Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists\u27 expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth

Artificial Neural Network: A New Approach for Prediction of Body Fat Percentage Using Anthropometry Data in Adult Females

Assessing body fat using anthropometric data would be useful in predicting chronic diseases. Accurate use of proper statistical models in analysing body composition data is of prime importance. This study was undertaken to assess body composition of diseased and non-diseased women using body composition analyser thereafter using data for development of statistical model. The objective was to find relationship of various anthropometric parameters with Percent Body Fat (BF%) and to develop various prediction models for estimating BF on the basis of anthropometric data. BF% was predicted using Linear Regression (LR), Multiple Linear Regression (MLR), Non-Linear Regression (NLR) and Artificial Neural Network (ANN) models. The predictors used in the study were age (yrs.), height (cm), weight (kg), Body Mass Index (BMI) (Kg/m2) and Waist Circumference (WC) (cm). Data utilized for the study was related to 860 adult females aged 18-60 years out of which 700 were non-diseased and 160 were diseased (diabetic and hypertensive). Out of various models developed using LR, MLR, NLR for Non-Diseased group, three predictors viz. age, BMI and WC were found to be appropriate for estimating BF%. However, the best prediction of BF% was achieved using ANN model taking age, height, weight and WC as predictors (R2 = 0.787). ANN technique was found as the most suitable technique for developing prediction models for estimation of BF% in non-diseased group. However, in diseased group ANN model could not predict BF% more precisely, may be due to some other factors affecting the body composition of females of diseased group

Predictors of candidemia in pediatric patients (0–12 years) admitted in a tertiary care hospital of Northern India

Background: Bloodstream infections due to Candida species are becoming a major cause of morbidity and mortality in hospitalized patients. The emergence of non-albicans Candida (NACs) species with lesser susceptibility to antifungals has added to the woes of clinicians. Objectives: The objectives of the study were to determine the clinical and laboratory predictors and microbiological profile of candidemia in pediatric patients. Materials and Methods: This is a hospital-based, prospective, and cross-sectional study conducted in the pediatric department of a tertiary care hospital. A total of 250 children aged 0–12 years with risk factors for fungal sepsis were enrolled. Demographic details, clinical, and laboratory parameters were noted and samples were sent for culture. Cultures yielding growth of Candida were included in the study, and antifungal susceptibility performed. Associations were assessed using Chi-square test first and then through logistic regression models. Results: Among the 250 patients with risk factors for fungal sepsis, 47 patients (18.8%) with culture proven candidemia were identified. Predictors of candidemia among neonates were prematurity (<30 weeks), prolonged ventilation (>72 h), and thrombocytopenia. Among pediatric patients, prolonged steroid intake, Candida isolation from sites other than blood and persistent neutropenia, were significantly associated with the candidemia. NAC species were the predominant isolates (78.7%). Conclusion: Candidemia should be suspected in premature neonates requiring prolonged ventilation with unexplained thrombocytopenia. Among pediatric patients, prolonged steroid intake, Candida isolation from sites other than blood and persistent neutropenia are predictors of candidemia

Disparity in Relation to Covid-19 Preventive Behaviour and Associated Myths among Rural and Urban Residents of Lucknow: A Community Based Study

Introduction: For curbing Covid-19 disease, adequate knowledge, attitude, and practices of both rural and urban population for Covid-19 disease prevention is required along with busting of the associated myths. Objectives: To assess the Knowledge, Attitude and Practices of urban and rural residents of Lucknow district regarding covid-19 preventive behaviour and associated myths. Methodology: A community-based study was conducted among 420 rural and 421 urban residents of Lucknow. Multistage random sampling was done to select the study subjects. A pre-designed pretested semi-structured questionnaire was used to collect the information regarding the Knowledge, Attitude and Practices of the residents for covid-19 disease causes, prevention, and treatment. Further, KAP scoring was done to compare the two groups. Results: The mean age of the rural and urban residents was 31.48 ± 12.05 and 30.93 ± 11.96 years respectively. Only 40.4 % urban and 25.5 % rural people had correct knowledge about social distancing (p<0.0001). Knowledge regarding quarantine for covid-19 disease prevention was less among the urban residents (64.6%) as compared to rural (70.5%) (p=0.035). More than one-third (37.6%) of the rural resident believed in the myth that alcohol can prevent the covid-19 disease (p=0.003). 68.8 and 70.5 percent rural and urban residents had positive attitude towards the Indian government’ efforts in curbing the disease. Majority of the urban (90%) and rural (87.6%) residents wore mask when they went out. Only one-fourth of the urban (24.7%) and rural (22.9%) had correct practices for the duration of hand washing. Conclusion: The knowledge was more among the urban people, attitude and practices were almost similar among both the rural and urban residents while myths were more observed among the rural residents

Small and sick newborn care during the COVID-19 pandemic: global survey and thematic analysis of healthcare providers' voices and experiences.

