Bone is a highly calcified and vascularized tissue. The vascular system plays a vital role in supporting bone growth and repair, such as the provision of nutrients, growth factors, and metabolic waste transfer. Moreover, the additional functions of the bone vasculature, such as the secretion of various factors and the regulation of bone-related signaling pathways, are essential for maintaining bone health. In the bone microenvironment, bone tissue cells play a critical role in regulating angiogenesis, including osteoblasts, bone marrow mesenchymal stem cells (BMSCs), and osteoclasts. Osteogenesis and bone angiogenesis are closely linked. The decrease in osteogenesis and bone angiogenesis caused by aging leads to osteoporosis. Long noncoding RNAs (lncRNAs) are involved in various physiological processes, including osteogenesis and angiogenesis. Recent studies have shown that lncRNAs could mediate the crosstalk between angiogenesis and osteogenesis. However, the mechanism by which lncRNAs regulate angiogenesis‒osteogenesis crosstalk remains unclear. In this review, we describe in detail the ways in which lncRNAs regulate the crosstalk between osteogenesis and angiogenesis to promote bone health, aiming to provide new directions for the study of the mechanism by which lncRNAs regulate bone metabolism.
RNA, Long Noncoding/physiology* ; Osteogenesis/physiology* ; Humans ; Neovascularization, Physiologic/genetics* ; Bone and Bones/metabolism* ; Animals ; Mesenchymal Stem Cells ; Signal Transduction ; Osteoblasts ; Osteoclasts ; Angiogenesis

Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.

OBJECTIVE: To explore the integrated traditional Chinese and Western medicine treatment plan for ankylosing spondylitis complicated with lower cervical spine fracture and dislocation, adopt the treatment plan of preoperative continuous traction, intraoperative prizing reduction combined with anterior long-segment plate-screw and posterior short-segment pedicle screw-rod system internal fixation, and evaluate its surgical efficacy and clinical application value. METHODS: From June 2018 to September 2022, 7 male patients with ankylosing spondylitis complicated with lower cervical spine fractures were admitted, aged 43 to 65 years old. Among them, there was 1 case of C_3,4 fracture, 1 case of C_4,5 fracture, 1 case of C_6,7 fracture, and 4 cases of C_5,6 fracture, all of which were fracture and dislocation. All patients received preoperative continuous skull traction, and intraoperative prizing reduction combined with anterior long-segment plate-screw and posterior short-segment pedicle screw-rod system internal fixation. The Neck Disability Index (NDI), Japanese Orthopedic Association (JOA) score, and Frankel scale were used to evaluate the neurological function and quality of life before and after surgery. The visual analogue scale (VAS) was used to evaluate neck and limb pain. The operation time, blood loss, hospital stay, and surgery-related complications were recorded. RESULTS: All 7 patients were followed up for 6 to 24 months after surgery. The operation time of the 7 patients ranged from 300 to 480 minutes, the blood loss ranged from 300 to 1000 ml, and the hospital stay ranged from 8 to 25 days. The preoperative NDI of the 7 patients ranged from 25% to 42%, which decreased to 12% to 30% at 1 week after surgery and 5% to 25% at the last follow-up. The preoperative JOA score ranged from 8 to 13 points, which increased to 12 to 15 points at 1 week after surgery and 13 to 16 points at the last follow-up. The preoperative VAS ranged from 6 to 8 points, which decreased to 2 to 4 points at 1 week after surgery and 0 to 3 points at the last follow-up. Regarding the Frankel grade of neurological function, 2 patients were grade C before surgery and recovered to grade D at the last follow-up after surgery, and the remaining patients recovered to grade E at the last follow-up after surgery. There were 3 cases of pressure ulcers, including 1 case of intraoperative pressure ulcer, 1 case of cervical cerebrospinal fluid leakage, 1 case of screw loosening, and 1 case of aggravated fracture dislocation due to preoperative traction. CONCLUSION Preoperative cervical traction combined with intraoperative prizing reduction and anterior long-segment plate combined with posterior short-segment pedicle screw internal fixation provides a safe and effective surgical option for ankylosing spondylitis complicated with lower cervical spine fracture and dislocation, which can minimize surgical trauma and improve clinical efficacy. However, this study has a small sample size and a short follow-up time for some patients, so further verification with large-sample and long-term follow-up data is still needed.
Humans ; Male ; Adult ; Middle Aged ; Fracture Fixation, Internal/methods* ; Spondylitis, Ankylosing/surgery* ; Cervical Vertebrae/surgery* ; Spinal Fractures/surgery* ; Traction ; Aged ; Joint Dislocations/surgery*

