Melatonin, a multifunctional signaling molecule, is ubiquitously distributed in different parts of a plant and responsible for stimulating several physiochemical responses against adverse environmental conditions in various plant systems. Melatonin acts as an indoleamine neurotransmitter and is primarily considered as an antioxidant agent that can control reactive oxygen and nitrogen species in plants. Melatonin, being a signaling agent, induces several specific physiological responses in plants that might serve to enhance photosynthesis, growth, carbon fixation, rooting, seed germination and defense against several biotic and abiotic stressors. It also works as an important modulator of gene expression related to plant hormones such as in the metabolism of indole-3-acetic acid, cytokinin, ethylene, gibberellin and auxin carrier proteins. Additionally, the regulation of stress-specific genes and the activation of pathogenesis-related protein and antioxidant enzyme genes under stress conditions make it a more versatile molecule. Because of the diversity of action of melatonin, its role in plant growth, development, behavior and regulation of gene expression it is a plant’s master regulator. This review outlines the main functions of melatonin in the physiology, growth, development and regulation of higher plants. Its role as anti-stressor agent against various abiotic stressors, such as drought, salinity, temperatures, UV radiation and toxic chemicals, is also analyzed critically. Additionally, we have also identified many new aspects where melatonin may have possible roles in plants, for example, its function in improving the storage life and quality of fruits and vegetables, which can be useful in enhancing the environmentally friendly crop production and ensuring food safety.

The whole world is concerned about the pandemic of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), due to fatality of this condition. This has become a public health emergency of international concern. No specific vaccine and medicine have proven effective in large-sized trials at this time. With the rapidly increasing number of positive cases and deaths, there is a dire need for effective treatments and an effective vaccine for prevention. An urgent unmet need led to the planning and opening of multiple drug development trials for treatment and vaccine development. In this article, we have summarized data on cell receptor interactions and data on prospects of new vaccines targeting the deoxyribonucleic acid (DNA), messenger ribonucleic acid (mRNA), and viral minigenes. We have tabulated the available data on various clinical trials testing various aspects of COVID-19 vaccines.

The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.

People of all age groups have been affected worldwide during the coronavirus disease 2019 (COVID-19) pandemic. While the global efforts of researchers, clinicians, and scientists are underway, cases involving multiple systems with a wide range of presentations are on the horizon. As health organizations have started warnings about unusual manifestations of a Kawasaki disease (KD)-like inflammatory syndrome associated with COVID-19, some pediatric cardiologists noted that even classic cases are likely going undercounted. Here we report a case of a previously healthy eight-year-old Pakistani boy who presented with a four-day history of low-grade fever. The patient was admitted and diagnosed with COVID-19-associated atypical KD in the setting of fever for more than five days, maculopapular eruptions, and mild conjunctivitis. He screened positive for COVID-19 with an immunoglobulin G titer of 2.1 plus ruling out other childhood illnesses. He was managed with intravenous immunoglobulins and aspirin with gradual resolution of symptoms. His initial echocardiogram was unremarkable. He was discharged home on day six with a follow-up at two weeks.

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