This study was designed to compare the performance of a new weight estimation strategy (Mercy Method) with 12 existing weight estimation methods (APLS, Best Guess, Broselow, Leffler, Luscombe-Owens, Nelson, Shann, Theron, Traub-Johnson, Traub-Kichen) in children from India. Otherwise healthy children, 2 months to 16 years, were enrolled and weight, height, humeral length (HL), and mid-upper arm circumference (MUAC) were obtained by trained raters. Weight estimation was performed as described for each method. Predicted weights were regressed against actual weights and the slope, intercept, and Pearson correlation coefficient estimated. Agreement between estimated weight and actual weight was determined using Bland–Altman plots with log-transformation. Predictive performance of each method was assessed using mean error (ME), mean percentage error (MPE), and root mean square error (RMSE). Three hundred seventy-five children (7.5 ± 4.3 years, 22.1 ± 12.3 kg, 116.2 ± 26.3 cm) participated in this study. The Mercy Method (MM) offered the best correlation between actual and estimated weight when compared with the other methods (r2 = .967 vs .517-.844). The MM also demonstrated the lowest ME, MPE, and RMSE. Finally, the MM estimated weight within 20% of actual for nearly all children (96%) as opposed to the other methods for which these values ranged from 14% to 63%. The MM performed extremely well in Indian children with performance characteristics comparable to those observed for US children in whom the method was developed. It appears that the MM can be used in Indian children without modification, extending the utility of this weight estimation strategy beyond Western populations.

Abstract. Clustering techniques are unsupervised learning methods of grouping similar from dissimilar data types. Therefore, these are popular for various data mining and pattern recognition purposes. However, their performances are data dependent. Thus, choosing right clustering technique for a given dataset is a research challenge. In this paper, we have tested the performances of a Soft clustering (e.g., Fuzzy C means or FCM) and a Hard clustering technique (e.g., K-means or KM) on Iris (150 x 4); Wine (178 x 13) and Lens (24 x 4) datasets. Distance measure is the heart of any clustering algorithm to compute the similarity between any two data. Two distance measures such as Manhattan (MH) and Euclidean (ED) are used to note how these influence the overall clustering performance. The performance has been compared based on seven parameters: (i) sensitivity, (ii) specificity, (iii) precision, (iv) accuracy, (v) run time, (vi) average intra cluster distance (i.e. compactness of the clusters) and (vii) inter cluster distance (i.e. distinctiveness of the clusters). Based on the experimental results, the paper concludes that both KM and FCM have performed well. However, KM outperforms FCM in terms of speed. FCM-MH combination produces most compact clusters, while KM-ED yields most distinct clusters.

Objective Intussusception has been linked with rotavirus vaccine (RVV) as a rare adverse reaction. In view of limited background data on intussusception in India and in preparation for RVV introduction, a surveillance network was established to document the epidemiology of intussusception cases in Indian children. Methods Intussusception in children 2–23 months were documented at 19 nationally representative sentinel hospitals through a retrospective surveillance for 69 months (July 2010 to March 2016). For each case clinical, hospital course, treatment and outcome data were collected. Results Among the 1588 intussusception cases, 54.5% were from South India and 66.3% were boys. The median age was 8 months (IQR 6, 12) with 34.6% aged 2–6 months. Seasonal variation with higher cases were documented during March-June period. The most common symptoms and signs were vomiting (63.4%), bloody stool (49.1%), abdominal pain (46.9%) and excessive crying (42.8%). The classical triad (vomiting, abdominal pain, and blood in stools) was observed in 25.6% cases. 96.4% cases were diagnosed by ultrasound with ileocolic location as the commonest (85.3%). Management was done by reduction (50.8%) and surgery (41.1%) and only 1% of the patients’ died. 91.1% cases met Brighton criteria level 1 and 3.3% Level 2. Between 2010 and 2015, the case load and case ratio increased across all regions. Conclusion Intussusception cases have occurred in children across all parts of the country, with low case fatality in the settings studied. The progressive rise cases could indicate an increasing awareness and availability of diagnostic facilities.