INTRODUCTION: The COVID-19 pandemic is disrupting health systems globally. Maternity care disruptions have been surveyed, but not those related to vulnerable small newborns. We aimed to survey reported disruptions to small and sick newborn care worldwide and undertake thematic analysis of healthcare providers' experiences and proposed mitigation strategies. METHODS: Using a widely disseminated online survey in three languages, we reached out to neonatal healthcare providers. We collected data on COVID-19 preparedness, effects on health personnel and on newborn care services, including kangaroo mother care (KMC), as well as disruptors and solutions. RESULTS: We analysed 1120 responses from 62 countries, mainly low and middle-income countries (LMICs). Preparedness for COVID-19 was suboptimal in terms of guidelines and availability of personal protective equipment. One-third reported routine testing of all pregnant women, but 13% had no testing capacity at all. More than 85% of health personnel feared for their own health and 89% had increased stress. Newborn care practices were disrupted both due to reduced care-seeking and a compromised workforce. More than half reported that evidence-based interventions such as KMC were discontinued or discouraged. Separation of the mother-baby dyad was reported for both COVID-positive mothers (50%) and those with unknown status (16%). Follow-up care was disrupted primarily due to families' fear of visiting hospitals (~73%). CONCLUSION: Newborn care providers are stressed and there is lack clarity and guidelines regarding care of small newborns during the pandemic. There is an urgent need to protect life-saving interventions, such as KMC, threatened by the pandemic, and to be ready to recover and build back better

Object picking using robotic arm mounted with a camera for detection using image processing

In lieu of making today’s world working environment better for humans, the field of robotics has taken up new challenge these days with devices like humanoid type robot which works on human command. Robotic arm is one of the most important parts of the humanoid. In this paper, the design of robotic arm that can be used to pick up objects of given shape and color is described. The robotic arm works with a voice command from user. This arm is realized using servo motor for joints of robotic arm, Bluetooth module, Arduino Uno, MATLAB software and a mounted camera. The designed robotic arm is practically implemented to demonstrate its effectiveness such that it is able to pick objects of given shape and color using voice command from user. The task is completed using control of servo motors via Arduino board/ MATLAB software and a camera

Seasonal variation of HbA1c levels in diabetic and non-diabetic patients

Background: Hemoglobin A1c (HbA1c) serves as a pivotal marker for long-term glycemic control. The Diabetes Control and Complications Trial (DCCT) established its relevance, yet gaps exist in understanding potential seasonal variations in HbA1c levels among diabetic patients. The study highlights the need to explore potential seasonal variations in HbA1c levels and their impact on diabetic patients. Materials and methods: This is an observational study conducted in a tertiary care hospital from January to December 2019, the study analyzed HbA1c levels in 8138 patients. Blood samples were collected using Potassium EDTA-containing vials and processed with an automated analyzer. Seasonal variations were explored using time series analysis. Results: Mean HbA1c levels peaked during the monsoon (June to September) and were lowest in autumn (October to November). Subgroup analysis revealed differences in patients with HbA1c values below and above 6.5 %. Those with controlled blood sugar showed higher levels in winter (December to February) and monsoon (June to September), while patients with HbA1c values ≥ 6.5 % exhibited significantly lower levels in monsoon (June to September) and autumn (October to November) compared to summer (March to May). Conclusion: In contrast to global trends, Indian patients demonstrated distinct seasonal variations in HbA1c levels. The highest levels during the monsoon (June to September) may be linked to reduced outdoor activity and dietary changes. The study emphasizes the need for tailored diabetes management considering seasonal influences. Further extensive, longitudinal studies across diverse Indian regions are recommended to comprehensively grasp the impact of seasonal changes on diabetes outcomes

Raman scattering of rare earth sesquioxide Ho2O3: A pressure and temperature dependent study

Pressure and temperature dependent Raman scattering studies on Ho2O3 have been carried out to investigate the structural transition and the anharmonic behavior of the phonons. Ho2O3 undergoes a transition from cubic to monoclinic phase above 15.5 GPa, which is partially reversible on decompression. The anharmonic behavior of the phonon modes of Ho2O3 from 80K to 440K has been investigated. We find an anomalous line-width change with temperature. The mode Gruneisen parameter of bulk Ho2O3 was estimated from high pressure Raman investigation up to 29 GPa. Furthermore, the anharmonic components were calculated from the temperature dependent Raman scattering