Aporphine alkaloids from the leaves of Nelumbo nucifera Gaertn are substances of great interest because of their important pharmacological activities, particularly anti-diabetic, anti-obesity, anti-hyperlipidemic, anti-oxidant, and anti-HIV’s activities. In order to produce large amounts of pure alkaloid for research purposes, a novel method using high-speed counter-current chromatography (HSCCC) was developed. Without any initial cleanup steps, four main aporphine alkaloids, including 2-hydroxy-1-methoxyaporphine, pronuciferine, nuciferine and roemerine were successfully purified from the crude extract by HSCCC in one step. The separation was performed with a simple two-phase solvent system composed of n-hexane-ethyl acetate-methanol-acetonitrile-water (5:3:3:2.5:5, v/v/v/v/v). In each operation, 100 mg crude extracts was separated and yielded 6.3 mg of 2-hydroxy-1-methoxyaporphine (95.1% purity), 1.1 mg of pronuciferine (96.8% purity), 8.5 mg of nuciferine (98.9% purity), and 2.7 mg of roemerine (97.4%) respectively. The chemical structure of four aporphine alkaloids are identified by means of electrospray ionization MS (ESI-MS) and nuclear magnetic resonance (NMR) analysis. Moreover, the effects of four separated aporphine alkaloids on insulin-stimulated glucose consumption were examined in 3T3-L1 adipocytes. The results showed that 2-hydroxy-1-methoxyaporphine and pronuciferine increased the glucose consumption significantly as rosiglitazone did.
BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.
Objective. To investigate the application value of comprehensive nursing combined with comfort nursing for severe stroke patients with diabetes in the intensive care unit (ICU), as well as its effect on the incidence of pressure ulcers and aspiration. Methods. Between March 2019 and March 2021, 123 severe stroke patients with diabetes who were treated at our hospital were randomly assigned to one of two groups: the control group (n = 61) or the study group (n = 62). The control group received normal care, but the research group received comprehensive nursing as well as comfort nursing. The two patient groups were compared in terms of the effects of the clinical application. Results. The two groups did not differ significantly in general data (
P
>
0.05
). The shorter ICU monitoring and extubation times, the lower incidence of pressure ulcers, aspiration, and nosocomial infections, and higher self-rating anxiety scale (SAS) and self-rating depression scale (SDS) scores and a lower MOS 36-item short-form health survey (SF-36) score were all observed in the research group when compared to the control group (
P
<
0.05
). Conclusion. For severe stroke patients with diabetes in the ICU, comprehensive nursing combined with comfort nursing has a promising effect, significantly, lowering the risk of pressure ulcers, aspiration, and nosocomial infections, accelerating physical recovery, enhancing mental state, and ensuring a better prognosis, deserving general clinical promotion.
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