ADMISSION TEST, AMNIOTIC FLUID INDEX, AND COLOR OF LIQUOR IN TERM PREGNANCIES IN ACTIVE LABOR AND THEIR ASSOCIATION WITH LABOR AND PERINATAL OUTCOME

Objective: The aim of the study was to study the admission non-stress test, amniotic fluid index, and color of liquor at term gestation in active labor in all primi gravida and multi gravida irrespective of their medical condition. Methods: An observational study was done from January 2015 to August 2016 on 200 pregnant women who were admitted for labor and delivery. A detailed examination was done and non-stress test, amniotic fluid index (AFI), and color of liquor were studied in active labor. Details of the mode of delivery and condition of the mother and the neonate were assessed at the end of each delivery. Results: The sensitivity of studying all the three parameters is 100% and specificity is 91.91%. The positive predictive value is 85.33%, negative predictive value is 100%, and accuracy is 94.58% with significant p value of <0.001. Conclusion: From this study, we can conclude that studying all the three parameters, that is, admission test, AFI, and color of liquor in term pregnancies is a reliable method to assess perinatal outcome

On Board Diagnostics (OBD) Scan Tool to Diagnose Emission Control System

Climate change has become very important issue the world is facing today. To control impact of climate change and improve quality of life, one of the key factor targeted is vehicular emissions. To control emissions very stringent emission norms are introduced by various government agencies across the world. This called for increased use for electronics in the engines and vehicles. This complicates the matter at service and manufacturers. The engine computer (Electronic Control Unit) with international protocol like OBD is used to control electronic parameters in engines. This review paper describes emission compliance requirement with brief introduction of the OBD system along with scan tool to diagnose the system

Butorphanol for Post-Operative Analgesia - A Comparative Clinical Study with Ketorolac

Introduction: Butorphanol, an opioid derivative has been shown to have, in addition to its analgesic properties, several other advantageous effects like antistressor, sedative and anti-shivering action. The efficacy and safety profile of ketorolac, yet another widely used post-operative analgesic is well documented. This study aims to compare the two analgesics. Aims and objectives: This study was conducted to compare the analgesic efficacy and other effects of butorphanol and ketorolac, administered intramuscularly, in post-operative patients who have undergone lower abdominal and pelvic surgeries. Materials and methods: 50 patients undergoing lower abdominal and pelvic surgeries under general or spinal anaesthesia were randomly divided into two Groups (25 each). At a particular level of post-operative pain, the patients Groups I and II were administered intramuscular ketorolac 30mg and butorphanol 2mg respectively. The analgesic effect was studied using Visual Analogue Scale (VAS) and the verbal category scale. Patients were monitored for the sedative action, respiratory status and other vital parameters for 300 minutes and for other adverse reactions over the next twelve hours. Observations: Butorphanol provided better analgesia within the first two hours of administration, while ketorolac was more effective at 4-5 hours. Better sedative action without any significant respiratory depressant effect was demonstrated with butorphanol. There were no clinically significant hemodynamic fluctuations or adverse reactions with butorphanol or ketorolac. Conclusions: Butorphanol provides better early analgesia as compared to ketorolac with a desirable and safe sedative effect in post-operative patients who have undergone lower abdominal and pelvic surgeries

Hybrid Low Complex near Optimal Detector for Spatial Modulation

In our previous work maximum throughput in multi stream MIMO is analyzed by overcoming the inter antenna interference. To mitigate the Inter antenna interference spatial modulation can be used. Spatial Modulation(SM) aided MIMO systems are the emerging MIMO systems which are low complex and energy efficient. These systems additionally use spatial dimensions for transmitting information. In this paper a low complex detector based on matched filter is proposed for spatial modulation to achieve near maximum likelihood performance while avoiding exhaustive ML search since MF based detector exhibits a considerable reduced complexity since activated transmitting antenna and modulated amplitude phase modulation constellation are estimated separately. Simulation results show the performance of the proposed method with optimal ML detector, MRC and conventional matched filter methods

Myxoedemic coma: an uncommon presentation of Sheehan syndrome

This is a rare case of a 35 year old multiparous female who presented with electrolyte abnormality, hyponatremia in a setting of seizure and moderate pallor. She had a significant past history of childbirth complicated with post-partum haemorrhage after which she developed secondary amenorrhoea and lactation failure. Workup showed suppressed levels of all pituitary hormones and was treated as myxoedemic coma. A diagnosis of Sheehan’s syndrome presenting as myxoedemic coma - a rare but emergency presentation was made

