Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis

Natural Tragedy Commendation Hasty Alert Using Tweet Events Over Distributed Processing Framework

An Event processing is the scheme of streams that related with information (data) about things that happen (events), and deriving a conclusion from tweet in real time. Twitter is a social network platform that consists of billions of users all over the world where people collaborate and Share information related to real world events. An important characteristic of Twitter is its real-time nature and also investigate the real-time interaction of events such as cyclones in Twitter and propose a framework to monitor tweets to detect a target event. These large scales tweet data processing are done by placing those tweet events in a distributed system. The server processes the tweet queue and executes the operations based on it. An devise classifier of tweets based on features such as the keywords in a tweet, the number of character, the number of words, and their context. The status update which almost pinpoints what is happening in and around an individual user and also tracks the user location. This small content with real world information when processed with some statistical tool may assist us to predict a live occurring event (e.g. cyclone) and regard each twitter user as a feeler and apply particle filtering, which are widely used for location estimation. Tweet in the message queue is done by Apache Kafka which is a distributed publish-subscribe messaging queue system. These frameworks will parallelize our computations over a cluster of machines

A Study of Serum Paraoxonase-1 Activity in Patients with Metabolic Syndrome

BACKGROUND: Metabolic syndrome leads to prothrombotic & proinflammatory state that result in LDL oxidation which plays an important role in the development of Atherosclerosis. In normal individuals, High Density Lipoprotein inhibits the oxidation of LDL.Serum Paraoxonase-1 is an HDL associated enzyme capable of hydrolyzing oxidized phospholipids & various substrates.Thus, estimation of PON-1 activity in metabolic syndrome is valuable in assessing the risk for atherosclerosis and thereby predicting future cardiovascular complications. AIMS AND OBJECTIVES: 1. To estimate the levels of serum Paraoxonase-1 in patients with metabolic syndrome and to compare the levels with healthy controls. 2. To evaluate the association of Paraoxonase -1 activity & malondialdehyde levels with components of metabolic syndrome. MATERIALS AND METHODS: PON-1 activity was estimated using the paraoxon (O,O diethyl-O-4 nitro phenyl phosphate) as the substrate for hydrolysis. Fasting Blood glucose was estimated by GOD-POD method. The serum triglycerides were measured by enzymatic (GPO-PAP method).For the determination of total cholesterol, an enzymatic (GPO-PAP) method was used.HDL was measured by Phosphotungstic acid method .VLDL & LDL were calculated by friedwalds formula.Estimation of Malondialdehyde (MDA) by Thio Barbituric Acid reactivity assay method. RESULTS: The serum PON1 activity among the Study group (59.32 ± 19 U/L) is significantly lower than the control group (154.84 ± 30.71 U/L). The Serum Malondialdehyde values in study group (7.81 ± 3.8 μmol/L) is significantly increased, than that of control group (2.809 ± 1.40). As the number of components of Metabolic Syndrome increases mean PON 1 Activity decreases & mean MDA levels increases. CONCLUSION: This study shows that there is a significant decrease in PON-1 activity in Metabolic Syndrome group and there is significant increase in Malondialdehyde. Since PON-1 is an antiatherogenic and antioxidant enzyme, associated with HDL, reduction in PON-1 activity in Metabolic Syndrome, may play an important role in causation of premature atherosclerosis. Based on the results obtained the present study shows that PON-1 activity may be used as a useful marker for early prediction of atherosclerosis in Metabolic Syndrome

Poboljšanje slike i vrednovanje radnih značajki korištenjem raznih filtara na daljinski mjerene podatke IRS-P6 satelita Liss IV

