Alzheimer's disease (AD) is an increasingly prevalent neurological disorder of the central nervous system. There is growing evidence that amyloidogenesis is a pathological hallmark for AD; this leads to the formation of senile plaques. Naringenin is a bioflavonoid which has neuroprotective effects through its antioxidant and anti-inflammatory properties. However, its clinical usage is limited due to its inefficient transport across biological membranes. In the present study, a naringenin nanoemulsion was prepared and its neuroprotective effects were tested against β-amyloid induced neurotoxicity in a human neuroblastoma cell line (SH-SY5Y). The optimised, naringenin-loaded nanoemulsion formulation had a droplet size of 113.83 ± 3.35 nm and around 50 nm, as assessed respectively by photon correlation spectroscopy and transmission electron microscopy. The preparation showed a low polydispersity index (0.312 ± 0.003), a high zeta potential (12.4 ± 1.05) and a high percentage transmittance (97.01%). The neuroprotective activity of naringenin nanoemulsions was determined by assessing their ability to protect SH-SY5Y neuroblastoma cells against the neurotoxic effect of beta amyloid (Aβ). Aβ-induced production of reactive oxygen species (ROS), amyloid precursor protein (APP), β-secretase (BACE), total tau and phosphorylated tau (pT231) was also determined. The naringenin loaded nanoemulsion significantly alleviated the direct neurotoxic effects of Aβ on SH-SY5Y cells; this was associated with a down-regulation of APP and BACE expression, indicating reduced amyloidogenesis. Furthermore, it decreased the levels of phosphorylated tau in SH-SY5Y cells exposed to Aβ. These results suggest that a naringenin-loaded nanoemulsion could be a promising agent for the treatment of Alzheimer's disease.
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.