Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Objective To examine the intention of users to adopt mobile healthcare services from the perspective of service encounter theory,and to promote the improvement of mobile healthcare service quality.Methods Combining service encounter theory,perceived trust,and social presence,a model of user willingness to use mobile healthcare services is constructed.This model and its hypotheses are empirically tested using a questionnaire survey and structural equation modeling.Results It reveals that perceived trust significantly influences users'willingness to use mobile healthcare services.Moreover,word-of-mouth encounter,technical encounter,personal encounter,and social presence all impact perceived trust.In order of impact,these factors are personal encounter,technical encounter,social presence,and word-of-mouth encounter.Conclusion Based on the research findings,strategies for improving mobile healthcare services are proposed,including:bridging the digital divide and including marginalized groups,innovating technology applications and exploring Artificial Intelligence empowerment paths,enhancing multi-party collaboration to build a service ecosystem,and optimizing user relationships to convey brand value.

Objective To examine the intention of users to adopt mobile healthcare services from the perspective of service encounter theory,and to promote the improvement of mobile healthcare service quality.Methods Combining service encounter theory,perceived trust,and social presence,a model of user willingness to use mobile healthcare services is constructed.This model and its hypotheses are empirically tested using a questionnaire survey and structural equation modeling.Results It reveals that perceived trust significantly influences users'willingness to use mobile healthcare services.Moreover,word-of-mouth encounter,technical encounter,personal encounter,and social presence all impact perceived trust.In order of impact,these factors are personal encounter,technical encounter,social presence,and word-of-mouth encounter.Conclusion Based on the research findings,strategies for improving mobile healthcare services are proposed,including:bridging the digital divide and including marginalized groups,innovating technology applications and exploring Artificial Intelligence empowerment paths,enhancing multi-party collaboration to build a service ecosystem,and optimizing user relationships to convey brand value.

【Objective】 To analyze the gene expression profile of central nervous system primitive neuroectodermal tumors （CNS-PNETs） by bioinformatics methods so as to explore the possible pathogenesis of CNS-PNETs at the molecular level. 【Methods】 The gene expression profile of CNS-PNETs was downloaded from the GEO database， GSE35493 and GSE74195. The differentially expressed genes （DEGs） were screened by the online analysis tool of GEO2R and Venn software， DEGs were analyzed by using the online analysis tools of David database， such as Gene Ontology （GO） and pathway enrichment （KEGG）. The protein interaction network analysis （PPI） of CNS-PNETs was made by using STRING online analysis tool， Cytoscape software and its plug-in cytohubba to find the key genes. 【Results】 We obtained 262 DEGs， including 49 upregulated genes and 213 downregulated genes. The analysis of GO function and KEGG signal pathway enrichment showed that DEG was involved in DNA transcription and mitosis， cell division， synaptic signal transmission and other biological processes， and associated with cell cycle， tumor-related pathway， p53 signal pathway， synapsis-related signal pathway， cAMP signal pathway and calcium ion signal pathway. Ten key genes， namely， CDK1， CDC20， MAD2L1， KIF11， ASPM， TOP2A， TTK， NDC80， NUSAP1 and DLGAP5 were screened out by STRING analysis. 【Conclusion】 Ten key genes including CDK1 may play an important role in the initiation and progression of CNS-PNETs， providing new clues for exploring the pathogenesis of CNS-PNETs.

To ensure the safety of the commercially available chenpi, a convenient and fast analytical method was developed for the determination of 133 pesticide residues in chenpi using gas chromatography-tandem mass spectrometry (GC-MS/MS). In this study, different extraction solvents, redissolution solvents and adsorbents were tested according to the recovery and purification effect to obtain a modified QuEChERS method. The samples were extracted with acetonitrile. During the clean-up step, octadecyl-modified silica (C18) and graphitized carbon black (GCB) were selected, and aminopropyl (NH2) was used instead of primary secondary amine (PSA) because of its weaker ion exchange capacity which had little effect on the recovery of ditalimfos. Samples were quantified by matrix-matched calibration with internal stan-dards. All pesticides showed good linearity in the respective range, both with values of r2 >0.99. The average recoveries of the pesticides spiked samples ranged from 70.0％ to 112.2％ with the RSDs of 0.2％–14.4％. The modified QuEChERS method was validated and applied to twenty real samples. Five pesticides were found in eight batches, but no pesticide exceeded the maximum residue limits (MRL, MRL reference to European commission).