This paper presents fast and effective filtering techniques for image enhancement from remote sensing Indian remote sensing satellite P6 Liss IV remotely sensed data like Near-Infrared band. There are four filtering techniques used for image enhancement based on spatial domain filters and frequency domain filters such as median filter, wiener filter, bilateral filter and Gaussian homomorphic filter and selected noises salt and pepper and Gaussian noise used with filter. Selected images tested with each filter and based on PSNR performance metric value and best filtering technique identified from these filters. Finally, Gaussian homomorphic filtering technique is suitable for image enhancement of the Liss IV remotely sensed Near-Infrared band. Image enhancement technique is preprocessing for future work such as edge detection and image segmentation.U radu su prikazane brze i učinkovite tehnike filtriranja za poboljšanje slike iz podataka u bliskom infracrvenom području dobivenih indijskim satelitom za daljinska istraživanja P6 Liss IV. Korištene su četiri tehnike filtriranja temeljene na filtrima u prostornoj i frekvencijskoj domeni kao što su: medijan filtar, Wiener filtar, bilateralni filtar i gaussovski homomorfni filtar uz odabrane šumove “salt and pepper” i gaussovski šum s filtrom. Odabrane slike testirane su sa svakim od filtera te je na temelju metričke vrijednosti PSNR (Peak Signal Noise Ratio) radne značajke prepoznata najbolja tehnika filtriranja. Konačno se pokazalo da je gaussovska homomorfna tehnika filtriranja prikladna za poboljšanje slika dobivenih pomoću satelita Liss IV u bliskom infracrvenom području. Tehnika poboljšanja slike je predobrada za budući rad, kao što je detekcija ruba i segmentacija slike

IN VITRO AND IN SILICO APPROACHES ON THE ANTIBACTERIAL ACTIVITY OF TINOSPORA CORDIFOLIA METHANOLIC STEM EXTRACT

Objective: The objective of the study was to evaluate the antibacterial activity of methanolic stem fraction of Tinospora cordifolia against Escherichia coli and Staphylococcus aureus by in vitro and in silico approaches. Methods: In agar disc diffusion method, the inhibitory zone produced by various concentrations of the fraction showed a dose-dependent inhibition pattern. Minimum inhibitory concentration (MIC) values were calculated by broth dilution method. The total DNA present in the fraction treated bacterial cultures was estimated and compared with control DNA. The two-dimensional and three-dimensional structures of the gas chromatography– mass spectrometry (GC–MS) identified compounds were generated using ChemSketch tool. The docking studies were performed for analyzing the receptor and ligand interactions. Results: The higher zone revealed the maximum inhibition of the growth of bacteria that were ranged from 2 mm to 6 mm for E. coli and 1.5 mm to 6.3±0.29 mm for S. aureus. MIC values showed that 30 μg/ml of the fraction was found as the effective dose. The DNA content isolated from the treated culture of both the strains was comparatively lesser than that of the untreated control culture. The GC–MS data analysis depicted the presence 15 major components in the fraction and the sharp peaks were obtained at time intervals 17.50, 20.27, 30.06, etc. Conclusion: Thus, methanolic stem fraction of T. cordifolia possesses promising therapeutic activity against the urinary tract infection pathogens such as E. coli and S. aureus and a further exploration in the isolation and characterization such as plant-derived phytoconstituents would open up new ventures in the field of antibacterial drug discovery

The ORNATE India project: Building research capacity and capability to tackle the burden of diabetic retinopathy-related blindness in India

The ORNATE India project is an interdisciplinary, multifaceted United Kingdom (UK)–India collaborative study aimed to build research capacity and capability in India and the UK to tackle the burden of diabetes-related visual impairment. For 51 months (October 2017–December 2021), this project built collaboration between six institutions in the UK and seven in India, including the Government of Kerala. Diabetic retinopathy (DR) screening models were evaluated in the public system in Kerala. An epidemiological study of diabetes and its complications was conducted through 20 centers across India covering 10 states and one union territory. The statistical analysis is not yet complete. In the UK, risk models for diabetes and its complications and artificial intelligence-aided tools are being developed. These were complemented by joint studies on various aspects of diabetes between collaborators in the UK and India. This interdisciplinary team enabled increased capability in several workstreams, resulting in an increased number of publications, development of cost-effective risk models, algorithms for risk-based screening, and policy for state-wide implementation of sustainable DR screening and treatment programs in primary care in Kerala. The increase in research capacity included multiple disciplines from field workers, administrators, project managers, project leads, screeners, graders, optometrists, nurses, general practitioners, and research associates in various disciplines. Cross-fertilization of these disciplines enabled the development of several collaborations external to this project. This collaborative project has made a significant impact on research capacity development in both India and the UK

