Objective To clone the full-length sequence,subcellular localization,and analyze the expression patterns of the β-caryophyllene synthase gene(AaCPS)of Artemisia argyi,in order to lay a foundation for the gene function analysis of sesquiterpene biosynthesis pathway in A.argyi.Methods The genes annotated as CPS were selected from the transcriptome data of A.argyi.The full-length cDNA sequence of the target gene was obtained by PCR,and the coding region was analyzed using bioinformatics.A prokaryotic expression vector was constructed and transformed into Escherichia coli competent cells to express recombinant protein.A green fluorescent protein(GFP)fused expression vector was constructed,and the subcellular localization of AaCPS was observed using Agrobacterium tumefaciens transient expression method in tobacco.Real-time quantitative PCR(qRT-PCR)was used to analyze the expression patterns of the AaCPS at different harvest times(April,May,June,July).Determination of β-caryophyllene by HS-SPME-GC-MS and correlation analysis.Results The AaCPS gene was cloned from A.argyi.The full-length open reading frame(ORF)was 1647 bp,encoding 548 amino acids.The molecular weight was 63 667 Da with a theoretical pI of 5.94.AaCPS contained conserved Terpen_synth and Terpen_synth_C domains.Phylogenetic analysis indicated that AaCPS was closely related to the CPS protein of Artemisia annua.SDS-PAGE showed the presence of target protein bands between 75 000-120 000 Da(containing 28 132 Da of the labeled protein),indicating successful expression of the AaCPS protein.The protein was found to be localized in the cytoplasm.As shown by qRT-PCR,the expression of AaCPS gene showed an upward trend,reaching the highest in July.The results of HS-SPME-GC-MS showed that the content of β-caryophyllene increased gradually from April to the highest in June.It is consistent with the trend of AaCPS gene expression from April to June.Conclusion Through the cloning and analysis of AaCPS gene,a foundation has been laid for further study of the functional identification of the AaCPS gene in the sesquiterpenes biosynthesis pathway in A.argyi.

Objective To observe the clinical efficacy of Shuangye San(the prescription mainly composed of Mori Folium and Nelumbinis Folium)in the treatment of type 2 diabetes mellitus(T2DM)complicated with non-alcoholic fatty liver disease(NAFLD)of spleen deficiency and phlegm stasis type.Methods A total of 80 patients with T2DM complicated with NAFLD of spleen deficiency and phlegm stasis type were randomly divided into a treatment group and a control group,with 40 cases in each group.The control group was treated with conventional western medicine for lowering blood glucose and lipid,protecting liver and lowering enzymes.The treatment group was treated with the granules of Shuangye San orally on the basis of treatment for the control group.The course of treatment lasted for three months.The changes of traditional Chinese medicine(TCM)syndrome scores,homeostasis model assessment of insulin resistance(HOMA-IR),fasting insulin(FINS),fasting blood glucose(FBG),2-hour postprandial blood glucose(2hPG),glycosylated hemoglobin(HbA1C),total cholesterol(TC),triglyceride(TG),low-density lipoprotein cholesterol(LDL-C),high-density lipoprotein cholesterol(HDL-C),liver function indicators and B-ultrasound grading of fatty liver in the two groups were observed before and after treatment.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After three months of treatment,the total effective rate of the treatment group was 85.00％(34/40),and that of the control group was 70.00％(28/40).The intergroup comparison(tested by chi-square test)showed that the efficacy of the treatment group was significantly superior to that of the control group(P＜0.01).(2)After treatment,the scores of TCM symptoms of obese physique,heaviness and weakness in the limbs,shortness of breath and unwilling to talk,tightness and stabbing pain in the chest,abdominal distension and poor appetite in the two groups were decreased compared with those before treatment(P＜0.05),and the decrease of TCM syndrome scores in the treatment group was significantly superior to that in the control group(P＜0.05).(3)After treatment,the levels of glucose and lipid metabolism indicators of FINS,HOMA-IR,FBG,2hPG,HbA1C,TC,TG and LDL-C in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the level of HDL-C was significantly increased compared with that before treatment(P＜0.05).The decrease of FINS,HOMA-IR,FBG,2hPG,HbA1C,TC,TG and LDL-C levels and the increase of HDL-C levels in the treatment group were significantly superior to those in the control group(P＜0.05).(4)After treatment,the levels of liver function indicators of alanine aminotransferase(ALT),aspartate aminotransferase(AST)and gamma-glutamyl transpeptidase(GGT)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease of liver function indicators in the treatment group was significantly superior to that in the control group(P＜0.05).(5)After treatment,the B-ultrasound grading of fatty liver of the two groups was significantly improved compared with that before treatment(P＜0.05),and the improvement of fatty liver B-ultrasound grading in the treatment group was significantly superior to that in the control group,and the difference was statistically significant(P＜0.05).(6)During the treatment,there were no adverse reactions such as impairment of liver and kidney function and abnormalities in routine blood,urine and stool test in the two groups.Conclusion Shuangye San exerts certain effect in the treatment of T2DM complicated with NAFLD of spleen deficiency and phlegm stasis type.It can alleviate the clinical symptoms of patients,correct the disorder of glucose and lipid metabolism,and improve liver function and fatty liver B-ultrasound grading.

