OBJECTIVE: To explore the mechanism by which the extract of Semen Ziziphi Spinosae extract promotes bone growth in mice by modulation of the expression of miR-34a-5p in brain tissue. METHODS: Mice were assigned to four experimental groups:a normal control group, a drug administration group (receiving 0.320 mg·g^-1 body weight of Semen Ziziphi Spinosae extract via intragastric administration), a positive control group (receiving 0.013 mg·g^-1 body weight of jujube seed saponin via intragastric administration), and a combination group administration with Semen Ziziphi Spinosae extract plus a 5-hydroxytryptamine 2A receptor (5-HT2AR) agonist (intragastric administration of Semen Ziziphi Spinosae extract combined with intracerebroventricular injection of 8 μg P-MPPF per mice for the final three days of the experiment). Following a 20-day administration period, the effects of the interventions on bone growth, serum growth hormone (GH) levels, and 5-HT2AR expression in brain tissue were evaluated. MicroRNAs (miRNAs) that were differentially expressed in the brain tissues of mice exhibiting bone growth induced by Semen Ziziphi Spinosae extract, as compared to those in normal mice, were identified using a gene chip approach. The interaction between miR-34a-5p and 5-HT2AR was subsequently validated through quantitative reverse transcription polymerase chainreaction (RT-qPCR) and dual-luciferase reporter gene assays. Subsequently, by utilizing the miR-34a-5p inhibitor group and mimics group, along with the normal control group, the drug administration group, the positive control group, and the drug administration combined with miR-34a-5p inhibitor group, the variations in 5-HT2AR expression in mouse brain tissue across all groups were examined, and the binding activity of 5-hydroxytryptamine (5-HT) to the 5-hydroxytryptamine 1A receptor (5-HT1AR) in mice was assessed. RESULTS: The body lengths of the normal control group and the drug administration group were(8.9±0.3) and(10.4±0.4) cm;femur lengths were (8.5±0.3) and (9.1±0.5) mm;tibia lengths were (10.7±0.3) and (11.2±0.4) mm, respectively. The contents of GH levels were (58.6±8.2) and (72.9±6.1) ng·ml-1;and the contents of 5-HT2AR were (32.0±5.0) and (21.9± 5.5) ng·ml-1, respectively. Compared with the normal control group, the drug administration group promoted the growth of body length, femur, and tibia in mice, and increased GH secretion, showing statistically significant differences (P<0.05). Additionally, it significantly reduced the content of 5-HT2AR in brain tissue, with statistical significance (P<0.01). The gene chip analysis identified a total of 16 differentially expressed miRNAs, of which 13 were up-regulated and 3 were down-regulated. Bioinformatics analysis predicted that the up-regulated miR-34a-5p could regulate the expression of 5-HT2AR, a prediction that was confirmed through a dual-luciferase reporter gene assay, demonstrating a direct regulatory interaction between the two. Furthermore, in vivo experiments in mice revealed that overexpression and silencing of miR-34a-5p resulted in corresponding changes in the expression levels of 5-HT2AR in brain tissues/cells, as well as in the binding activity between 5-HT and 5-HT1AR. CONCLUSION The Semen Ziziphi Spinosae extract promotes animal bone growth by enhancing miR-34a-5p expression in brain tissue, downregulating the expression level of 5-HT2AR, improving the binding activity between 5-HT and 5-HT1AR, and extending slow-wave sleep duration, thereby stimulating GH secretion.
Animals ; MicroRNAs/metabolism* ; Mice ; Male ; Brain/metabolism* ; Ziziphus/chemistry* ; Bone Development/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Plant Extracts/pharmacology*

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

Objective:To investigate the causal correlation between depression and stress urinary incontinence(SUI)using Mendelian randomization(MR)analysis.Methods:We searched the FinnGen Consortium database for genome-wide association studies(GWAS)on depression and obtained 23 424 case samples and 192 220 control samples,with the GWAS data on SUI provided by the UK Biobank,including 4 340 case samples and 458 670 control samples.We investigated the correlation between depression and SUI based on the depression data collected from the Psychiatric Genomics Consortium(PGC).We employed inverse-variance weighting as the main method for the MR study,and performed sensitivity analysis to verify the accuracy and stability of the findings.Results:Analysis of the data from the UK Biobank and FinnGen Consortium showed that depression was significantly correlated with an increased risk of SUI(P=0.005),but not SUI with the risk of depression(P=0.927).And analysis of the PGC data verified the correlation of depression with the increased risk of SUI(P=0.043).Conclusion:Depression is associated with an increased risk of SUI,while SUI does not increase the risk of depression.

AIM To study the chemical constituents from the leaves of Citrus reticulata Blanco and their anti-inflammatory activities.METHODS The 85%ethanol extract from the leaves of C.reticulata was isolated and purified by silica gel,D101 macroporous resin,MCI,ODS and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The Griess method was used to determine their inhibitory activities on lipopolysaccharide-induced NO production in macrophages RAW 264.7 cells.The mice foot swelling inflammation model induced by carrageenan was established,and the levels of IL-1β,TNF-α were detected.RESULTS Twelve compounds were isolated and identified as nobiletin(1),tangeretin(2),5-demethylinoblitin(3),5,4'-dihydroxy-6,7,8,3'-tetramethoxy flavone(4),5-hydroxy-7,8,3',4'-trimethoxyflavanone(5),3,5,6,7,8,3',4'-heptamethoxyflavanone(6),hesperetin(7),5-hydroxy-6,7,3',4'-tetramethoxyphenone(8),β-balsam alcohol(9),stigmaster-5-en-3β-alcohol(10),p-hydroxybenzaldehyde(11),vanillin(12).Compounds 1,4,6,7,10 and 12 had strong inhibitory activites on NO release in LPS-induced RAW 264.7 cells,and the IC50 values were(25.21±2.10),(37.77±0.50),(38.19±1.58),(21.89±1.73),(43.81±1.18),(47.98±2.55),(41.23±1.11),(43.80±1.43)μmol/mL,respectively.Compounds 2-3 reduced IL-1β and TNF-α levels(P＜0.05,P＜0.01).CONCLUSION Compounds 6-7,9 are isolated from this plant for the first time.Compounds 1-4,8 exhibit strong in vitro anti-inflammatory activities,and compounds 2-3 exhibit significant in vivo anti-inflammatory activities.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.