METHODS: Ardisia crispa roots hexane extract (ACRH) was prepared from the plant roots using absolute n-hexane. ACRH was fractionated into quinone-rich fraction (QRF) and further isolated to yield benzoquinonoid compound (BQ), respectively. In vitro experiments using VEGF-induced human umbilical vein endothelial cells (HUVECs) and IL-1β-induced human fibroblast-like synoviocytes for rheumatoid arthritis (HFLS-RA) were performed to evaluate the effects of these samples on VEGF-induced HUVECs proliferation and tube formation, and towards IL-1β-induced HFLS-RA proliferation, invasion, and apoptosis, respectively. Therapeutic concentrations (0.05, 0.5, and 5 μg/mL) tested in this study were predetermined based on the IC50 values obtained from the MTT assay.
RESULTS: ACRH, QRF, and BQ exerted concentration-independent antiproliferative effects on VEGF-induced HUVECs and IL-1β-induced HFLS-RA, with IC50 values at 1.09 ± 0.18, 3.85 ± 0.26, and 1.34 ± 0.16 μg/mL in HUVECs; and 3.60 ± 1.38, 4.47 ± 0.34, and 1.09 ± 0.09 μg/mL in HFLS-RA, respectively. Anti-angiogenic properties of these samples were verified via significant inhibition on VEGF-induced HUVECs tube formation, in a concentration-independent manner. The invasiveness of IL-1β-induced HFLS-RA was also significantly inhibited in a concentration-independent manner by all samples. ACRH and BQ, but not QRF, significantly enhanced the apoptosis of IL-1β-induced HFLS-RA elicited at their highest concentration (5 μg/mL) (P RA's cellular functions in vitro, possibly mediated via their anti-angiogenic effects.
OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.
METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.
RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.
CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.