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Acute myeloid leukemia is one of the most commonly identified hematological malignancies with poor prognosis. This research was planned to identify the cytotoxic effects of Auraptene on HL60 and U937 cell lines. The cytotoxic effects of Auraptene were measured by AlamarBlue assay (Resazurin) after 24- and 48-h treatments with different doses of Auraptene. The inductive effects of Auraptene on cellular oxidative stress were investigated by determining cellular ROS levels. The cell cycle progression and cell apoptosis were also evaluated by flow cytometry method. Our findings revealed that Auraptene decreased HL60 and U937 cellular proliferation by downregulation of Cyclin D1. Auraptene also induces cellular oxidative stress by upregulation of cellular ROS levels. Auraptene induces cell cycle arrest the early and late phases of apoptosis by upregulation of Bax and p53 proteins. Our data suggest that the anti-tumor function of Auraptene can be mediated by promoting apoptosis and cell cycle arrest and inducing cellular oxidative stress in HL60 and U937 cell lines. These results support that Auraptene may be used as a potent anti-tumor agent against hematologic malignancies in the further studies. [ABSTRACT FROM AUTHOR]

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease. Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool. Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation. Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. [ABSTRACT FROM AUTHOR]

Background: We investigated the efficacy of a mobile-based bolus advisor app in comparison with the usual multiple daily injections (MDIs) in diabetic patients. Materials and Methods: In a nonrandomized, controlled clinical trial, 62 diabetic patients were selected to receive a 12-week intensive glycemic control by either a mobile-based bolus advisor (app) or MDI in the usual manner. We compared mean blood glucose (BG) and HbA1c before and just after the treatment program. The data were analyzed using paired sample t-test and analysis of variance (ANOVA). Results: Fifty-six patients (30 cases and 26 controls) completed the study. The mean [standard deviation (SD)] of BG was 220.57 (43.7) and 231.5 (55) in the app group and control group, respectively. Mean BG decreased 38 mg/dL in the app and 16 mg/dL in the control group (P = 0.001 and 0.049 respectively). Changes of mean BG were different between the two groups significantly (P = 0.039). HbA1c decreased from 8.4% to 7.6% in the case and from 8.4% to 8% in the control group (P = 0.001 and 0.06, respectively). Changes of HbA1c were not different between the two groups (P = 0.141). The mean episodes of hypoglycemia were not different between the groups significantly (P = 0.108). Conclusion: In conclusion, this study revealed that mobile-based bolus advisors can reduce mean BG better in patients who are planned to have a tight glycemic control as a feasible and available method and may improve HbA1c in the long term. [ABSTRACT FROM AUTHOR]

There are a few experimental and numerical studies on the behavior of microscale vortex tubes. The intention of this work is to investigate the energy separation phenomenon in a micro-scale vortex tube by using the computational fluid dynamic. The flow is assumed as steady, turbulent, compressible ideal gas, and the shear-stress transport k-? is used for modeling of turbulence phenomenon. The results show that 3-D computational fluid dynamic simulation is more accurate than 2-D axisymmetric one. Moreover, optimum cold-mass ratios to maximize the refrigeration-power and isentropic-efficiency are evaluated. The results of static temperature, velocity magnitude, and pressure distributions show that the temperature-separation in the micro-scale vortex tube is a function of kinetic-energy variation and air-expansion in the radial direction. [ABSTRACT FROM AUTHOR]