Objective To analyze the molecular markers of various nuclei in the human basal ganglia and the differentially expressed genes(DEGs)among different nuclei,gender,and Parkinson's disease(PD),followed by the biological function annotations of the DEGs.Methods Forty-five specimens of basal ganglia from 10 human postmortem brains were divided into control and PD groups,and the control group was further categorized into female and male groups.RNA from each sample was extracted for high-throughput transcriptome sequencing.Bioinformatic analysis was conducted to identify molecular markers of each nuclei in the control group,nuclei-specific,gender-specific,and PD-specific DEGs,followed by gene enrichment analysis and functional annotation.Results Sequencing analysis revealed top DEGs such as DRD1,FOXG1,and FAM183A in the caudate;SLC6A3,EN1,SLC18A2,and TH in the substantia nigra;MEPE and FGF10 in the globus pallidus;and SLC17A6,PMCH,and SHOX2 in the subthalamic nucleus.In them,putamen showed some overlapping DEGs with caudate,such as DRD1 and FOXG1.A significant number of DEGs were identified among different nuclei in the control group,with the highest number between caudate and globus pallidus(9321),followed by putamen and globus pallidus(6341),caudate and substantia nigra(6054),and substantia nigra and subthalamic nucleus(44).Gene enrichment analysis showed that downregulated DEGs between caudate and globus pallidus were significantly enriched in processes like myelination of neurons and cell migration.Upregulated DEGs between putamen and globus pallidus were enriched processes like chemical synaptic transmission and regulation of membrane potential,while downregulated DEGs were enriched in myelination and cell adhesion.Upregulated DEGs between caudate and substantia nigra were enriched in processes like chemical synaptic transmission and axonal conduction,while downregulated DEGs were enriched in myelination of neurons.Totally 468,548,1402,333,and 341 gender-specific upregulated DEGs and 756,988,2532,444,and 1372 downregulated DEGs were identified in caudate,putamen,substantia nigra,globus pallidus,and subthalamus nucleus.Gene enrichment analysis revealed upregulated DEGs mostly enriched in pathways related to immune response and downregulated DEGs in chemical synaptic transmission.At last,709,852,276,507,and 416 PD-specific upregulated DEGs and 830,2014,1218,836,and 1730 downregulated DEGs were identified in caudate,putamen,substantia nigra,globus pallidus,and subthalamus nucleus.Gene enrichment analysis revealed upregulated DEGs mostly enriched in apoptotic regulation and downregulated DEGs in chemical synaptic transmission and action potential regulation.Conclusion We identified and analysed the molecular markers of different human basal ganglia nuclei,as well as DEGs among different nuclei,different gender,and between control and PD.

