BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.

BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.

Osteoporosis is a common bone metabolic disease， which is mainly characterized by the decrease in the number of bone trabeculae and the destruction of bone tissue microstructure， leading to increased bone fragility and fracture risks. This disease is common in postmenopausal women， elderly men， diabetes patients， and obese people. Due to the lack of awareness to prevent bone losses and the limitations of bone mass measurement methods， osteoporosis is only concerned when there are serious complications， which imposes a heavy burden on both patients and medical resources. Oxidative stress refers to the excessive production of highly active molecules such as reactive oxygen species and reactive nitrogen in the body subjected to harmful stimuli， leading to the imbalance between the oxidative and antioxidant systems and causing oxidative damage. Studies have shown that oxidative stress can increase the generation and activity of osteoclasts and inhibit the differentiation of osteoblasts， thus playing a role in the occurrence and development of osteoporosis. Traditional Chinese medicine （TCM） is considered an effective antioxidant that can alleviate oxidative stress-induced osteoporosis by regulating a variety of signaling pathways. Studies have shown that TCM can alleviate oxidative stress and promote bone angiogenesis and osteogenesis by regulating the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）， nuclear factor-kappa B， and nuclear factor erythroid 2-related factor （Nrf2） signaling pathways. TCM alleviates oxidative stress and promotes osteogenesis by regulating the Nrf2， PI3K/Akt/mammalian target of rapamycin， and secreted glycoprotein Wnt/β-catenin signaling pathways. In addition， TCM regulates NF-κB， mitogen-activated protein kinase， and receptor activator of nuclear factor kappa B （RANK）/RANK ligand/osteoprotegerin signaling pathway to alleviate excessive bone resorption induced by oxidative stress. This paper systematically summarizes the literature on the prevention and treatment of osteoporosis by TCM or its active ingredients via the above-mentioned signaling pathways to reduce oxidative stress in recent years. It briefs the possible molecular mechanisms of oxidative stress regulation-related signaling pathways to cause osteoporosis. In addition， this paper discusses the effects and mechanisms of TCM on bone angiogenesis， osteogenesis， and bone resorption by reducing oxidative stress through the regulation of related signaling pathways， aiming to provide a theoretical basis for the research and clinical treatment of osteoporosis.

BACKGROUND:Gegenmaqi prescription has a good effect on periarthritis of shoulder combined with type 2 diabetes and has a good application prospect,but the specific mechanism is not clear.OBJECTIVE:To explore the action mechanism of Gegenmaqi prescription on periarthritis of shoulder and type 2 diabetes by network pharmacology,molecular docking,and molecular dynamics.METHODS:The active components and protein targets of Gegenmaqi prescription were retrieved from the Traditional Chinese Medicine System Pharmacology database and analysis platform,referred to as TCMSP jointly established by the Shanghai Institute of Materia Medica,Chinese Academy of Sciences and the Institute of Chinese Materia Medica,and China Academy of Chinese Medical Sciences in 2013.Genecards created by Professor Doron Lancet's team at the Weizmann Institute of Science in Israel in 1997,Drugbank created by scientists at the University of Alberta in Canada in 2006,and the OMIM database established by Dr.Victor A.McKusick's team at Johns Hopkins University in the United States in 1966 were used to search the disease protein targets of periarthritis of shoulder and type 2 diabetes,and the intersection targets were obtained based on the WeChat online tool.The protein-protein interaction network was constructed based on the STRING database created in 2000 by Peer Bork's team at the European Bioinformatics Institute(EMBL),and the protein-protein interaction relationship was analyzed.The core targets were screened according to the degree value.The intersection targets were subjected to GO and KEGG enrichment analyses.Finally,molecular docking and molecular dynamics simulation were used to verify the binding of key components to key targets.RESULTS AND CONCLUSION:(1)One hundred and forty-two active ingredients of Gegenmaqi prescription were obtained,including 65 intersections between component targets and disease targets,5 key active ingredients(β-sitosterol,stigmasterol,kaempferol,quercetin,and formononetin),and 5 key targets(AKT1,tumor necrosis factor,interleukin-10,JUN,and TP53).(2)GO function enrichment included 508 items,390 biological process items,77 molecular function items and 41 cell component items.KEGG pathway analysis showed 146 pathways,mainly involving advanced glycation end products receptor signaling pathway,lipid and atherosclerosis signaling pathway,tumor necrosis factor signaling pathway,and interleukin-17 signaling pathway.(3)Molecular docking showed that the key components and key targets had good binding activity.Molecular dynamics simulation showed that β-sitosterol had stable interactions with AKT1,tumor necrosis factor and interleukin 10.(4)Gegenmaqi prescription has been comprehensively studied,and the material basis of its pharmacological effect has been primarily clarified.It is predicted that Gegenmaqi prescription can treat periarthritis of shoulder combined with type 2 diabetes through multi-components,multi-targets,and multi-pathways to exert anti-inflammatory and regulate insulin secretion.

