Pharmacotherapeutic Potential of Bitter Gourd (Momordica charantia) in Age-related Neurological Diseases.

Due to the growth of the elderly population, age-related neurological disorders are an increasing problem. Aging begins very gradually and later leads to several neurological issues such as lower neurotransmitter levels, oxidative stress, neuronal inflammation, and continual neuronal loss. These changes might contribute to brain disorders such as Alzheimer's disease (AD), dementia or mild cognitive impairment, and epilepsy and glioma, and can also aggravate these disorders if they were previously present. Momordica charantia (bitter gourd), a member of the Cucurbitaceae family, is a good source of carbohydrates, proteins, vitamins, and minerals. It is used for diabetes and known for its hypoglycemic and antioxidant effects. In this review, we discuss the pharmaceutical effects of M. charantia on age-related neurological disorders. We searched several databases, including PubMed and Google Scholar, using MeSH terms. We searched articles published up until 2022 regardless of publication language. M. charantia is rich in luteolin, which increases acetylcholine in neurons by binding to enzymes in acetylcholine metabolism pathways, including butyrylcholinesterase and acetylcholinesterase. This binding inhibits the hyperphosphorylation of tau protein by restraining its kinase enzyme. Furthermore, this substance can lower serum cholesterol and has multi-target activity in AD and memory loss. M. charantia can also improve memory by decreasing tau protein and it also has potent antioxidant activity and anti-inflammatory effects. This review highlights that M. charantia has effects on many age-related neurological disorders, and can be a cost-effective supplement with minimal side effects.

Relief Supply Chain Management Using Internet of Things to Address COVID-19 Outbreak.

Nowadays, due to the COVID-19 outbreak, the most significant factor to be considered all over the world is to manage this pandemic and especially to address positive cases, efficiently and effectively. This can be achieved by simultaneously utilizing the present network with supply chain settings and also the Internet of Things (IoT). This consideration enables the accurate monitoring of suspected cases in real-time to optimize total service time. Hence, this paper firstly designs two sub-models to minimize distance and traffic while minimizing total response time. Our main contribution in this paper is to develop a dynamic scheme using IoT to deal with suspected cases. We also investigate the proposed methodology on a real case problem in Canada. A comprehensive analysis of the proposed methodology behavior has been conducted and the results showed the managerial decision-making process to address COVID-19 patients. The proposed approach establishes efficient strategies to identify suspicious COVID-19 cases and provide them with medical observance in a short time when utilized with IoT. The obtained results of the considered scenarios show 12% up to 15% improvement in the ambulance response time when using IoT.

Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic.

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient's condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.