METHODS: Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist, systematic literature searches were performed in the PubMed, Scopus, and Web of Science databases from the earliest date available to June 2023. Data from observational studies in English that described the association between CVD and PCS in adults (≥ 18 years old) were included. A minimum of two authors independently performed the screening, study selection, data extraction, data synthesis, and quality assessment (Newcastle-Ottawa Scale). The protocol of this review was registered under PROSPERO (ID: CRD42023440834).
RESULTS: In total, 594 studies were screened after duplicates and non-original articles had been removed. Of the 11 included studies, CVD including hypertension (six studies), heart failure (three studies), and others (two studies) were significantly associated with PCS development with different factors considered. The included studies were of moderate to high methodological quality.
CONCLUSION: Our review highlighted that COVID-19 survivors with pre-existing CVD have a significantly greater risk of developing PCS symptomology than survivors without pre-existing CVD. As heart failure, hypertension and other CVD are associated with a higher risk of developing PCS, comprehensive screening and thorough examinations are essential to minimise the impact of PCS and improve patients' disease progression.
METHODS: ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models' classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students' academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.
CONCLUSION: The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.
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.