BACKGROUND:Gegenmaqi prescription has a good effect on periarthritis of shoulder combined with type 2 diabetes and has a good application prospect,but the specific mechanism is not clear.OBJECTIVE:To explore the action mechanism of Gegenmaqi prescription on periarthritis of shoulder and type 2 diabetes by network pharmacology,molecular docking,and molecular dynamics.METHODS:The active components and protein targets of Gegenmaqi prescription were retrieved from the Traditional Chinese Medicine System Pharmacology database and analysis platform,referred to as TCMSP jointly established by the Shanghai Institute of Materia Medica,Chinese Academy of Sciences and the Institute of Chinese Materia Medica,and China Academy of Chinese Medical Sciences in 2013.Genecards created by Professor Doron Lancet's team at the Weizmann Institute of Science in Israel in 1997,Drugbank created by scientists at the University of Alberta in Canada in 2006,and the OMIM database established by Dr.Victor A.McKusick's team at Johns Hopkins University in the United States in 1966 were used to search the disease protein targets of periarthritis of shoulder and type 2 diabetes,and the intersection targets were obtained based on the WeChat online tool.The protein-protein interaction network was constructed based on the STRING database created in 2000 by Peer Bork's team at the European Bioinformatics Institute(EMBL),and the protein-protein interaction relationship was analyzed.The core targets were screened according to the degree value.The intersection targets were subjected to GO and KEGG enrichment analyses.Finally,molecular docking and molecular dynamics simulation were used to verify the binding of key components to key targets.RESULTS AND CONCLUSION:(1)One hundred and forty-two active ingredients of Gegenmaqi prescription were obtained,including 65 intersections between component targets and disease targets,5 key active ingredients(β-sitosterol,stigmasterol,kaempferol,quercetin,and formononetin),and 5 key targets(AKT1,tumor necrosis factor,interleukin-10,JUN,and TP53).(2)GO function enrichment included 508 items,390 biological process items,77 molecular function items and 41 cell component items.KEGG pathway analysis showed 146 pathways,mainly involving advanced glycation end products receptor signaling pathway,lipid and atherosclerosis signaling pathway,tumor necrosis factor signaling pathway,and interleukin-17 signaling pathway.(3)Molecular docking showed that the key components and key targets had good binding activity.Molecular dynamics simulation showed that β-sitosterol had stable interactions with AKT1,tumor necrosis factor and interleukin 10.(4)Gegenmaqi prescription has been comprehensively studied,and the material basis of its pharmacological effect has been primarily clarified.It is predicted that Gegenmaqi prescription can treat periarthritis of shoulder combined with type 2 diabetes through multi-components,multi-targets,and multi-pathways to exert anti-inflammatory and regulate insulin secretion.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To analyze the current state,research hotspots,and development trends of electroencephalography(EEG)applied in the field of autism spectrum disorder(ASD). Methods Relevant literature from the Web of Science core collection database from January,2014 to January,2024 were retrieved and analyzed using CiteSpace 6.2.R4. Results A total of 1 509 articles were included,with an increasing trend in publication volume over the years.The United States ranked highest in both publication volume and node centrality.The primary journals in this field were concentrated in clinical medicine,immunology and psychology.Keyword co-occurrence and clustering indicated that research primarily focused on the correlation between core symptoms of ASD and EEG indicators,differential diagnosis of ASD and its comorbidities,brain functional connectivity,and assessment of rehabilitation efficacy.Keywords bursted in the past three years mainly included artificial intelligence and machine learning. Conclusion The researches in EEG technology in the field of ASD is generally increasing.Future researches may focus on exploring the brain network mechanisms of ASD using EEG combined with multimodal neuroimaging,and machine learning technologies.

