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Introduction: Type 1 diabetes is a complex and demanding condition, which mainly affects children and places a considerate amount of substantial behavioral and psychological burden on young people and stress to their families. Objectives: The study aimed to assess knowledge of mothers of T1DM child, to assess the stress level of mothers and to assess their coping strategies adopted while caring for their child with type 1 DM. Methodology: Quantitative (non-experimental) approach with an exploratory survey design was used. The data was collected from 100 mothers of T1DM children at NGO named, Yog Dhyan Foundation, New Delhi. A structured questionnaire and structured rating scale for knowledge and stress assessment respectively and a standardized scale (CRIS) to assess coping strategies of mothers were used for data collection and later data was analyzed using descriptive and inferential statistics. Result: The findings revealed that 64% of the mothers had inadequate knowledge and 34% of the mothers had adequate knowledge regarding Type 1 diabetes mellitus. Majority of the mothers of T1DM children (79%) had moderate stress , followed by severe stress which was 13% and only 8% mothers had mild stress. It was also found that the majority of the mothers of type 1 diabetes mellitus children (84%) were having above average stress coping ability. Conclusion: Imparting knowledge to help the parents especially mothers to deal with the problems of the diabetes in children and live a healthy life is pertinent. How to cite this article: Arora R, Shaiju B, Hashmi S. A Study to Assess the Kowledge, Stress Level and Coping Strategies among Mothers of Child with Type-1 Diabetes Mellitus (T1DM). Int J Nurs Midwif Res 2019; 6(4): 28-33. DOI: https://doi.org/10.24321/2455.9318.201929

Deep Learning (DL) algorithms, especially Convolutional Neural Network (CNN) have outperformed in medical image classification tasks and have achieved human-competitive performance. This has become possible because CNN learns image features through backpropagation. However, the strategy for designing a CNN model with the highest accuracy for a specific application is often unclear. Because finding an appropriate network structure with the best combination of hyperparameters for different datasets is always a challenging task. To address this, we propose an optimized CNN framework that automatically and efficiently tune its hyper-parameters using a hyperband search optimization approach. In this paper, an efficient CNN with optimized hyperparameters for the classification of tuberculosis disease in Chest X-Ray (CXR) images is trained and tested on a publicly available NLM-China dataset. The experimental results illustrate that the hyperparameters optimize the CNN framework and achieve 91.42% accuracy for the classification of tuberculosis disease in CXR images.

Wegener's granulomatosis is a rare systemic autoimmune disease of unknown etiology. Typically, it affects the upper respiratory tract, lungs, and kidneys. Sometimes localized form of the disease in head and neck region is also seen. These often go unrecognized till the patient develops systemic signs and symptoms leading to significant complications affecting the quality of life of patient. If timely diagnosed, such potentially life altering sequelae can be avoided with treatment. We present here a case report of a female who presented with breathing difficulty along with Otological symptoms but remained undiagnosed for a long time leading to permanent morbidity because of facial nerve damage and hearing loss.

Due to COVID-19, demand for Chest Radiographs (CXRs) have increased exponentially. Therefore, we present a novel fully automatic modified Attention U-Net (CXAU-Net) multi-class segmentation deep model that can detect common findings of COVID-19 in CXR images. The architectural design of this model includes three novelties: first, an Attention U-net model with channel and spatial attention blocks is designed that precisely localize multiple pathologies; second, dilated convolution applied improves the sensitivity of the model to foreground pixels with additional receptive fields valuation, and third a newly proposed hybrid loss function combines both area and size information for optimizing model. The proposed model achieves average accuracy, DSC, and Jaccard index scores of 0.951, 0.993, 0.984, and 0.921, 0.985, 0.973 for image-based and patch-based approaches respectively for multi-class segmentation on Chest X-ray 14 dataset. Also, average DSC and Jaccard index scores of 0.998, 0.989 are achieved for binary-class segmentation on the Japanese Society of Radiological Technology (JSRT) CXR dataset. These results illustrate that the proposed model outperformed the state-of-the-art segmentation methods.

In medical imaging, automatic lung segmentation from Chest X-ray (CXR) images helps doctors to diagnose various diseases and guide for further treatment. U-Net is the most popular model for biomedical image segmentation. But it does not consider regions outside the target, thus its performance decreases for complex images. An advanced U-Net++ model provides better performance on complex images than U-Net. In this paper, we propose a modified UNet++ framework for the segmentation of lungs in CXR images. This model is a deep supervised encoder-decoder architecture with dense skip connections. The proposed model used an easily accessible NLM-China CXR dataset and achieved promising performance for the segmentation of the lung fields. The experimental results showed that the modified UNet++ model achieved a Dice Similarity Coefficient (DSC) score of 0.9680 for lung segmentation that identifies tuberculosis disease.

Background: To evaluate the relative efficacy of synthetic nanocrystalline hydroxyapatite (HA) (Ostim®) and microcrystalline HA (Osteogen®) bone grafts in the treatment of human periodontal intrabony defects clinically and radiographically through denta scan. Materials and Methods: Ten chronic periodontitis patients with bilateral intrabony periodontal defects of ≥2 mm radiographic defect depth below 55 years of age were selected randomly and treated with synthetic nanocrystalline HA (Ostim®) or synthetic microcrystalline HA (Osteogen®) bone graft. Clinical parameters including probing depth (PD) and clinical attachment level (CAL) were measured preoperatively and postoperatively at 3 and 6 months for each of the defects using an occlusal acrylic stent. Radiographic parameters were measured with the help of denta scan preoperatively and postoperatively at 6 months. Results: At 6 months following therapy, the Osteogen® group showed a reduction in mean PD from 11.10 ± 1.663 to 8.50 ± 0.850 mm and a change in mean CAL from 6.30 ± 1.160 to 3.40 ± 0.516 mm, whereas in the Ostim® group, the mean PD decreased from 11.20 ± 0.919 to 8.30 ± 0.823 mm and mean CAL decreased from 6.10 ± 0.738 to 3.30 ± 0.483 mm. At 6 months following therapy, denta scan showed a reduction in mean intrabony defect depth in the Osteogen® group from 2.54 ± 0.786 to 1.01 ± 0.448 mm, whereas in the Ostim® group, it was 2.71 ± 0.650 mm to 1.12 ± 0.563 mm. Conclusion: It was concluded that both the HA bone grafts produced statistically significant reduction in pocket depth, in the depth of osseous lesion, and a statistically significant gain in attachment level, irrespective of their physico-chemical properties.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzyme defect in humans. G6PD deficiency is widely distributed in tropical and subtropical parts of the world and a conservative estimate is that at least 500 million people have a G6PD deficient gene. In several of these areas, the frequency of a G6PD deficiency gene may be as high as 20% or more. The vast majority of people with G6PD deficiency remain clinically asymptomatic throughout their lifetime. However, all of them have an increased risk of developing neonatal jaundice and a risk of developing acute hemolytic anemia when challenged by a number of oxidative agents. The most important treatment measure is prevention: Avoidance of the drugs and foods that cause hemolysis.