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India)

Introduction The aim of this study is to develop practical and affordable models to (a) diagnose people with diabetes and prediabetes and (b) identify those at risk of diabetes complications so that these models can be applied to the population in low-income and middle-income countries (LMIC) where laboratory tests are unaffordable. Methods and analysis This statistical and economic modelling study will be done on at least 48 000 prospectively recruited participants aged 40 years or above through community screening across 20 predefined regions in India. Each participant will be tested for capillary random blood glucose (RBG) and complete a detailed health-related questionnaire. People with known diabetes and all participants with predefined levels of RBG will undergo further tests, including point-of-care (POC) glycated haemoglobin (HbA1c), POC lipid profile and POC urine test for microalbuminuria, retinal photography using non-mydriatic hand-held retinal camera, visual acuity assessment in both eyes and complete quality of life questionnaires. The primary aim of the study is to develop a model and assess its diagnostic performance to predict HbA1c diagnosed diabetes from simple tests that can be applied in resource-limited settings; secondary outcomes include RBG cut-off for definition of prediabetes, diagnostic accuracy of cost-effective risk stratification models for diabetic retinopathy and models for identifying those at risk of complications of diabetes. Diagnostic accuracy inter-tests agreement, statistical and economic modelling will be performed, accounting for clustering effects. Ethics and dissemination The Indian Council of Medical Research/Health Ministry Screening Committee (HMSC/2018–0494 dated 17 December 2018 and institutional ethics committees of all the participating institutions approved the study. Results will be published in peer-reviewed journals and will be presented at national and international conferences. Trial registration number ISRCTN57962668 V1.0 24/09/2018

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India)

INTRODUCTION: The aim of this study is to develop practical and affordable models to (a) diagnose people with diabetes and prediabetes and (b) identify those at risk of diabetes complications so that these models can be applied to the population in low-income and middle-income countries (LMIC) where laboratory tests are unaffordable. METHODS AND ANALYSIS: This statistical and economic modelling study will be done on at least 48 000 prospectively recruited participants aged 40 years or above through community screening across 20 predefined regions in India. Each participant will be tested for capillary random blood glucose (RBG) and complete a detailed health-related questionnaire. People with known diabetes and all participants with predefined levels of RBG will undergo further tests, including point-of-care (POC) glycated haemoglobin (HbA1c), POC lipid profile and POC urine test for microalbuminuria, retinal photography using non-mydriatic hand-held retinal camera, visual acuity assessment in both eyes and complete quality of life questionnaires. The primary aim of the study is to develop a model and assess its diagnostic performance to predict HbA1c diagnosed diabetes from simple tests that can be applied in resource-limited settings; secondary outcomes include RBG cut-off for definition of prediabetes, diagnostic accuracy of cost-effective risk stratification models for diabetic retinopathy and models for identifying those at risk of complications of diabetes. Diagnostic accuracy inter-tests agreement, statistical and economic modelling will be performed, accounting for clustering effects. ETHICS AND DISSEMINATION: The Indian Council of Medical Research/Health Ministry Screening Committee (HMSC/2018–0494 dated 17 December 2018 and institutional ethics committees of all the participating institutions approved the study. Results will be published in peer-reviewed journals and will be presented at national and international conferences. TRIAL REGISTRATION: ISRCTN57962668 V1.0 24/09/2018