Objective:To assess the efficacy and toxicities of carboplatin+etoposide(CE)regimens combined with abiraterone+prednisone(AAP)in patients with metastatic castration-resistant prostate cancer(mCRPC)after progression with docetaxel+prednisone(DP)regimens chemotherapy and novel hormone therapy(NHT).Methods:Retrospective analysis of mCRPC treated with DP regimens chemotherapy and/or NHT after progression,received CE regimens with AAP every 3 weeks for one cycle×6 cycles.The outcome were prostate specific an-tigen(PSA)response rate,time to PSA progression(TTPP),radiographic progression-free survival(rPFS),30%reduction in PSA,90%reduc-tion in PSA,the objective response remission rate and overall survival(OS).Results:From March 2019 to February 2024,37 eligible mCRPC patients were admitted to Cancer Hospital of Huanxing Chaoyang District Beijing and National Cancer Center/National Cancer Clinical Re-search Center/Cancer Hospital.After progression,CE regimens combined with AAP regimens was used for treatment.The median follow-up was 12.0(3.0-57.0)months.The median treatment cycle was 4 cycles.The PSA response rate was 42.1%.The median TTPP was 4.0 months;the median rPFS was 8.9 months and the median OS was 15.0 months.The objective remission rate was 24.3%,the proportion of 30%de-crease in PSA was 59.5%,and the proportion of 90%decrease in PSA was 16.2%.As for treatment side effects,10 cases had grade 3 or higher adverse reactions.Conclusions:CE regimens combined with AAP for mCRPC patients who failed DP regimens chemotherapy and/or NHT initially showed good clinical efficacy and tolerability.Additional sample size and follow-up time are needed to further validate the effic-acy.

Objective:To investigate the utilization of health management services and its influencing factors among new urban population.Methods:It is a cross-sectional study. From July 2020 to March 2021, a stratified random sampling method was used to extract 1978 new urban population in Jining city, and an anonymous self-administered questionnaire survey was conducted using a self-made questionnaire ′Residents Health Questionnaire′. The survey included general demographic characteristics, personal behavior lifestyle and medical care status. The χ2 test and binary logistic regression were used to analyze the factors influencing the utilization of health management services by new urban population. Results:The overall utilization of health management services in the new urban population was 47.22%. There were significant differences in utilization of health management services among new urban population with different gender, age, education level, occupation and monthly income. Binary logistic regression analysis showed that female ( OR=1.354, 95% CI: 1.094-1.676), people aged over 60 years ( OR=1.873, 95% CI: 1.413-2.483), people with a mean monthly income over 3 000 yuan ( OR=1.498, 95% CI: 1.123-1.997), people engaged in light manual labor ( OR=1.596, 95% CI: 1.003-2.539), people who exercise regularly( OR=2.400, 95% CI: 2.028-2.841) and people having social basic medical insurance ( OR=2.633, 95% CI: 2.042-3.394) had better utilization of health management projects. People who sat more than 3 hours a day ( OR=0.630, 95% CI: 0.532-0.745) had lower utilization of health management care. Conclusion:The utilization of health management projects in the new urban population is low. Gender, age, monthly income, physical exercise, sedentary time, daily labor intensity and social basic medical insurance status are the main influencing factors.