After treatment,the levels of immunological indicators of T lymphocyte subset CD3+ and CD4+ in the two groups were increased compared with those before treatment(P＜0.05),and the levels of CD8+ and B cells were decreased compared with those before treatment(P＜0.05).The intergroup comparison showed that the increase in the levels of CD3+ and CD4+ as well as the decrease in the levels of CD8+ and B cells of the observation group was significantly superior to that of the control group(P＜0.01).(6)After treatment,the levels of inflammatory factors of serum amyloid A(SAA),C-reactive protein(CRP),serum tumor necrosis factor alpha(TNF-α),soluble interleukin 2 receptor(SIL-2R),and interleukin 6(IL-6)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the levels of interleukin 2(IL-2)and interferon γ(IFN-γ)ls were all significantly increased compared with those before treatment(P＜0.05).The intergroup comparison showed that the decrease of serum SAA,CRP,TNF-α,SIL-2R,and IL-6 levels and the increase of serum IL-2 and IFN-γ levels in the observation group were significantly superior to those in the control group(P＜0.01).(7)The incidence of adverse reactions in the observation group was 2.00%(2/100),which was significantly lower than that of 8.00%(8/100)in the control group,but the difference was not statistically significant(χ2 = 3.789,P = 0.052).Conclusion Fuzheng Hejie Prescription exerts certain effect in treating children with respiratory viral infection of H1N1 virus,which can effectively decrease children's TCM syndrome scores,regulate the inflammatory response,improve the immune function,accelerate the relief of clinical symptoms and shorten the course of the disease.

BACKGROUND:Increasing evidence suggests that N6-methyladenosine(m6A)regulators are closely associated with osteoarthritis and are considered to be a new direction in the prevention and treatment of osteoarthritis,but their specific mechanism of action is unknown. OBJECTIVE:To conduct a bioinformatics analysis of the osteoarthritis gene microarray dataset in order to explore the role of m6A in osteoarthritis and analyze the pathogenesis of osteoarthritis. METHODS:The m6A regulators associated with osteoarthritis and their expression were first extracted from the GSE1919 dataset in the GEO database using R software,and then the results were analyzed by gene difference analysis and GO and KEGG enrichment analyses.Subsequently,the results of protein-protein interaction network topology analysis and machine learning results were intersected to obtain the m6A Hub regulators,which were validated by in vitro cellular experiments. RESULTS AND CONCLUSION:A total of 16 osteoarthritis-related m6A regulators were extracted and 11 m6A differential regulators,including ZC3H13,YTHDC1,YTHDF3 and HNRNPC,were obtained by differential analysis.GO enrichment analysis showed that osteoarthritis-related m6A differential regulators played a role in the biological processes such as mRNA transport,RNA catabolism,and regulation of insulin-like growth factor receptor signaling pathway.(3)KEGG enrichment analysis showed that the differential regulators were mainly involved in the p53,interleukin-17 and AMPK signaling pathways.The combined protein-protein interaction network topology analysis and machine learning results obtained the m6A Hub regulator-YTHDC1.(5)The results of in vitro cellular experiments showed that there was a significant difference in the expression of m6A key regulator between the control and experimental groups(P<0.05).To conclude,YTHDC1 is closely related to the development of osteoarthritis,which is expected to be a molecular target of m6A for the treatment of osteoarthritis.

BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION:(1)12 548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.000 14).(5)In vitro chondrocyte assay showed that type Ⅱ collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes(P<0.05),while in osteoarthritis ferroptosis,mast cells activated was closely correlated with dendritic cells(r=0.99);KLF2 was closely correlated with natural killer cells(r=-1,P=0.017)and T cells follicular helper(r=-1,P=0.017).(6)The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2.