Comparative study: normotensive and preeclampsia mother presenting with imminent symptoms of eclampsia in third trimester of pregnancy

Background: This prospective study compares the maternal and fetal outcome in normotensive and preeclampsia mother presenting with imminent symptoms of eclampsia in third trimester. This prospective study was conducted in the department of obstetrics and gynaecology, Government Theni Medical College, Tamil Nadu, India in 2019.Methods: A total 100 antenatal mothers were selected for the study. Group A - 50 known case of preeclampsia presented with imminent symptoms. Group B-50 previously normotensive patients present with imminent symptoms of eclampsia. Maternal and fetal outcome were analysed.Results: Incidence of eclampsia - 0.1%, HELLP syndrome - 0.04%, pulmonary edema - 0.06%, PRES - 0.07%, abruptio placenta - 0.14% and maternal death in Group A was 2% and in Group B was 8%. Maternal complications are more in normotensive women (46%) presented with imminent symptoms than in preeclampsia women (26%) with imminent symptoms. Incidence of IUGR in Group A was 46%, whereas in Group B 12%. Incidence of preterm babies in Group A was 18%, whereas in Group B was 42%. Perinatal death incidence was 2.2% in imminent eclampsia.Conclusions: Because known preeclampsia patients were aware of imminent symptoms and presented early to hospital. Early identification and treatment of this dreadful outcome at the imminent state itself can reduce the complications. In current status on preventive aspect of eclampsia, atypical presentation should also be considered for which new screening and diagnostic tools has to be developed

Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images

Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19

Efficient Cost Optimization Algorithm InIaas Cloud by Load Balancing

The distributed computing innovation is quickly growing these days because of its adaptable highlights of programmed arranging, uninhibitedly extending, on-request asset designating, force and value sparing, which just hits the objective of different associations on IT framework development. In the interim, virtualization innovation has firm relationship with distributed computing as a result of the regular idea of virtualization highlight. Virtualization innovation is much of the time used to progressively deal with load adjusting of the cloud framework where it gets conceivable to remap virtual machines (VMs) and physical assets as indicated by the adjustment in load. Nonetheless, in order to understand the least difficult presentation, the virtual machines need to completely use its administrations and assets by adjusting to the distributed computing condition powerfully. The heap adjusting and legitimate portion of assets must be ensured in order to upgrade asset utility. Numerous analysts in the past have proposed distinctive booking and cost enhancing calculations like static, dynamic and blended methodologies like best fit diminishing, first fit diminishing, however they don't ensure ideal arrangements. This task gives a cost decrease system and empowers successful burden balance. The technique utilized in this paper will propel the framework

Analogue Rice as the Vehicle of Public Nutrition Diversity

Analogue rice is artificial rice product made from non-rice raw material by extrusion technique, which can be the vehicle of public nutrition diversity. The objectives of this research were to formulate and characterize analogue rice made from of sorghum, mocaf and other additional material. The method of analogue rice production is by twin screw extruder hot extrusion done in 2013. The research steps were the formulation of analogue rice, sensory evaluation to choose the best formula, and physico-chemical characterization of the best formula. The best two samples that were chosen are analogue rice made from 30% sorghum flour, 15% cornstarch, and 15% arenga starch (analogue rice B) and analogue rice made from 30% mocaf and 30% cornstarch (analogue rice F). Analogue rice B has 21.72% of amylose (medium) with 4% of dietary fiber while analogue rice F has low amylose which is 14.49%, make it more sticky, with 4.21% of dietary fiber

EFFECTS OF AQUEOUS EXTRACT OF GLYCYRRHIZA GLABRA LINN. AND DIOSMETIN ON MODULATION OF SPATIAL MEMORY THROUGH ACETYLCHOLINESTERASE AND BRAIN-DERIVED NEUROTROPHIC FACTOR IN ETHANOL-INDUCED COGNITIVE IMPAIRMENT MODEL RATS

Objective: The objective of this research was to evaluate the cognitive impairment due to excessive consumption of alcohol and memory enhancement action of Glycyrrhiza glabra Linn. (AEGGL) and diosmetin (Dm). Methods: In this study, 36 adult male Wistar rats were divided into the six groups (n=6) and eight-arm radial maze, narrow beam test, and open field behavior parameters were assessed on day 1, 10, and 21. After the 21 days of experiment, animals were sacrificed, and blood samples were collected for serum acetylcholinesterase (AChE) and brain-derived neurotrophic factor (BDNF) estimation. We have also analyzed the morphology of CA3 region of the hippocampus. Results: The results of this study suggested that AEGGL and Dm treatment could be the potential drugs for ethanol-induced cognitive impairment. Conclusion: Ethanol-induced cognitive impairment was recovered by AEGGL and Dm treatment, we suggested that this might be due to anticholinesterase activity and increased synthesis of BDNF levels in the brain. Further, researches are warranted to understand the exact mechanism of action of drugs

Effectiveness of MCH care package on knowledge and attitude regarding male involvement in MCH services among males at selected setting, Chennai - 2011.

