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.

Effects of Covid-19 Vaccination during Pregnancy on the Obstetric and Neonatal Outcomes in a Tertiary Health Care Center.

BACKGROUND: Pregnancy is an immuno-compromised state, and pregnant women with COVID-19 are at an increased risk for adverse pregnancy outcomes. Thus, the Center for Disease Control and Prevention (CDC) and the Advisory Committee on Immunization (ACIP) have advocated for COVID-19 vaccination in pregnant women. COVAXIN and COVISHIELD were the vaccines being used in India in the first phase of vaccination, but limited data exist on pregnancy outcomes regarding SARS-CoV-2 vaccines and pregnancy and lactation. MATERIAL AND METHODS: A retrospective study was conducted which included only women who delivered after 24 weeks gestation. Women with an unknown vaccination status or with past or active COVID-19 infection were excluded. Demographic characteristics, maternal and obstetric outcomes, and fetal and neonatal outcomes were compared between the unvaccinated and vaccinated groups. Statistical analysis was done with Chi-square testing and the Fisher exact test using SPSS-26 software. RESULTS: Deliveries before a gestation of 37 weeks were significantly higher in the unvaccinated group compared to the vaccinated group. Rates of vaginal deliveries and preterm deliveries were found to be higher in the unvaccinated population. Women who had taken COVAXIN had a higher rate of adverse events compared to those who had taken COVISHIELD. CONCLUSION: There were no significant differences in adverse obstetric outcomes attributed to vaccine administration between the vaccinated and unvaccinated pregnant women. The beneficial effects of the vaccines in protecting against COVID-19 infection, particularly in pregnancy, outweigh the minor adverse events associated with vaccine administration.

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.

Drug Targets for Cardiovascular-Safe Anti-Inflammatory: In Silico Rational Drug Studies.

Cyclooxygenase-2 (COX-2) plays an important role in memory consolidation and synaptic activity, the most fundamental functions of the brain. It converts arachidonic acid to prostaglandin endoperoxide H2. In contrast, if over-expressed, it causes inflammation in response to cytokine, pro-inflammatory molecule, and growth factor. Anti-inflammatory agents, by allosteric or competitive inhibition of COX-2, alleviate the symptoms of inflammation. Coxib family drugs, particularly celecoxib, are the most famous anti-inflammatory agents available in the market showing significant inhibitory effect on COX-2 activity. Due to high cardiovascular risk of this drug group, recent researches are focused on the investigation of new safer drugs for anti-inflammatory diseases. Natural compounds, particularly, phytochemicals are found to be good candidates for drug designing and discovery. In the present study, we performed in silico studies to quantitatively scrutinize the molecular interaction of curcumin and its structural analogs with COX-2, COX-1, FXa and integrin αIIbßIII to investigate their therapeutic potential as a cardiovascular-safe anti-inflammatory medicine (CVSAIM). The results of both ADMET and docking study indicated that out of all the 39 compounds studied, caffeic acid had remarkable interaction with proteins involved in inflammatory response. It was also found to inhibit the proteins that are involved in thrombosis, thereby, having the potential to be developed as therapeutic agent.