Mutagenic potential of Indian tobacco products

The mutagenic potential of aqueous extracts of masheri (ME), chewing tobacco alone (CTE) and a mixture of chewing tobacco plus lime (CTLE) was tested using the Ames assay. ME exhibited mutagenicity in Salmonella typhimurium TA98 upon metabolic activation with aroclor-1254-induced rat liver S9, while nitrosation rendered it mutagenic in TA100 and TA102. CTE exhibited borderline mutagenicity in the absence or presence of S9 in TA98 and TA100 and after nitrosation in TA102, while nitrosation led to doubling of TA98 and TA100 revertants. In contrast, CTLE exhibited direct mutagenicity in TA98, TA100 and TA102, was mutagenic to TA98 upon S9 addition and induced mutagenic responses in all three tester strains after nitrosation. Experiments using scavengers of reactive oxygen species (ROS) suggested that CTLE-induced oxidat-ive damage in TA102 was mediated by a variety of ROS. The high mutagenic potency of CTLE vis a vis that of CTE may be attributed to changes in the pH leading to differences in the amount and nature of compounds extracted from tobacco. Thus, exposure to a wide spectrum of tobacco-derived mutagcns and promutagens may play a critical role in the development of oral cancer among users of tobacco plus lime

Flow structure of low-density gas jets and gas jet diffusion flames.

Buoyant jets and flames have been a subject of significant research in fluid dynamics. Such flows are influenced by near field instabilities, turbulence, buoyancy, chemistry, heat release, etc. The present research deals with computational and experimental studies to improve understanding of laminar-turbulent jets and flames.As a first step, computational analysis of the near-field flow structure in an isothermal helium jet injected into quiescent ambient air environment was conducted. The jet Reynolds number, Re was varied from 40 to 150 to encompass steady and oscillating jet flow regimes. At low jet Reynolds numbers, the flow was steady and the concentration shear layer at the tube exit was stratified by mixing between jet and ambient fluids inside the tube. At higher jet Reynolds numbers (Re = 90 and 150), buoyancy induced acceleration contracted the jet core to form a toroidal vortex by entrainment of the ambient fluid. Next, the effects of buoyancy on buoyant and inertial low-density gas jets were studied by initiating computations in Earth gravity and subsequently, reducing the gravity to simulate microgravity conditions in the 2.2 s drop tower.As a successive step towards development of advanced optical diagnostic systems for measuring fluid flow phenomena in small scale turbulent structures, a miniature rainbow schlieren deflectometry system to non-intrusively measure species concentration and temperature data across the whole field was developed. The capability of the system was demonstrated by obtaining concentration measurements in a helium micro-jet (diameter, d = 650 mum) and temperature and concentration measurements in a hydrogen jet diffusion flame from a micro-injector (d = 50 mum). Finally, the flow field of under-expanded nitrogen jets was visualized to reveal details of the shock structures existing downstream of the jet exit.From an experimental perspective, in order to facilitate turbulence measurements, a crossbeam rainbow schlieren deflectometry system was developed and demonstrated by presenting schlieren visualizations of turbulent jets and flames. Subsequently, the theoretical framework of the crossbeam correlation technique requiring assumptions of homogeneous and isotropic turbulence was presented. The validity of the technique was also verified using laminar and turbulent data generated synthetically. The limitations of the technique were also discussed

Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning

Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning

Covid-19 Detection For CT-scan Images Using Transfer Learning Models

COVID-19 is a respiratory illness caused by a virus called SARS-CoV-2 which affected around 455 million people around the world. CT-scan is a medical imaging technique that uses X-rays to create detailed images of the body and which can be used to detect many respiratory diseases. Transfer learning models are a type of machine learning model that are trained on a large dataset of images and which can be used for their already trained ability to extract features from image in other tasks. They can then be used to classify new images with similar features.This paper presents a study of different transfer learning models for the task of classifying chest X-ray images into three classes: COVID-19, pneumonia, and normal. The study was implemented using Python and the dataset used was the COVID-19 Chest X-ray Dataset. The train-test split used was 0.2–0.8. The parameters used to test the models were the precision, recall, accuracy, F1 score, and Matthew’s correlation score. Other than these, different optimizers were also compared such as ADAM, SGD with different learning rates of 0.01, 0.001, and 0.0001.The models used in this study are EfficientNetB0, EfficientNetB7, VGG16, and InceptionV3. Out of these models, the most effective model was the EfficientNetB0 model, which achieved an accuracy of 98.6%. This study provides valuable insights into the use of transfer learning for medical image analysis. The results suggest that transfer learning can be used to develop accurate and efficient models that can be used as a secondary option for the diagnosis of COVID-19 using chest X-ray images

Heart Disease Prediction using Different Machine Learning Algorithms

Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab.  The performance evaluation parameters used here are Accuracy, precision, recall and Fi-score. Training and testing are implemented for different ratios such as 60:40, 70:30 and 80:20. From the analysis and comparisons of evaluation parameters of all the above algorithms, XG-Boost is having the highest accuracy and recall value. KNN having worst accuracy and recall amongst all. XG-Boost is having a training accuracy of 98.86, 98.74 and 97.68 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. XG-Boost is having a testing accuracy of 95.85, 95.45 and 96.09 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. So, XG-Boost algorithm can be used for obtaining the best prediction for heart disease.  This type of heart disease prediction can be used as a secondary diagnostic tool for doctors, for best and fast prediction. This can help the early prediction of heart disease thus increasing the chances of the saving the life heart patient