Severe iron deficiency anemia in an infant: A case report

Iron deficiency anemia (IDA) is the most common nutritional deficiency disorder in children and is worldwide in distribution. In fact, iron deficiency is the only micronutrient deficiency that is prevalent in virtually all developed countries. An infant is predominantly fed on milk, bioavailability of breast milk is much better than cow’s milk; although both are deficient in iron content. The peak prevalence of nutritional IDA occurs in late infancy and very rarely seen before the age of 6 months in a term baby who is exclusively breastfed. We report a case of severe IDA in a 6-month-old child in whom no other obvious cause was found

COMPUTATIONAL ANALYSIS OF INTERACTIONS BETWEEN ANTI-EPILEPTIC DRUGS AND IMPORTANT PLACENTAL PROTEINS-A POSSIBLE ROUTE FOR NEURAL TUBE DEFECTS IN HUMANS

Objective: The reason behind the occurrence of Neural Tube Defects (NTDs) in pregnant women treated with certain Anti-Epileptic Drugs (AEDs) such as Carbamazepine, Valproate, Lamotrigine, Phenobarbital, etc., is not yet known. The relationship between Folic Acid intake and NTDs is not yet established. Folate receptors play a critical role in mediating placental transport of maternal folate to the foetus. Another important protein is Carnitine O-acetyltransferase that is involved in the transport of carnitine, which is much needed for foetal metabolic functions and tissue development. The objective of this study is to understand the interaction of AEDs with two important placental proteins through a docking approach to establishing a suitable explanation of AED's role in NTD.Methods: A generic algorithm based docking was used to identify and study the mode of interactions between the drugs and placental proteins. For comparison purpose, the natural ligands of these receptors have also been included in the dataset containing AEDs.Results: Both bonded and non-bonded interactions were observed between AEDs and the crucial residues of these proteins. The drugs formed complex with these proteins with satisfactory binding energy. Some amount of Electrostatic interaction was also observed among a few pairs of protein residue and drug molecules.Conclusion: We suggest that these drug-protein associations, involving bonded and non-bonded interactions, could be a possible portal by which certain AEDs induce NTDs in the foetus. Higher interaction of Pantothenic Acid with Folate Receptor could be a mechanism through which Pantothenic Acid inhibits Valproic Acid-induced NTDs. And thus, its supplementation can specifically prevent Valproic Acid-induced NTDs. The above mechanism also explains how increased intake of Folic Acid during pregnancy can reduce the occurrence of NTDs.Keywords: Neural Tube Defect, Anti-epileptic drug, Placenta, Folate transporters, Docking, Carnitine O-acetyltransferas

Duodenal perforation with Ascaris lumbricoides in a child: A case report

Gastrointestinal infestation with Ascaris lumbricoides is common in temperate and tropical countries. Although heavy worm infestation produces wide range of acute abdominal complications, duodenal perforation in association with ascariasis, especially in children, is rarely reported. We report a case of 5-year-old girl with duodenal perforation secondary to ascariasis

Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric

What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, even terrestrial environmental sciences. We review the existing metrics of habitability and the new classification schemes of extrasolar planets and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as CDHS and CEESA, cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially habitable planets for detailed follow-up targets.Comment: 17 pages, 6 figures, in pres

Unusual presentation of fibrolamellar carcinoma: A rare case report

Fibrolamellar hepatocellular carcinoma (fHCC) is a distinct type of first time used hence- hepatocellular carcinoma affecting particularly young patient with no gender predilection. However, there is increasing evidence of occurrence of this tumor in elderly patients also. Abdominal imaging with pre-operative biopsy provides accurate diagnosis. However, in difficult situations, CD68, cytokeratin 7, HepPar1, etc., immunohistochemical stains provide accurate diagnosis to differentiate this condition from other malignancies. Hereby, we present a case of fHCC in a 55-year-old female with equivocal imaging features and diagnosis was made by histopathology aided by immunohistochemistry

Oxidative Unzipping and Transformation of High Aspect Ratio Boron Nitride Nanotubes into ?White Graphene Oxide? Platelets

Morphological and chemical transformations in boron nitride nanotubes under high temperature atmospheric conditions is probed in this study. We report atmospheric oxygen induced cleavage of boron nitride nanotubes at temperatures exceeding 750?ÁC for the first time. Unzipping is then followed by coalescence of these densely clustered multiple uncurled ribbons to form stacks of 2D sheets. FTIR and EDS analysis suggest these 2D platelets to be Boron Nitride Oxide platelets, with analogous structure to Graphene Oxide, and therefore we term them as ?White Graphene Oxide? (WGO). However, not all BNNTs deteriorate even at temperatures as high as 1000?ÁC. This leads to the formation of a hybrid nanomaterial system comprising of 1D BN nanotubes and 2D BN oxide platelets, potentially having advanced high temperature sensing, radiation shielding, mechanical strengthening, electron emission and thermal management applications due to synergistic improvement of multi-plane transport and mechanical properties. This is the first report on transformation of BNNT bundles to a continuous array of White Graphene Oxide nanoplatelet stacks

Reinforcement Learning and Advanced Reinforcement Learning to Improve Autonomous Vehicle Planning

Planning for autonomous vehicles is a challenging process that involves navigating through dynamic and unpredictable surroundings while making judgments in real-time. Traditional planning methods sometimes rely on predetermined rules or customized heuristics, which could not generalize well to various driving conditions. In this article, we provide a unique framework to enhance autonomous vehicle planning by fusing conventional RL methods with cutting-edge reinforcement learning techniques. To handle many elements of planning issues, our system integrates cutting-edge algorithms including deep reinforcement learning, hierarchical reinforcement learning, and meta-learning. Our framework helps autonomous vehicles make decisions that are more reliable and effective by utilizing the advantages of these cutting-edge strategies.With the use of the RLTT technique, an autonomous vehicle can learn about the intentions and preferences of human drivers by inferring the underlying reward function from expert behaviour that has been seen. The autonomous car can make safer and more human-like decisions by learning from expert demonstrations about the fundamental goals and limitations of driving. Large-scale simulations and practical experiments can be carried out to gauge the effectiveness of the suggested approach. On the basis of parameters like safety, effectiveness, and human likeness, the autonomous vehicle planning system's performance can be assessed. The outcomes of these assessments can help to inform future developments and offer insightful information about the strengths and weaknesses of the strategy