ObjectiveTo investigate the mechanism by which Luoshi Neiyi prescription treats endometriosis （EMs） through the Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB） signaling pathway. MethodsAnimal experiments were conducted with 50 female SD rats， which were randomized into a sham operation group （10 rats） and a modeling group （40 rats）. An autologous endometrial transplantation method was used for the modeling of EMs. The 36 successfully modeled rats were randomly allocated into four groups （n=9 each）： The model group， the low-dose （7.87 g·kg^-1） Luoshi Neiyi prescription group， the high-dose （15.74 g·kg^-1） Luoshi Neiyi prescription group， and the dienogest （0.20 mg·kg^-1） group. The physiological status and ectopic lesion volumes of rats in each group were observed. Hematoxylin-eosin （HE） staining was used to observe the morphology of the eutopic endometrial tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the levels of inflammatory factors including interleukin-1β （IL-1β） and prostaglandin E₂ （PGE₂） in the serum of EMs rats. Immunohistochemistry was used to detect the expression of matrix metalloproteinase-9 （MMP-9） and vascular endothelial growth factor A （VEGFA） in the eutopic endometrial tissue. Western blot was adopted to determine the protein levels of TLR4， myeloid differentiation factor 88 （MyD88）， phosphorylated nuclear factor-κB （p-NF-κB）/NF-κB， MMP-9， and VEGFA in the eutopic endometrial tissue. In the cell experiments， the cell-counting kit-8 （CCK-8） assay was employed to screen the optimal concentration of Luoshi Neiyi prescription-containing serum， and a scratch assay was performed to assess the migration ability of iheESCs cells. Interventions with Luoshi Neiyi prescription-containing serum， resatorvid （TAK-242， a TLR4 inhibitor）， and lipopolysaccharides （LPS， a TLR4 agonist） were conducted， and Western blot was used to detect the expression of proteins related to the TLR4/NF-κB signaling pathway. ResultsThis experiment successfully replicated 36 EMs rat models. Compared with the sham operation group， the model group exhibited visible ectopic lesions on the abdominal wall and an increase in the writhing response score （P<0.01）. Furthermore， HE staining revealed the model group exhibited a thickened eutopic epithelium， stromal cell disarrangement， and evident infiltration of inflammatory cells. In addition， the model group showed elevated serum levels of IL-1β and PGE₂ （P<0.05， P<0.01）， increased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.01）， and up-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. Compared with the model group， all treatment groups exhibited a reduction in the ectopic lesion volume （P<0.01）. Furthermore， the writhing response scores were decreased in the high-dose Luoshi Neiyi prescription group and the dienogest group （P<0.05， P<0.01）. The pathological state of the ectopic endometrial tissue was alleviated to varying degrees in the treatment groups. Low-dose Luoshi Neiyi prescription reduced serum PGE₂ levels， and high-dose Luoshi Neiyi prescription and dienogest decreased serum IL-1β and PGE₂ levels in EMs rats （P<0.05， P<0.01）. The treatment groups showed decreased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01） and down-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. The cell experiments showed that 5%， 10%， and 20% Luoshi Neiyi prescription-containing sera significantly reduced the viability and inhibited the migration of iheESCs. Compared with the control group， 5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups and the TAK-242 group showed reduced protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA （P<0.01）. Compared with the control group， the LPS group showed increased expression of the above proteins （P<0.01）. Compared with the LPS group， the LPS+5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups showed reduced expression of the above proteins （P<0.05， P<0.01）. ConclusionLuoshi Neiyi prescription may modulate the TLR4/NF-κB signaling pathway to reduce the inflammatory response and histopathological damage in the eutopic endometrium and suppress the adhesive， invasive， and angiogenic biological processes in the ectopic endometrial tissue， thereby exerting its therapeutic effect on EMs.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

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.

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.

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.