Objective: To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U-Net neural network. Methods: Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state-of-the-art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine-tuned U-Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U-Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland-Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U-Net segmentation and the manual segmentation. Results: The sensitivity, specificity and Dice coefficient of the fine-tuned U-Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland-Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm². The substantial agreement was observed between U-Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine-tuned U-Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

Objective To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U?Net neural network. Methods Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state?of?the?art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine?tuned U?Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U?Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland?Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U?Net segmentation and the manual segmentation. Results The sensitivity, specificity and Dice coefficient of the fine?tuned U?Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland?Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm2. The substantial agreement was observed between U?Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine?tuned U?Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

OBJECTIVE: To explore the mechanism by which simvastatin (SIM) regulates osteoclast apoptosis. METHODS: Murine macrophage RAW264.7 cells were divided into 5 groups, namely group A (control group), group B (sRANKL+ M-CSF), group C (SIM+sRANKL+M-CSF), group D (VIVIT peptide+sRANKL+ M-CSF), and group E (SIM+VIVIT peptide+sRANKL+M-CSF). WST-1 assay was used to assess the effects of simvastatin on the proliferation activity of the osteoclasts, and flow cytometry was performed to analyze the effects of SIM and VIVIVIT peptide (a NFATc1 pathway inhibitor) on apoptosis of the osteoclasts. The translocation of NFATc1 into the nucleus was investigated using immunofluorescence assay, and Western blotting was employed to assess the effect of SIM on the phosphorylation of NFATc1 in the nucleus. RESULTS: WST-1 assay showed that SIM (1×10 mol/L) treatment for 24 and 48 h significantly inhibited the proliferation of the osteoclasts (=0.039 and 0.022, respectively). Compared with the control group, the SIM-treated osteoclasts exhibited significantly reduced cell percentage in G0/G1 phase (=0.041) and increased cells in sub-G1 phase (=0.028) with obvious cell apoptosis. DAPI staining and flow cytometry showed that both SIM and VIVIVIT peptide alone significantly promoted osteoclast apoptosis (=0.002 and 0.015, respectively), and their combination produced a similar pro-apoptosis effect (=0.08). Immunofluorescence and Western blotting showed that SIM significantly inhibited the intranuclear translocation of NFATc1 and the phosphorylation of NFATc1 pathway protein (=0.013). CONCLUSIONS SIM promotes osteoclast apoptosis through NFATc1 signaling pathway.
Animals ; Apoptosis ; Cell Differentiation ; Mice ; NFATC Transcription Factors ; Osteoclasts ; RANK Ligand ; Simvastatin

Objective To introduct the experience of anesthesia and operation of complicated resection of the trachea,and promote techniques of anesthesia and operation of the tracheal resection and reconstruction. Methods Reviewing the anesthetic and operative process of 5 cases of tracheal resection and reconstruction,dis-close difficulties with corresponding methods,postoperative follow-up,summarizing suitable measures for succeed of large-segment resection and reconstruction of the trachea. Results In 4 cases of patients with benign stricture of the trachea,3 cured with good quality of life in 2 - 5 years follow-up, 1 case of resection of 6 cm trachea with one-stage reconstruction dead from anastomotic fistula and infection of mediastinum. One case with malignancy re-section of 8 cm trachea and reconstructed with Zhao's artificial trachea dead from remote metastasis one and a half year later. Conclusion The complexity of tracheal operation is with big different from case to case,therefore, preoperative precisely evaluation with careful individually protocol of anesthesia and operation,and good coopera-tion between surgeon and anesthesiologist are critical.

Objective:To study influence of intravenous injection urapidil and nitroglycerin micro pump on blood pressure and heart rate (HR) of hypertension patients undergoing tooth extraction.Methods:116 hypertension patients underwent electrocardiographic monitoring tooth extraction in our hospital from January 2015 to October 2016 were enrolled in this study.According to the random number table method,the patients were divided into observation group and control group with 58 cases,the control group was given nitroglycerin plus pump static point to maintain,the observation group was given intravenous urapidil and pump maintenance,compared the changes of systolic blood pressure (SBP),diastolic blood pressure (DBP) and HR in two groups before operation,at anesthesia,10min after anesthesia,in operation,10 min after operation,and compared the adverse reactions condition of the two groups.Results:The levels of SBP and DBP in two groups in operation and 10 min after operation were significantly lower than that before operation,and the SBP and DBP levels in the observation group in operation were significantly lower than control group,the differences were statistically significant (all P＜0.05).The HR in control group in operation,10 min after operation were significantly higher than before operation,while the observation group were significantly lower control group,the differences were statistically significant (P＜0.05).The total incidence of adverse reactions in the observation group was 6.90％(4/58),which has no significant difference than 10.34％ (6/58) in control group (P＞0.05).Conclusion:Intravenous injection urapidil has little effect on blood pressure and HR in hypertension patients undergoing tooth extraction,and with good safety,which is worthy of promotion.