Background Cyclin-dependent kinase-like 1 (CDKL1) is a member of the cell division control protein 2-related serine–threonine protein kinase family. It is known to occur in various malignant tumors, but its role in neuroblastoma (NB) remains unclear. Methods We constructed a CDKL1-silenced NB cell strain (SH-SY5Y) and used real-time PCR and western blotting to confirm the silencing. Functional analyses were performed using the MTT, colony-formation, FACS, wound-healing and transwell invasion assays. Results The expression of CDKL1 was significantly upregulated in NB tissue as compared to the adjacent normal tissue. CDKL1 knockdown significantly suppressed cell viability and colony formation ability. It also induced cell cycle G0/G1 phase arrest and apoptosis, and suppressed the migration and invasion ability of SH-SY5Y cells. CDKL1 knockdown decreased the CDK4, cyclin D1 and vimentin expression levels, and increased the caspase-3, PARP and E-cadherin expression levels in SH-SY5Y cells. Conclusions Our findings suggest that CDKL1 plays an important role in NB cell proliferation, migration and invasion. It might serve as a potential target for NB therapy.
Background To develop a prediction model predicting in-hospital mortality of elder patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU). Methods In this cohort study, data of 619 patients with CAP aged ≥ 65 years were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001–2012 database. To establish the robustness of predictor variables, the sample dataset was randomly partitioned into a training set group and a testing set group (ratio: 6.5:3.5). The predictive factors were evaluated using multivariable logistic regression, and then a prediction model was constructed. The prediction model was compared with the widely used assessments: Sequential Organ Failure Assessment (SOFA), Pneumonia Severity Index (PSI), systolic blood pressure, oxygenation, age and respiratory rate (SOAR), CURB-65 scores using positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), area under the curve (AUC) and 95% confidence interval (CI). The decision curve analysis (DCA) was used to assess the net benefit of the prediction model. Subgroup analysis based on the pathogen was developed. Results Among 402 patients in the training set, 90 (24.63%) elderly CAP patients suffered from 30-day in-hospital mortality, with the median follow-up being 8 days. Hemoglobin/platelets ratio, age, respiratory rate, international normalized ratio, ventilation use, vasopressor use, red cell distribution width/blood urea nitrogen ratio, and Glasgow coma scales were identified as the predictive factors that affect the 30-day in-hospital mortality. The AUC values of the prediction model, the SOFA, SOAR, PSI and CURB-65 scores, were 0.751 (95% CI 0.749–0.752), 0.672 (95% CI 0.670–0.674), 0.607 (95% CI 0.605–0.609), 0.538 (95% CI 0.536–0.540), and 0.645 (95% CI 0.643–0.646), respectively. DCA result demonstrated that the prediction model could provide greater clinical net benefits to CAP patients admitted to the ICU. Concerning the pathogen, the prediction model also reported better predictive performance. Conclusion Our prediction model could predict the 30-day hospital mortality in elder patients with CAP and guide clinicians to identify the high-risk population.
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