Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning

Recently, COVID-19 infection has been spread to a wider human population worldwide and deemed a pandemic for its rapidity. The absence of medicine or immunization for the “COVID-19” illness, along with the requirement for early discovery and isolation of affected persons, is critical in reducing the risk of infection in healthy population. Blood specimens, or “RT-PCR” are primary screening technique for “COVID-19”. However, average positive “RT-PCR” is expected as 30 to 60%, leading to undiscovered infections and potentially endangering a broad population of healthy persons with infectious symptoms. With the quick examination approach, chest radiography as a common approach for identifying respiratory disorders is straightforward to execute. A board-certified radiologist indicated the presence of disease in these radiographs. Four transfer learning techniques to COVID-19 illness identification were trained using 2,000 X-rays: VGG-16, GoogleNet, ResNet, and SqueezeNet. The result of the experimental assessment shows that the VGG-16 network fine-tuned with Keras achieved sensitivity of 100% with specificity of 98.5% and accuracy of approximately 99.3%

Formulation and pharmacodynamic evaluation of meloxicam liquisolid compacts

The purpose of this study was to improve the meloxicam dissolution rate through its formulation into liquisolid compacts and then to evaluate the in vitro and in vivo performance of the prepared liquisolid compacts. Dissolution efficiency, mean dissolution time and relative dissolution rate of liquisolid compacts were calculated and compared to marketed formulation. The degree of interaction between the ME and excipients was studied by differential scanning calorimetry and X-ray diffraction were used and results revealed that, there was a loss of meloxicam crystallanity upon liquisolid formulation and almost molecularly dispersed state, which contributed to the enhanced drug dissolution properties. The optimized liquisolid compact showed higher dissolution rates and dissolution efficiency compared to commercial product. The analgesic and anti inflammatory response of optimized liquisolid compact in Swiss albino mice and Wistar rats was found to be superior compared to the marketed formulation.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Early Life Child Micronutrient Status, Maternal Reasoning, and a Nurturing Household Environment have Persistent Influences on Child Cognitive Development at Age 5 years : Results from MAL-ED

Funding Information: The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED) is carried out as a collaborative project supported by the Bill & Melinda Gates Foundation, the Foundation for the NIH, and the National Institutes of Health/Fogarty International Center. This work was also supported by the Fogarty International Center, National Institutes of Health (D43-TW009359 to ETR). Author disclosures: BJJM, SAR, LEC, LLP, JCS, BK, RR, RS, ES, LB, ZR, AM, RS, BN, SH, MR, RO, ETR, and LEM-K, no conflicts of interest. Supplemental Tables 1–5 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Address correspondence to LEM-K (e-mail: [email protected]). Abbreviations used: HOME, Home Observation for Measurement of the Environment inventory; MAL-ED, The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project; TfR, transferrin receptor; WPPSI, Wechsler Preschool Primary Scales of Intelligence.Peer reviewe

Early life child micronutrient status, maternal reasoning, and a nurturing household environment have persistent influences on child cognitive development at age 5 years: Results from MAL-ED

Background: Child cognitive development is influenced by early-life insults and protective factors. To what extent these factors have a long-term legacy on child development and hence fulfillment of cognitive potential is unknown. Objective: The aim of this study was to examine the relation between early-life factors (birth to 2 y) and cognitive development at 5 y. Methods: Observational follow-up visits were made of children at 5 y, previously enrolled in the community-based MAL-ED longitudinal cohort. The burden of enteropathogens, prevalence of illness, complementary diet intake, micronutrient status, and household and maternal factors from birth to 2 y were extensively measured and their relation with the Wechsler Preschool Primary Scales of Intelligence at 5 y was examined through use of linear regression. Results: Cognitive T-scores from 813 of 1198 (68%) children were examined and 5 variables had significant associations in multivariable models: mean child plasma transferrin receptor concentration (β: −1.81, 95% CI: −2.75, −0.86), number of years of maternal education (β: 0.27, 95% CI: 0.08, 0.45), maternal cognitive reasoning score (β: 0.09, 95% CI: 0.03, 0.15), household assets score (β: 0.64, 95% CI: 0.24, 1.04), and HOME child cleanliness factor (β: 0.60, 95% CI: 0.05, 1.15). In multivariable models, the mean rate of enteropathogen detections, burden of illness, and complementary food intakes between birth and 2 y were not significantly related to 5-y cognition. Conclusions: A nurturing home context in terms of a healthy/clean environment and household wealth, provision of adequate micronutrients, maternal education, and cognitive reasoning have a strong and persistent influence on child cognitive development. Efforts addressing aspects of poverty around micronutrient status, nurturing caregiving, and enabling home environments are likely to have lasting positive impacts on child cognitive development.publishedVersio

Data Mining Techniques for Earthquake Frequency-Magnitude Analysis and Seismic Zone Estimation

Abstract

An Artificial Intelligence-based Crop Recommendation System using Machine Learning

558-567Agriculture is the backbone of the Indian economy and a source of employment for millions of people across the globe. The perennial problem faced by Indian farmers is that they do not select crops based on environmental conditions, resulting in significant productivity losses. This decision support system assists in resolving this issue. In our study, the AI system helps precision agriculture improve overall crop harvest quality and accuracy. This research feature selection, Industry 4.0, proposes one solution, such as a recommendation system, using AI and a family of machine learning algorithms. The data set used in this research work is downloaded from Kaggle, and labeled. It contains a total of 08 features with 07 independent variables, including N, P, K, Temperature, Humidity, pH, and rainfall. Then SMOTE data balancing technique is applied to achieve better results. Additionally, authors used optimization techniques to tune the performance further as smart factories. Cat Boosting (C-Boost) performed the best with an accuracy value of 99.5129, F-measure-0.9916, Precision-0.9918, and Kappa-0.8870. GNB, on the other hand, outperformed ROC-0.9569 and MCC-0.9569 in the classification, regression, and boosting family of machine learning algorithms

Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning

241-248Recently, COVID-19 infection has been spread to a wider human population worldwide and deemed a pandemic for its rapidity. The absence of medicine or immunization for the “COVID-19” illness, along with the requirement for early discovery and isolation of affected persons, is critical in reducing the risk of infection in healthy population. Blood specimens, or “RT-PCR” are primary screening technique for “COVID-19”. However, average positive “RT-PCR” is expected as 30 to 60%, leading to undiscovered infections and potentially endangering a broad population of healthy persons with infectious symptoms. With the quick examination approach, chest radiography as a common approach for identifying respiratory disorders is straightforward to execute. A board-certified radiologist indicated the presence of disease in these radiographs. Four transfer learning techniques to COVID-19 illness identification were trained using 2,000 X-rays: VGG- 16, GoogleNet, ResNet, and SqueezeNet. The result of the experimental assessment shows that the VGG-16 network finetuned with Keras achieved sensitivity of 100% with specificity of 98.5% and accuracy of approximately 99.3%