Study on Iron Profile in Children with Cyanotic Congenital Heart Disease

INTRODUCTION: In the world, the most common cause of nutritional anemia is Iron deficiency anemia. Children during the phase of rapid growth such as preschool and adolescence are at higher risk of developing iron deficiency anemia . Rural areas and children from poor socioeconomic status show increased prevalence of iron deficiency .(1) Iron deficiency is an important problem in patients with cyanotic congenital heart disease .In CCHD , arterial oxygen saturation decreases and red blood cell count may reach to high level and hyperviscosity develops(2). In anemic patients especially those with microcytic iron deficiency anemia, permeability of microcyic erythrocytes decreases in comparison to normocytic cells, therefore thromboembolic and cardiovascular events are encountered more commonly. As erythropoiesis and hence haemoglobin ,haematocrit and erythrocyte count increases in patients with cyanotic congenital heart disease, haemoglobin and haematocrit are not useful indicators of iron deficiency anaemia. In children with cyanotic congenital heart disease, when there is decreasing arterial oxygen saturation, it causes compensatory increase in haemoglobin and haematocrit levels. Iron deficiency causes discrepant values for arterial oxygen saturation and haemoglobin/haematocrit and that "normal" haemoglobin/haematocrit levels in such children may constitute anaemia(3). In CCHD, normal hemoglobin represent relative anemia and may have disastrous effects. The normal postnatal fall in haemoglobin levels that occurs in neonates will not occur if arterial desaturation is marked from birth ,although relative anaemia in this situation develops by the third or fourth month of life(4).The bone marrow normally responds to hypoxia by increasing erythropoiesis, with an increase in red cell count, haemoglobin level and haematocrit. The consequences of iron deficiency anaemia in cyanotic heart disease are dire, either in infancy or later. Metabolic acidosis, and cyanotic attacks are exacerbated by the presence of iron deficiency anaemia . Similarly very high counts of red blood cells, blood viscosity is increased, and with it the tendency to cerebrovascular accidents. When the haematocrit is above 60%,further small increases produces large increments in viscosity(5). At a haematocrit level of 70% , blood viscosity is so high that fluidity in small vessel becomes critical.Measurements of MCV, MCH and serum ferritin reveal the existence of iron deficiency anaemia. In this study we made an attempt to study the children with cyanotic congenital heart disease ,by simple tests like complete hemogram and red cell indices and ascertain if it be enough to diagnose iron deficiency than the more expensive diagnostic tests like serum iron, total iron binding capacity and serum ferritin levels. AIMS AND OBJECTIVES: a) To study prevalence of iron deficiency anaemia in children having cyanotic congenital heart disease b) To assess various biochemical and hematological parameters of iron status in children having cyanotic congenital heart disease. DISCUSSION: In this study ,50 children with congenital cyanotic heart disease were included and investigated by doing complete blood count and iron profile . Based on the transferrin saturation ,they were categorised into two groups as iron deficient and iron sufficient. Children who had a transferrin saturation level < 16% were grouped as iron deficient and those who had a transferrin saturation level >16% were grouped as iron sufficient. In table 1(age distribution among cases), out of 50 children included in this study, 12 (24%) were less than 1 year ,24(48%) children were between 1 to 6 years and 14 (28%) children were in between 6 to 12 years. Most of the children were in the age group of 1 to 6 years. In table 2 (sex distribution among cases) ,Among the total of 50 patients,25(50% )were male and 25(50%) were female. In table 3: Distribution of cyanosis among cases : 40 cases(80%) have cyanosis, 10 cases(20%) donot have cyanosis. In table 4 :Distribution of clubbing among cases : 35 cases(70%) have clubbing, 15 cases(30%) donot have clubbing. In table 5,Based on transferrin saturation, children were grouped into iron deficient and iron sufficient.35(70%) children who had transferrin saturation of < 16% and 15(30%) children had transferrin saturation of >16%. In this study the prevalence of iron deficiency among children with cyanotic congenital heart disease was 70%. In other similar studies, cemilebanu et al(37)the prevalence of iron deficiency anemia was 63.6% and Lango et al(40) the prevalence was 16.9%. In table 6, we see that, out of 12 children who were <1 year of age, 6(50%) were iron deficient and 6(50%) were iron sufficient, and out of 24 patients between 1-6 years age group,20 (83.3%) were iron deficient and 4 (16.7%) were iron sufficient. Among 14 children between 6-12 years age group,9(64.3%) were iron deficient and 5(35.7%) were iron sufficient. This shows that iron deficiency is more common between 1-6 years. This finding is consistent with the finding of NFHS 3, where maximum number of children with iron deficiency anaemia lie between 6 -35 months of age. But there was no statistical association between age of the patient and iron status (p=0.0104.). CONCLUSION: 1. Prevalence of iron deficiency anaemia in children with cyanotic congenital heart disease was 70% 2. Iron deficiency anaemia was more common among 1 – 6 yrs(83.3%) and female children(76%), though there was no statistical association. 3. 82.9 % of cases with iron deficiency anaemia had MCV < 72 fl. There was statistical significant association between MCV level of the patient and iron status (p=0.07). 4. Raised HCT> 60% was found in 100 %(13 cases) of children with iron deficiency anaemia in children with cyanotic congenital heart disease . There was statistical significant association between HCT of the patient and iron status (p=0.021). 5. 20(100%) of children who had sr .iron < 50 mg/dl were iron deficient . There was statistical significant association between iron status of the patient and iron status (p=0.00). 6. 27(100%) of children who had sr .ferritin < 15 ng/dl were iron deficient . There was statistical significant association between serum ferritin status of the patient and iron status (p=0.00)