Spinal cord injury (SCI) is a devastating traumatic disease seriously impairing the quality of life in patients. Expectations to allow the hopeless central nervous system to repair itself after injury are unfeasible. Developing new approaches to regenerate the central nervous system is still the priority. Exosomes derived from mesenchymal stem cells (MSC-Exo) have been proven to robustly quench the inflammatory response or oxidative stress and curb neuronal apoptosis and autophagy following SCI, which are the key processes to rescue damaged spinal cord neurons and restore their functions. Nonetheless, MSC-Exo in SCI received scant attention. In this review, we reviewed our previous work and other studies to summarize the roles of MSC-Exo in SCI and its underlying mechanisms. Furthermore, we also focus on the application of exosomes as drug carrier in SCI. In particular, it combs the advantages of exosomes as a drug carrier for SCI, imaging advantages, drug types, loading methods, etc., which provides the latest progress for exosomes in the treatment of SCI, especially drug carrier.

Objective To investigate the effect and mechanism of andrographolide(AG)on lipopolysaccharide(LPS)-induced ferroptosis in renal tubular epithelial cells(HK-2 cells).Methods HK-2 cells were treated with LPS to simulate the in vitro HK-2 injury model of sepsis.The cells were further treated with AG of 5,10,20,40 μmol/L and randomly divided into control group,LPS group,LPS+dimethyl sulfoxide group(DMSO group),and AG group.Cell viability was detected by the CCK-8 method,and the optimal concentrations of LPS and AG were screened.Cell morphological change,the levels of kidney injury markers,including neutrophil gelatinase-associated lipocalin(NGAL),kidney injury molecule-1(KIM-1),malondialdehyde(MDA),glutathione(GSH)and reactive oxygen species(ROS),as well as the expression levels of ferroptosis regulatory proteins such as solute carrier family 7 member 11(SLC7A11),glutathione peroxidase 4(GPX4)and ferritin in each group were compared,and the pro-tective effect of AG treatment on the cells was evaluated.Results Compared with the control group,the cell viabi-lity and GSH content decreased significantly in HK-2 cells treated with 10 μg/mL LPS;cell shrinkage and adhesion ability were poor;the contents of oxidative products MDA and ROS,as well as the levels of kidney injury markers NGAL and KIM-1 increased significantly,while expression levels of SLC7A11 and GPX4 protein decreased;ferritin expression level increased;differences were all statistically significant(all P＜0.05).Compared with LPS group,the cell viability,GSH content,as well as protein expression levels of SLC7A11 and GPX4 increased significantly after AG intervention,while ferritin expression level decreased,differences were all significant(all P＜0.05).MDA content,ROS fluorescence intensity,and the levels of kidney injury markers NGAL and KIM-1 decreased sig-nificantly,difference were all significant(all P＜0.05).Conclusion AG has a protective effect on LPS-induced HK-2 cell injury,possibly by activating SLC7A11/GPX4 pathway,reducing oxidative stress,up-regulating antioxi-dant enzyme activity,and alleviating ferroptosis.

Protein phosphorylation modification is an important mechanism of physiological regulation that is closely related to protein biological functions. In particular, protein kinases are responsible for catalyzing the phosphorylation process of proteins, and phosphatases are responsible for catalyzing the dephosphorylation process of phosphorylation-modified proteins, which together mediate the achievement of dynamic and reversible phosphorylation modifications of proteins. Abnormal phosphorylation levels of proteins contribute to the development of many diseases, such as cancer, neurodegenerative diseases, and chronic diseases. Therefore, rational design of small molecules to regulate protein phosphorylation is an important approach for disease treatment. Based on the mechanism of protein phosphorylation regulation, small molecule drug design strategies can be classified into three types, protein kinase modulators, phosphatase modulators, and bifunctional molecules with proximity-mediated mechanism. This review emphasizes the above three small molecule design strategies for targeting protein phosphorylation regulation, including molecular design ideas, research progress and current challenges, and provides an outlook on small molecule modulators targeting protein phosphorylation modification.

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: 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.