BackgroundFew single center studies from resource-poor settings have reported about the epidemiology, clinical feature and outcome of multisystem inflammatory syndrome in children (MIS-C). However, larger data from multi-center studies on the same is lacking including from Indian setting.MethodsThis retrospective collaborative study constituted of data collected on MIS-C from five tertiary care teaching hospitals from Eastern India. Children ≤ 15 years of age with MIS-C as per the WHO criteria were included. Primary outcome was mortality.ResultsA total of 134 MIS-C cases were included (median age, 84 months; males constituted 66.7%). Fever was a universal finding. Rash was present in 40%, and conjunctivitis in 71% cases. Gastro-intestinal and respiratory symptoms were observed in 50.7% and 39.6% cases, respectively. Co-morbidity was present in 23.9% cases. Shock at admission was noted in 35%, and 27.38% required mechanical ventilation. Fifteen (11.2%) children died. The coronary abnormalities got normalized during follow-up in all except in one child. Initial choice of immunomodulation had no effect on the outcomes. Presence of underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were the factors significantly associated an increased mortality.ConclusionsMIS-C has myriad of manifestations. Underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were associated with an increased mortality. No difference in outcome was noted with either steroid or IVIg or both. Coronary artery abnormalities resolved in nearly all cases.

Background Birth asphyxia is a major cause of early neonatal death and leads to severe consequences such as epilepsy, cerebral palsy, and developmental delay. This study aims to determine the correlation between dyselectrolytemia and the degree of hypoxic-ischemic encephalopathy (HIE) and to find out major risk factors contributing to the severity of HIE and neonatal death. Methods In this prospective cohort study (n=150), term babies weighing ≥ 2.5 kg at birth, with the diagnosis of birth asphyxia, admitted in a medical college in Odisha state from September 2014 to August 2016 were included. Clinical findings, biochemical parameters, treatment, and outcome of HIE babies were recorded. Result The majority of the asphyxiated babies were having moderate HIE (HIE II) (57.33%), whereas mild and severe stages were seen in 15.33%, and 27.34% of babies, respectively. Factors like prolonged labor (87.8%) and meconium-stained liquor (63.4%) were mostly attributed to the severe degree of birth asphyxia (p < 0.001). Apnea, lethargy, and hypothermia were the most remarkable feature of HIE III. The degree of hyponatremia, hypocalcemia, and hyperkalemia (124.4±4.4 mmol/l, 0.83±0.08 mmol/l, and 6.17± 0.89 mmol/l, respectively) were more severely affected in HIE III as compared to HIE l (137.5±3.8 mmol/l, 1.06±0.17 mmol/l, and 5.0±0.79 mmol/l, respectively). Serum urea and creatinine increased proportionately with an increase in the severity of HIE grade. The mildly asphyxiated neonates recovered completely, whereas all the cases who died (n=29,19.3%) belonged to the moderate or severe degree of birth asphyxia. Conclusion The asphyxiated neonates had hyponatremia, hypocalcemia, hyperkalemia, raised serum urea, and creatinine and correlated with the severity of birth asphyxia. Prolonged labor and meconium-stained liquor were the most attributable factor for the severe degree of birth asphyxia. Effective neonatal resuscitation and quick correction of electrolyte imbalances will help in the reduction of neonatal mortality and long-term neurological sequelae.

Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the domain of computer vision. It is challenging due to structural complexity and a marginal contrast difference between the retinal vessels and the background of the fundus image. As bright lesions are prominent in the green channel, we applied contrast-limited adaptive histogram equalization (CLAHE) on the green channel for image enhancement. This work proposes a novel diabetic retinopathy screening technique using an asymmetric deep learning feature. The asymmetric deep learning features are extracted using U-Net for segmentation of the optic disc and blood vessels. Then a convolutional neural network (CNN) with a support vector machine (SVM) is used for the DR lesions classification. The lesions are classified into four classes, i.e., normal, microaneurysms, hemorrhages, and exudates. The proposed method is tested with two publicly available retinal image datasets, i.e., APTOS and MESSIDOR. The accuracy achieved for non-diabetic retinopathy detection is 98.6% and 91.9% for the APTOS and MESSIDOR datasets, respectively. The accuracies of exudate detection for these two datasets are 96.9% and 98.3%, respectively. The accuracy of the DR screening system is improved due to the precise retinal image segmentation.

Retroperitoneal teratoma (RPT) is an exceedingly rare neoplasm in infancy. We came across a 3-month-old infant with large RPT in the left suprarenal area. The tumor was completely excised, and the patient was successfully discharged. Histopathological examination suggested the diagnosis of mature cystic teratoma.

The present study suggests that birth weight and mode of delivery of the neonates influences cord blood stem cell yield.