Skin Cancer classification using Convolutional Capsule Network (CapsNet)

Researchers are proficient in preprocessing skin images but fail in identifying efficient classifiers for classifying skin cancer due to the complex variety of lesion sizes, colors, and shapes. As such, no single classifier is sufficient for classifying skin cancer legions. Convolutional Neural Networks (CNNs) have played an important role in deep learning, as CNNs have proven successful in classification tasks across many fields. However, present day models available for skin cancer classification suffer from not taking important spatial relations between features into consideration. They classify effectively only if certain features are present in the test data, ignoring their relative spatial relation with each other, which results in false negatives. They also lack rotational invariance, meaning that the same legion viewed at different angles may be assigned to different classes, leading to false positives. The Capsule Network (CapsNet) is designed to overcome the above-mentioned problems. Capsule Networks use modules or capsules other than pooling as an alternative to translational invariance. The Capsule Network uses layer-based squashing and dynamic routing. It uses vector-output capsules and max-pooling with routing by agreement, unlike scale-output feature detectors of traditional CNNs. All of which assist in avoiding false positives and false negatives. The Capsule Network architecture is created with many convolution layers and one capsule layer as the final layer.  Hence, in the proposed work, skin cancer classification is performed based on CapsNet architecture which can work well with high dimensional hyperspectral images of skin

Skin Cancer Classification using Convolutional Capsule Network (CapsNet)

994-1001Researchers are proficient in preprocessing skin images but fail in identifying efficient classifiers for classifying skin cancer due to the complex variety of lesion sizes, colors, and shapes. As such, no single classifier is sufficient for classifying skin cancer legions. Convolutional Neural Networks (CNNs) have played an important role in deep learning, as CNNs have proven successful in classification tasks across many fields. However, present day models available for skin cancer classification suffer from not taking important spatial relations between features into consideration. They classify effectively only if certain features are present in the test data, ignoring their relative spatial relation with each other, which results in false negatives. They also lack rotational invariance, meaning that the same legion viewed at different angles may be assigned to different classes, leading to false positives. The Capsule Network (CapsNet) is designed to overcome the above-mentioned problems. Capsule Networks use modules or capsules other than pooling as an alternative to translational invariance. The Capsule Network uses layer-based squashing and dynamic routing. It uses vector-output capsules and max-pooling with routing by agreement, unlike scale-output feature detectors of traditional CNNs. All of which assist in avoiding false positives and false negatives. The Capsule Network architecture is created with many convolution layers and one capsule layer as the final layer. Hence, in the proposed work, skin cancer classification is performed based on CapsNet architecture which can work well with high dimensional hyperspectral images of skin

Analysis of COVID-19 Pandemic - Origin, Global Impact and Indian Therapeutic Solutions for infectious diseases

The first case of COVID-19 was reported in China on December 2019[1] and almost 213 countries has reported around 5,350,000 COVID-19 cases all over the world with the mortality rate up to 3.4% as of May 23,2020. On March 11, 2020 WHO (World Health Organization) declared COVID-19 as global pandemic. Moving towards from epidemic to global pandemic situation just in two months, COVID-19 has caused tremendous negative effects on people's wellbeing and the economy all over the world. Scientists and researchers all over the world have a vested interest in researching and mitigating to handle the dire situation. This paper covers the COVID-19's origin, characteristics of the virus, and reasons behind the outbreak and precautionary measures that have to be followed to handle the critical situation. Several therapeutic solutions in Indian healing tradition have been discussed to improve the immune system in order to equip ourselves to deal with the outbreak of COVID-19.

Integration of EEG and Eye Tracking Technology: A Systematic Review

Electroencephalography, or EEG, measures brain signals and activities using small electrodes which read electrical activity in the brain. For understanding cognitive patterns and diagnosing any abnormalities in the brain, this is a widely used test. Eye-tracking, on the other side, is the measurement of gaze or eye movements which is being utilized in research and investigation to decode human behaviors. Combining the features of EEG and eye-tracking activities would provide helpful insight into understanding a human\u27s cognitive behavioral process. An array of highly sophisticated experiments was conducted with the combination of both features. This paper will traverse previous studies and research integrating EEG and eye-tracking and prepare a taxonomy of the implementations in several prominent fields. Further, it unravels the current challenges and limitations of working in these areas and proposes enhancements to be considered in future research. This review work will assist as a consensus of the current state of knowledge in the future new research directions in exploring the integration of EEG and eye-tracking technology