A pre-experimental study to assess the effectiveness of MCH Care Package on knowledge and attitude regarding male involvement in the MCH services among males at selected setting, Chennai. OBJECTIVES 1. To assess the pre and post level of knowledge and attitude regarding male involvement in the MCH services among males 2. To assess the effectiveness of MCH Care package on knowledge and attitude regarding male involvement in the MCH services among males 3. To correlate mean differed knowledge score with attitude score. 4. To associate the mean differed level of knowledge and attitude score with selected demographic variables. ASSUMPTIONS 1. Males have a role to play in the maternal and child health services 2. Males may have some knowledge regarding their involvement in the maternal and child health services. 3. The maternal and child health care package may enhance the knowledge and attitude regarding male involvement in the maternal and child health services. 4. Knowledge on male involvement in MCH care may enhance the attitude on male involvement during MCH care practices. NULL HYPOTHESES NH1 - There is no significant difference between pre &posttest level of knowledge and attitude regarding male involvement in the MCH services NH2 - There is no significant relationship between mean differed knowledge and attitude score. NH3 - There is no significant association of the mean improvement level of knowledge and attitude score with the selected demographic variables. DELIMITATION The study was delimited to a period of 4 weeks of data collection

Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine learning model, CNN performed the best on the external dataset (accu-racy 88%) in comparison to the deep learning models, indicating that a light-weight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19

Prevalence and practice of self-medication among undergraduate medical students and non-medical students in south India

Background: Self-medication is commonly practiced worldwide and the irrational use of drugs for self-medication is a major cause of concern. The situation is more complex when a number of prescriptions only medicines are used for self-medication which are easily available over the counter through pharmacies without any prescription. The objective of this study was to assess the prevalence and practice of self-medication among undergraduate medical students and non-medical students.Methods: This cross-sectional questionnaire based study was carried out among 100 undergraduate students of a tertiary care medical college and 100 undergraduate students of an arts and science college in south India. The respondents were selected from the students who were present on the day of study. A pre-tested, self-assessing questionnaire was used to obtain the information on the prevalence and practice of self-medication.Results: Self-medication was practiced by 96% and 92% of medical and nonmedical students respectively. Overall practice of self-medication was 94%. Majority of females were self-medicating than males, 94% and 90% respectively. The most common symptom leading to self-medication among medical students were cough and common cold compared to headache among nonmedical students. The commonly used medicines for self-medication in both the groups were analgesics, antipyretics, cough suppressants and antibiotics. More number of medical students reported the use of antibiotics to treat infections (70%) which was statistically significant.Conclusions: The prevalence and practice of self-medication was alarming in both groups. The use of antimicrobials was also found to be very high among medical students. It is a need of the hour to create better awareness regarding the use of drugs for self-medication, to implement policies to prevent the dispensing of medicines without any prescription which would remain as the cornerstone for reducing the practice of self-medication

Assessment of Genetic Diversity of Seagrass Populations Using DNA Fingerprinting: Implications for Population Stability and Management

Populations of the temperate seagrass, Zostera marina L. (eelgrass), often exist as discontinuous beds in estuaries, harbors, and bays where they can reproduce sexually or vegetatively through clonal propagation. We examined the genetic structure of three geographically and morphologically distinct populations from central California (Elkhorn Slough, Tomales Bay, and Del Monte Beach), using multilocus restriction fragment length polymorphisms (DNA fingerprints). Within-population genetic similarity (Sw) values for the three eelgrass populations ranged from 0.44 to 0.68. The Tomales Bay population located in an undisturbed, littoral site possessed a within-population genetic similarity (Sw = 0.44) that was significantly lower than those of the other two populations. Cluster analysis identified genetic substructure in only the undisturbed subtidal population (Del Monte Beach). Between-population similarity values Sb for all pairwise comparisons ranged from 0.47 to 0.51. The three eelgrass populations show significantly less between locale genetic similarity than found within populations, indicating that gene flow is restricted between locales even though two of the populations are separated by only 30 km. The study demonstrates that (i) natural populations of Z. marina from both disturbed and undisturbed habitats possess high genetic diversity and are not primarily clonal, (ii) gene flow is restricted even between populations in dose proximity, (iii) an intertidal population from a highly disturbed habitat shows much lower genetic diversity than an intertidal population from an undisturbed site, and (iv) DNA fingerprinting techniques can be exploited to understand gene flow and population genetic structure in Z. marina, a widespread and ecologically important species, and as such are relevant to the management of this coastal resource

A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC)

A novel data dispatching or communication technique based on circulating networks of any network IP is suggested for multi data transmission in multiprocessor systems using Networks-On-Chip (NoC). In wireless communication network management have some negatives have heavy data losses and traffic of data sending data while packet scheduling and low performance in the varied network due to workloads. To overcome the drawbacks, in this method proposed system is Integrated Shortest Path Search Algorithm (ISPSA) using mesh topologies. The message is sent to IP (Internet Protocol) in the network until the specified bus accepts it. Integrated Shortest Path Search Algorithm for communication between two nodes is possible at any one moment. On-chip wireless communications operating at specific frequencies are the most capable option for overcoming metal interconnects multi-hop delay and excessive power consumption in Network-on-Chip (NoC) devices. Each node can be indicated by a pair of coordinates (level, position), where the level is the tree's vertical level and the view point is its horizontal arrangement in the sequence of left to right. The output gateway node's n nodes are linked to two nodes in the following level, with all resource nodes located at the bottommost vertical level and the constraint of this topology is its narrow bisection area. The software Xilinx 14.5 tool by using that overall performance analysis of mesh topology, each method are reduced data losses with better accuracy although the productivity of the delay is decreased by 21 % was evaluated and calculated.