Refinement and standardisation of the management of clinical diagnostic and treatment operations is a key aspect of achieving high-quality development in hospitals.By analysing the management status quo of clinical diagnosis and treatment operations in hospitals,it combed the problems existing in this field.Based on the closed-loop management model,it proposed measures and recommendations to promote the continuous optimisation of the management of clinical diagnostic operations in hospitals.Hospitals should establish hospital-level operation catalog and conduct classified management,authorize operators and dynamically adjust them,carry out operation quality management,pay attention to information management of operation management,and combine operation management with physician performance management.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

AIM To investigate effects of petroleum ether fraction from Derris eriocarpa How on glucose and lipid metabolism in a mouse model of metabolic syndrome(MS).METHODS KM mice were fed a high-fat diet and administered streptozotocin intraperitoneally to establish MS models.The MS mice were then randomly assigned to the model group,the metformin hydrochloride group,the lovastatin group,the ursolic acid group,and the high-,medium-and low-dose D.eriocarpa petroleum ether fraction groups,with 10 mice in each group.Ten additional mice maitained on a normal diet served as the normal control group.After 4 weeks of intragastric administration,glucose and lipid metabolism indicators were measured.Hepatic pathological changes were assessed using HE staining and oil red O staining.Liver tissue mRNA expressions of ATF3,PEPCK,FXR,CYP7A1,HNF4ɑ,CYP8B1 and SRB1 were quantified by RT-qPCR.Hepatic protein expressions of ATF3,HNF4ɑ,PEPCK,FXR and CYP7A1 was analyzed by Western blot in MS mice.RESULTS Compared to the model group,the high-dose D.eriocarpa petroleum ether fraction group exhibited significant glucose tolerance improvement(reduced OGTT-AUC,P＜0.01);favorable serum lipid modulation in terms of increased HDL-C levels(P＜0.01)and decreased TG,TC,LDL-C(P＜0.01);reduced renal biomarkers(BUN,SCR)and hepatotoxic indicators of TBA,AST and ALT activities(P＜0.01);alleviated hepatic lipid accumulation and histopathological damage;downregulated mRNA and protein expressions of ATF3,HNF4ɑ and PEPCK,as well as CYP8B1 mRNA expression(P＜0.01);and upregulated mRNA and protein expressions of FXR and CYP7A1,along with SRB1 mRNA expression(P＜0.01).CONCLUSION D.eriocarpa petroleum ether fraction ameliorates glucose and lipid metabolism dysregulation in MS mice by modulating the ATF3/HNF4ɑ/CYP7A1 signaling pathway,consequently eliciting hypoglycemic,hypolipidemic,hepatoprotective and nephroprotective effects.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective To assess the impact of long working hours on sleep disorders among manufacturing workers and explore the roles of alcohol consumption and mental health factors(anxiety and depressive symptoms)in this association.Methods A cross-sectional study design was used to survey 1 336 manufacturing workers in Shenzhen.We collected the data of their demographic characteristics,work-related factors,personal behaviors,sleep disorders,and mental health status.Multivariate Logistic regression analysis was used to assess the association between long working hours and sleep disorders.Stratified analysis and mediation effect models were applied to examine the effect modification by alcohol consumption and the mediating role of mental health factors,respectively.Results Among the study samples,31.8％reported long working hours and 45.6％had sleep disorders.Multivariate Logistic regression analysis showed that long working hours significantly increased the risk of sleep disorders(adjusted OR=2.073,95％ CI:1.478-2.907,P＜0.001).Stratified analysis revealed that the association between long working hours and sleep disorders was more pronounced among alcohol consumers(adjusted OR=2.556,95％ CI:1.432-4.562,P=0.001).Mediation effect analysis showed that anxiety and depressive symptoms partially mediated the relationship between long working hours and sleep disorders,with indirect effects accounting for 25.71％ and 27.14％,respectively.Conclusion Long working hours increase the risk of sleep disorders among manufacturing workers,particularly among those who consume alcohol.Anxiety and depressive symptoms partially explain the association between long working hours and sleep disorders.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.