Clinico-Etiopathogenesis of Vitamin B12, Folic Acid and Iron Deficiency in Severe Acute Malnutrition Children: A Tertiary Care Hospital Experience from Central India.

In severe acute malnutrition, micronutrient deficiency as well as protein energy malnutrition is a major obstacle to growth & development. Iron deficiency dominates the spectrum of nutritional anemia. After taking informed consent, 211 SAM children and 211 age-and sex-matched healthy children with normal nutritional status were enrolled for the study. MUAC was used to diagnose SAM. A 5-part automated hematoanalyzer was used to measure the complete blood count and red cell indices, and the peripheral smear method to determine the red cell morphology. We measured serum ferritin, Vitamin B12, and folic acid using the ELISA method. Compared to controls, children with SAM had significantly lower red cell indices, platelet counts, and white cell counts. The most common clinical symptoms seen in SAM children were diarrhea, pneumonia, acute gastroenteritis, and acute respiratory infection. Children with SAM are more likely to suffer from iron deficiency and B12 deficiency. Severe vitamin B12 deficiency was more frequently associated with severe anemia. The severe anemia in SAM children constantly changes the body's defense mechanism, affecting the haematopoiesis. In this study, haematological indices are recommended for predicting severity of anemia, and hematopoietic changes are described, in order to improve anticipatory care and outcome in children with SAM.

Neurodevelopmental Status of Children aged 6-30 months with Severe Acute Malnutrition.

OBJECTIVE: This cross sectional study was done to assess the developmental status in children (6-30 mo old) with severe acute malutrition (SAM). METHODS: Study subjects were enrolled from children in SAM therapeutic unit, and controls were selected from well-baby clinic of the institute. Neurodevelopment of both groups was assessed using the Developmental assessment scale of Indian infants (DASII). Developmental quotient (DQ) ≤70 was considered delayed. RESULTS: Mean (SE) motor DQ 59.04 (0.74) and mental DQ 62.1 (0.57) was lower in SAM as compared to controls (both P<0.0001). Clusters of early age were normal but clusters with items of later infancy were delayed. CONCLUSION: Children with SAM show significant delay in development, and motor DQ is affected more than mental DQ.

Fibrillation complexity as a predictor of successful defibrillation.

A major focus of Implantable Cardioverter Defibrillator (ICD) research has been to reduce the defibrillation shock energy to prolong battery life and provide an enhanced quality of life for the patient. We investigated whether the degree of disorganization (complexity) of the electrogram is correlated with defibrillation shock outcome. The study data sets were recorded using the high voltage leads of an ICD during device implantation. A total 57 data segments from 19 patients were analyzed. Beat cycles were identified using a novel wavelet based method. Two algorithms were proposed and implemented to quantify the disorganization of the electrogram signals: Approximate Entropy and Cross Correlation. Entropy Index based on the ApEn method, was able to discriminate successful episodes from failure ones with a specificity of 93% and sensitivity of 100%. Similarity Index based on Cross correlation method, obtained a specificity of 72% and sensitivity of 66%. We conclude that the organization of a VF episode is related to the minimum energy required for successful defibrillation.

Fibrillation complexity as a predictor of successful defibrillation.

A major focus of Implantable Cardioverter Defibrillator (ICD) research has been to reduce the defibrillation shock energy to prolong battery life and provide an enhanced quality of life for the patient. We investigated whether the degree of disorganization (complexity) of the electrogram is correlated with defibrillation shock outcome. The study data sets were recorded using the high voltage leads of an ICD during device implantation. A total 57 data segments from 19 patients were analyzed. Beat cycles were identified using a novel wavelet based method. Two algorithms were proposed and implemented to quantify the disorganization of the electrogram signals: Approximate Entropy and Cross Correlation. Entropy Index based on the ApEn method, was able to discriminate successful episodes from failure ones with a specificity of 93% and sensitivity of 100%. Similarity Index based on Cross correlation method, obtained a specificity of 72% and sensitivity of 66%. We conclude that the organization of a VF episode is related to the minimum energy required for successful defibrillation.