Medicinal plants have been considered as promising sources of drugs in treating various cancers. Crinum amabile (C. amabile), a plant species from the Amaryllidaceae family, is claimed to be a potential source for cancer chemotherapeutic compounds. Here, we aimed to investigate the potential of C. amabile as an anticancer agent. Dried leaves of C. amabile were serially extracted and our findings showed that chloroform extract (CE) was shown to exhibit cytotoxic effect against all cancer cell lines used. This active extract was further fractionated in which F5 fraction was shown to possess the highest cytotoxicity among all fractions. F5 fraction was then tested in-depth through Annexin V/FITC apoptosis and DNA fragmentation assays to determine its apoptotic effect on MCF-7 cells. Results revealed that F5 fraction only showed induction of cell apoptosis starting at 72-hour treatment while DNA fragmentation was not detected at any of the concentrations and treatment periods tested. Meanwhile, cell proliferation assay revealed that F5 fraction was able to inhibit normal cell proliferation as well as VEGF-induced cell proliferation of normal endothelial cell (HUVECs). In conclusion, F5 fraction from C. amabile leaf CE was able to exhibit cytostatic effect through antiproliferation activity rather than induction of cell apoptosis and therefore has the potential to be further investigated as an anticancer agent.
Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients. The method is able to segment the brain lesion from the stacked CT images automatically without prior knowledge of the location or the presence of the lesion. The aim is to reduce medical doctors' burden and assist them in making an accurate diagnosis. A case study with 300 sets of CT images from control subjects and stroke patients is conducted. Comparing with other existing methods, the outcome ascertains the effectiveness of the proposed method in detecting brain infarct of stroke patients.
Swietenia macrophylla King (Meliaceae) is used to treat diabetes mellitus in Malaysia. This study aims to evaluate the anti-hyperglycaemic potential of petroleum ether (PE), chloroform (CE) and methanol (ME) extracts of S. macrophylla seeds, in normoglycaemic and streptozotocin (STZ)-induced diabetic rats.
With severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as an emergent human virus since December 2019, the world population is susceptible to coronavirus disease 2019 (COVID-19). SARS-CoV-2 has higher transmissibility than the previous coronaviruses, associated by the ribonucleic acid (RNA) virus nature with high mutation rate, caused SARS-CoV-2 variants to arise while circulating worldwide. Neutralizing antibodies are identified as immediate and direct-acting therapeutic against COVID-19. Single-domain antibodies (sdAbs), as small biomolecules with non-complex structure and intrinsic stability, can acquire antigen-binding capabilities comparable to conventional antibodies, which serve as an attractive neutralizing solution. SARS-CoV-2 spike protein attaches to human angiotensin-converting enzyme 2 (ACE2) receptor on lung epithelial cells to initiate viral infection, serves as potential therapeutic target. sdAbs have shown broad neutralization towards SARS-CoV-2 with various mutations, effectively stop and prevent infection while efficiently block mutational escape. In addition, sdAbs can be developed into multivalent antibodies or inhaled biotherapeutics against COVID-19.
The present study evaluated the antioxidant activity and potential toxicity of 50% methanolic extract of Orthosiphon stamineus (Lamiaceae) leaves (MEOS) after acute and subchronic administration in rats. Superoxide radical scavenging, hydroxyl radical scavenging, and ferrous ion chelating methods were used to evaluate the antioxidant properties of the extract. In acute toxicity study, single dose of MEOS, 5000 mg/kg, was administered to rats by oral gavage, and the treated rats were monitored for 14 days. While in the subchronic toxicity study, MEOS was administered orally, at doses of 1250, 2500, and 5000 mg/kg/day for 28 days. From the results, MEOS showed good superoxide radical scavenging, hydroxyl radical scavenging, ferrous ion chelating, and antilipid peroxidation activities. There was no mortality detected or any signs of toxicity in acute and subchronic toxicity studies. Furthermore, there was no significant difference in bodyweight, relative organ weight, and haematological and biochemical parameters between both male and female treated rats in any doses tested. No abnormality of internal organs was observed between treatment and control groups. The oral lethal dose determined was more than 5000 mg/kg and the no-observed-adverse-effect level (NOAEL) of MEOS for both male and female rats is considered to be 5000 mg/kg per day.
This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.
Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.