The estimation of foetal birth weight is an important factor in the management of high risk pregnancies. Estimated foetal weight is calculated in the standard routine antepartum evaluation of high risk pregnancies and deliveries. This prospective observational study was done at the Department of Obstetrics and Gynecology in Border Guard Hospital, Peelkhana, Dhaka over a period of 6 months from January 2012 to June 2012. The present study was carried out to compare the accuracy of actual and ultrasonographic estimation of foetal weight at term. Hundred pregnant women at different gestational age from 37 weeks to 40 weeks were selected by simple random sampling. Ultrasonography was done for determination of estimated foetal weight (EFW) at term by using Hadlock method and birth weight was measured just after delivery. Data analysis was done by percentage and paired 't' test. The age range of patients were 18-37 years with mean ±SD is 25.13±4.46. Among 100 study patients 33% were nuliparous and 67% were multiparous. The mean ±SD of gestational age and actual birth weight is 38.76±1.09 and 3.11±0.391 respectively. Ultrasound biometric data that includes mean ±SD biparietal diameter (BPD) in mm, abdominal circumference (AC) in mm and femur length (FL) in cm were 90.21±3.52, 327.67±20.75 and 7.45±1.43 respectively. Mean ±SD of estimated foetal weight (EFW) Kg was 2.97±0.53. Actual birth weight is correlated with the estimated foetal weight and the result was not statistically significant (P >.05). Calculation of estimated fetal weight by ultrasonography is recommended to make decision about mode of delivery, so that an obstetrician can plan early in high risk cases.

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Cyanide toxicity is a fatal condition if not detected and treated in stipulated time. Lack of rapid detection modalities, and nonspecific nature of clinical presentation make the diagnosis more challenging. Cherry red colour of blood might be the only clue sometimes. We present a case of sudden onset altered sensorium which was detected as cyanide poisoning and treated successfully with antidots on the basis of central venous blood colour and corroborative presentation. How to cite this article Panigrahi N, Haranath P et al . Cyanide Toxicity!! Colour of Blood Says It All. Indian J Crit Care Med 2019;23(3):155-156.

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Background: Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD. Objectives: The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD. Methods: This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review. Conclusions: Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available.