Background
Hand, foot, and mouth disease (HFMD) is a viral infection caused by a virus from the enterovirus genus of picornavirus family that majorly affects children. Though most cases of HFMD do not cause major problems, the outbreaks of Enterovirus 71 (EV71) can produce a high risk of neurological sequelae, including meningoencephalitis, lung difficulties, and mortality. In Asia, HFMD caused by EV71 has emerged as an acutely infectious disease of highly pathogenic potential, which demands the attention of the international medical community.
Main body of the abstract
Some online databases including NCBI, PubMed, Google Scholar, ProQuest, Scopus, and EBSCO were also accessed using keywords relating to the topic for data mining. The paid articles were accessed through the Centre Library facility of Siksha O Anusandhan University. This work describes the structure, outbreak, molecular epidemiology of Enterovirus 71 along with different EV71 vaccines. Many vaccines have been developed such as inactivated whole-virus live attenuated, subviral particles, and DNA vaccines to cure the patients. In Asia–Pacific nations, inactivated EV71 vaccination still confronts considerable obstacles in terms of vaccine standardization, registration, price, and harmonization of pathogen surveillance and measurements.
Short conclusion
HFMD has emerged as a severe health hazard in Asia–Pacific countries in recent decades. In Mainland China and other countries with high HFMD prevalence, the inactivated EV71 vaccination will be a vital tool in safeguarding children's health. When creating inactivated EV71 vaccines, Mainland China ensured maintaining high standards of vaccine quality. The Phase III clinical studies were used to confirm the safety and effectiveness of vaccinations.
Graphical Abstract
Calotropis procera and Calotropis gigantea are medicinal plant having therapeutic value. The leaf extracts of C. procera have been investigated, its pharmacological actions in detail and leaf extracts of C. gigantea were not studied till date. The objective of present work was to find the bioactive constituents present in the ethanolic leaf extract of C. procera and C. gigantea to evaluate their antibacterial and anifungal activities. The major phytochemical groups in C. procera ethanolic leaf extracts were fatty acid ethyl ester (21.36%), palmitic acid ester (10.24%), linoleic acid (7.43%) and amino acid (8.10%) respectively, whereas ethanolic leaf extracts of C. gigantea contain palmitic acid (46.01%), diterpene (26.53%), triterpene (17.39%), linoleic acid (5.13%) as the major phytochemical groups. Ethanol extract of C. procera leaves showed the highest inhibition (11 mm) against Escherichia coli, while ethanolic extract of C. gigantea leaves inhibited Klebsiella (20 mm). These findings will use in new directions in pharmacological investigations.
The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent ‘curcumin.’ Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.
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