OBJECTIVE: This study investigates the antidiabetic and antioxidant effects of M. latifolia bark extracts, fractions, and isolated constituents.
MATERIALS AND METHODS: Melicope latifolia extracts (hexane, chloroform, and methanol), fractions, and isolated constituents with varying concentrations (0.078-10 mg/mL) were subjected to in vitro α-amylase and dipeptidyl peptidase-4 (DPP-4) inhibitory assay. Molecular docking was performed to study the binding mechanism of active compounds towards α-amylase and DPP-4 enzymes. The antioxidant activity of M. latifolia fractions and compounds were determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and β-carotene bleaching assays.
RESULTS: Melicope latifolia chloroform extract showed the highest antidiabetic activity (α-amylase IC50: 1464.32 μg/mL; DPP-4 IC50: 221.58 μg/mL). Fractionation of chloroform extract yielded four major fractions (CF1-CF4) whereby CF3 showed the highest antidiabetic activity (α-amylase IC50: 397.68 μg/mL; DPP-4 IC50: 37.16 μg/mL) and resulted in β-sitosterol (1), halfordin (2), methyl p-coumarate (3), and protocatechuic acid (4). Isolation of compounds 2-4 from the species and their DPP-4 inhibitory were reported for the first time. Compound 2 showed the highest α-amylase (IC50: 197.53 μM) and β-carotene (88.48%) inhibition, and formed the highest number of molecular interactions with critical amino acid residues of α-amylase. The highest DPP-4 inhibition was exhibited by compound 3 (IC50: 911.44 μM).
DISCUSSION AND CONCLUSIONS: The in vitro and in silico analyses indicated the potential of M. latifolia as an alternative source of α-amylase and DPP-4 inhibitors. Further pharmacological studies on the compounds are recommended.
OBJECTIVE: The aim of this study is to analyze the multiphase pulsatile blood flow in the left coronary artery tree with stenosis.
METHODS: The 3D left coronary artery model was reconstructed using 2D computerized tomography (CT) scan images. The Red Blood Cell (RBC) and varying hemodynamic parameters for single and multiphase blood flow conditions were analyzed.
RESULTS: Results asserted that the multiphase blood flow modeling has a maximum velocity of 1.017 m/s and1.339 m/s at the stenosed region during the systolic and diastolic phases respectively. The increase in Wall Shear Stress (WSS) observed at the stenosed region during the diastole phase as compared during the systolic phase. It was also observed that the highest Oscillatory Shear Index (OSI) regions are found in the downstream area of stenosis and across the bifurcations. The increase in RBCs velocity from 0.45 m/s to 0.6 m/s across the stenosis was also noticed.
CONCLUSION: The computational multiphase blood flow analysis improves the understanding and accuracy of the complex flow conditions of blood elements (RBC and Plasma) and provides the progression of the disease development in the coronary arteries. This study helps to enhance the diagnosis of the blocked (stenosed) arteries more precisely compared to the single-phase blood flow modeling.
METHODS: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.
RESULTS: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.
CONCLUSION: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
METHODS: This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted.
RESULTS: At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases.
CONCLUSIONS: While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.
METHODS: In this article, the steps involved in importing, segmenting, and registering tomographic images using 3D Slicer were thoroughly described, before importing them into GATE for MC simulation. The absorbed doses estimated using GATE were then compared with that of PM. SlicerRT, a 3D Slicer extension, was then used to visualize the isodose from the MC simulation.
RESULTS: A workflow diagram consisting of all the steps taken in the utilization of 3D Slicer for personalized dosimetry in 90 Y radioembolization has been presented in this article. In comparison to the MC simulation, the absorbed doses to the tumor and normal liver were overestimated by PM by 105.55% and 20.23%, respectively, whereas for lungs, the absorbed dose estimated by PM was underestimated by 25.32%. These values were supported by the isodose distribution obtained via SlicerRT, suggesting the presence of beta particles outside the volumes of interest. These findings demonstrate the importance of personalized dosimetry for a more accurate absorbed dose estimation compared to PM.
CONCLUSION: The methodology provided in this study can assist users (especially students or researchers who are new to MC simulation) in navigating intricate steps required in the importation of tomographic data for MC simulation. These steps can also be utilized for other radiation therapy related applications, not necessarily limited to internal dosimetry.