Deep neural network (DNN) modelling for prediction of the mode of delivery.

One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work, we suggested a computerized decision-making process for deciding on the best birthing style. The data was collected from 101 pregnant subjects who were admitted to hospital in eastern India for delivery from January 2021 to September 2021.The data set had 101 instances & 11 variables. The response was a binary variable with "caesarean" & "vaginal" as the outputs. A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. The results support the use of these algorithms in the creation of a clinical decision system to help gynaecologists choose the most appropriate delivery method.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil).

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery.

Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson's algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

Global emergence of Enterovirus 71: a systematic review.

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.

Risk of Late-Onset Alzheimer's Disease by Plasma Cholesterol: Rational In Silico Drug Investigation of Pyrrole-Based HMG-CoA Reductase Inhibitors.

Alzheimer's disease (AD), a worldwide renowned progressive neurodegenerative disorder, is the most common cause of dementia. There are several studies on the important role of cholesterol metabolism in AD pathogenesis, which indicated that the high concentrations of serum cholesterol increase the risk of AD. Biosynthesis of the plasma cholesterol and other isoprenoids is catalyzed by 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) through the conversion of HMG-CoA to mevalonic acid in mevalonate pathway. Normally, the high level of plasma cholesterol is downregulated by HGMCR inhibition as the result of degradation of LDL, but in abnormal conditions, for example, high blood glucose, the HMGCR over activated resulting in uncontrolled blood cholesterol. Selective HMGCR inhibitor drugs such as statins, which increase the catabolism of plasma LDL and reduce the plasma concentration of cholesterol, have been investigated as a possible treatment for AD. In the present study, we have identified the binding modes of 22 various derivatives of 3-sulfamoylpyrroles 16, prepared via a [3 + 2] cycloaddition of a münchnone with a sulfonamide-substituted alkyne, by using efficient biocomputational tools. Out of 22, 5 ligands, with code numbers 5b, 5c, 5d, 5i, and 5j, possessed most absorption, distribution, metabolism, and excretion (ADME) and toxicity profiles in acceptable ranges. Among ligands, 5j (sodium (3R,5R)-7-(3-(N,N-dimethylsulfamoyl)-5-(4-fluorophenyl)-2-isopropyl-4-phenyl-1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate) could inhibit HMGCR enzyme in inhibitory binding site with affinity value -12.17 kcal/mol and binding energy -94.10 kcal/mol through 5 hydrogen bonds. It showed the best ADME and toxicity profiling and higher affinity values than other potent candidate and market drugs such as atorvastatin and rosuvastatin. Therefore, it is suggested for further in vivo investigation, the druggability of 5j and its cholesterol regulatory impact on AD.

Chemometric profile & antimicrobial activities of leaf extract of Calotropis procera and Calotropis gigantea.

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.

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

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.

Rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga through tissue culture

An efficient protocol has been established for rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga L, a rare medicinal plant. Different plant growth regulators like Benzyladenine (BA), Indoleacetic acid (IAA), Indolebutyric acid (IBA), Napthaleneacetic acid (NAA) and adenine sulphates (Ads) have been tried for induction of multiple shoots using lateral bud of rhizome as explants. The highest rate of shoot multiplication (11.5 +/- 0.6) shoot/explant as well as leaf biomass production (7.4 +/- 0.3) gram/explant was observed on Murashige and Skoog medium supplemented with Benzyladenine (1 mg/l) and Indoleacetic acid (0.5 mg/l). Data of shoot multiplication and leaf biomass production were statistically analysed. Upon excission of leaves after 2 months of culture under sterile condition, the base of each plantlet was transferred to fresh media which could produce the same leaf biomass within another 2 months in a 50 ml culture tube containing 20 ml and 250 ml conical flasks containing 30 ml Murashige and Skoog medium. The rate of multiplication and leaf biomass production remained unchanged in subsequent subcultures. The regenerated plantlets were acclimatized in greenhouse and subsequently transferred to the field. Survival rate of the plantlets under ex vitro condition was 95 percent. Genetic fidelity of the regenerants was confirmed using random amplified polymorphic DNA (RAPD) marker. The protocol could be commercially utilized for large scale production of true-to-type plantlets and as an alternative method of leaf biomass production in Kaempferia galanga.

Mining and characterization of EST derived microsatellites in Curcuma longa L.

Turmeric (Curcuma longa L.) (Family: Zingiberaceae) is a perennial rhizomatous herbaceous plant often used as a spice since time immemorial. Turmeric plants are also widely known for its medicinal applications. Recently EST-derived SSRs (Simple sequence repeats) are a free by-product of the currently expanding EST (Expressed Sequence Tag) databases. SSRs have been widely applied as molecular markers in genetic studies. Development of high throughput method for detection of SSRs has given a new dimension in their use as molecular markers. A software tool SciRoKo was used to mine class I SSR in Curcuma EST database comprising 12953 sequences. A total of 568 non-redundant SSR loci were detected with an average of one SSR per 14.73 Kb of EST. Furthermore, trinucleotide was found to be the most abundant repeat type among 1-6-nucleotide repeat types. It accounted for 41.19% of the total, followed by the mononucleotide (20.07%) and hexanucleotide repeats (15.14%). Among all the repeat motifs, (A/T)n accounted for the highest proportion followed by (AGG)n. These detected SSRs can be greatly used for designing primers that can be used as markers for constructing saturated genetic maps and conducting comparative genomic studies in different Curcuma species.