Enhanced induction of cell cycle arrest and apoptosis via the mitochondrial membrane potential disruption in human U87 malignant glioma cells by aloe emodin.

Aloe emodin, one of the active compounds found in Aloe vera leaves, plays an important role in the regulation of cell growth and death. It has been reported to promote the anti-cancer effects in various cancer cells by inducing apoptosis. However, the mechanism of inducing apoptosis by this agent is poorly understood in glioma cells. This research is to investigate the apoptosis and cell cycle arrest inducing by aloe emodin on U87 human malignant glioma cells. Aloe emodin showed a time- and dose-dependent inhibition of U87 cells proliferation and decreased the percentage of viable U87 cells via the induction of apoptosis. Characteristic morphological changes, such as the formation of apoptotic bodies, were observed with confocal microscope by Annexin V-FITC/PI staining, supporting our viability study and flow cytometry analysis results. Our data also demonstrated that aloe emodin arrested the cell cycle in the S phase and promoted the loss of mitochondrial membrane potential in U87 cells that indicated the early event of the mitochondria-induced apoptotic pathway.

Development of COVID-19 Health-Risk Assessment and Self-Evaluation (CHaSe): a health screening system for university students and staff during the movement control order (MCO).

COVID-19 has triggered a global health crisis. Death from severe respiratory failure and symptoms, including fever, dry cough, sore throat, anosmia, and gastrointestinal disturbances, has been attributed to the disease. Development of screening and diagnosis methods prove to be challenging due to shared clinical features between COVID-19 and other pathologies, such as Middle Eastern respiratory syndrome, severe acute respiratory syndrome, and common colds. This study aims to develop a comprehensive one-stop online public health screening system based on clinical and epidemiological criteria. The immediate target populations are the university students and staff of University Sultan Zainal Abidin and the civil servants of the Malaysian Ministry of Science, Technology, and Innovation. Forty-nine (49) clinical and epidemiological factors associated with COVID-19 were identified and prioritized based on their prevalence via rigorous review of the literature and vetting sessions. A pilot study of 200 volunteers was conducted to assess the extent of risk mitigation of COVID-19 infection among the university students and civil servants using the prototyped model. Consequently, twelve (12) clinical parameters were identified and validated by the medical experts as essential variables for COVID-19 risk-screening. The updated model was then revalidated via real mass-screening of 5000 resulting in the final adopted CHaSe system. Principal component analysis (PCA) was used to confirm the weightage of risk level toward COVID-19 to procures the optimal accuracy, reliability, and efficiency of this system. Twelve (12) factor loadings accountable for 58.287% of the clinical symptoms and clinical history variables with forty-nine (49) parameters of COVID-19 were identified through PCA. The variables of the clinical and epidemiological aspects identified are the C6 (History of joining high-risk gathering (where confirmed cases had been recorded), CH11 [History of contact with confirmed cases (close contact)], CH13 [Duration of exposure with confirmed cases (minutes)] with substantial positive factors of 0.7053, 0.706 and 0.5086, respectively. The contribution toward high-risk infection of COVID-19 was firmly attributable to the variables CH14 [Last contact with confirmed cases (days)], CH13 [Duration of exposure with confirmed cases (minutes)], and S1 (Age). The revalidated PCA for 5000 respondents also yielded twelve significant PCs with a cumulative variance of 58.288%. Importantly, the medical experts have revalidated the CHaSe system for accuracy of all clinical aspects (clinical symptoms and clinical history) and epidemiological links to COVID-19 infection. After revalidating the model for 5000 respondents, the PC variance for PC1, PC2, PC3, and PC4 was 27.36%, 11.79%, 10.347%, and 8.785%, respectively, with the cumulative explanation of 58.288% in data variability. The level of risks detected using the CHaSe system toward COVID-19 provides optimal accuracy, reliability, and efficiency to conduct mass-screening of students and government servants for COVID-19 infection.

Medical Experts' Agreement on Risk Assessment Based on All Possible Combinations of the COVID-19 Predictors-A Novel Approach for Public Health Screening and Surveillance.

During the initial phase of the coronavirus disease 2019 (COVID-19) pandemic, there was a critical need to create a valid and reliable screening and surveillance for university staff and students. Consequently, 11 medical experts participated in this cross-sectional study to judge three risk categories of either low, medium, or high, for all 1536 possible combinations of 11 key COVID-19 predictors. The independent experts' judgement on each combination was recorded via a novel dashboard-based rating method which presented combinations of these predictors in a dynamic display within Microsoft Excel. The validated instrument also incorporated an innovative algorithm-derived deduction for efficient rating tasks. The results of the study revealed an ordinal-weighted agreement coefficient of 0.81 (0.79 to 0.82, p-value < 0.001) that reached a substantial class of inferential benchmarking. Meanwhile, on average, the novel algorithm eliminated 76.0% of rating tasks by deducing risk categories based on experts' ratings for prior combinations. As a result, this study reported a valid, complete, practical, and efficient method for COVID-19 health screening via a reliable combinatorial-based experts' judgement. The new method to risk assessment may also prove applicable for wider fields of practice whenever a high-stakes decision-making relies on experts' agreement on combinations of important criteria.

Expression profile of genes modulated by Aloe emodin in human U87 glioblastoma cells.

Glioblastoma, the most aggressive and malignant form of glioma, appears to be resistant to various chemotherapeutic agents. Hence, approaches have been intensively investigated to targeti specific molecular pathways involved in glioblastoma development and progression. Aloe emodin is believed to modulate the expression of several genes in cancer cells. We aimed to understand the molecular mechanisms underlying the therapeutic effect of Aloe emodin on gene expression profiles in the human U87 glioblastoma cell line utilizing microarray technology. The gene expression analysis revealed that a total of 8,226 gene alterations out of 28,869 genes were detected after treatment with 58.6 µg/ml for 24 hours. Out of this total, 34 genes demonstrated statistically significant change (p<0.05) ranging from 1.07 to 1.87 fold. The results revealed that 22 genes were up-regulated and 12 genes were down-regulated in response to Aloe emodin treatment. These genes were then grouped into several clusters based on their biological functions, revealing induction of expression of genes involved in apoptosis (programmed cell death) and tissue remodelling in U87 cells (p<0.01). Several genes with significant changes of the expression level e.g. SHARPIN, BCAP31, FIS1, RAC1 and TGM2 from the apoptotic cluster were confirmed by quantitative real-time PCR (qRT-PCR). These results could serve as guidance for further studies in order to discover molecular targets for the cancer therapy based on Aloe emodin treatment.