Neuroendocrinology of Pyridoxine Deficiency

Veuruenducrim lri v j p .rim, deficienc:v. NEUROSCI BIOBEHAV REV 12(3/4) 189-193. 1988.- Dihydroxyphenylalanine decarboxvlase and 5-hydroxytryptophan decarboxvlase respectively have high and low affinities for pyridoxal phosphate. In the pyridoxinedeficient animal. hypothalamic serotonin content is significantly reduced without any change in catecholamine levels. Hypothalamic neurotransmitters affect the hvpothalamo-pituitary-end organ axes. Specifically, the decrease in hypothalamic serotonin in the pyridoxine-deficient rat results in tertiary hypothyroidism. In addition. pineal function is affected in deficient animals due to decreased synthesis of melatonin.

Effect of Pyridoxine Deficiency in Young-Rats on High-Affinity Serotonin and Dopamine Receptors

The high-affinity bindings of [3H]-5-hydroxytryptamine to serotonin S-1 receptors, [3H]-ketanserin to serotonin S-2 receptors in the cerebral cortex, [3H]- fluphenazine to dopamine D-1 receptors, and [3H]-spiroperidol to dopamine D-2 receptors in the corpus striatum were studied in pyridoxine-deficient rats and compared to pyridoxine-supplemented controls. There was a significant increase in the maximal binding (Bmax) of serotonin S-1 and S-2 receptors with a significant decrease in their binding affinities (Kd). However, there were no significant changes either in the maximal binding or binding affinity of striatal dopamine D- 1 and D-2 receptors. Receptor sensitivity seems to correlate negatively with the corresponding neurotransmitter concentrations in the pyridoxine-deficient rats.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Chemical Evaluation of Selected Organics and Trace Metals in the Mangrove Macroflora Of Cochin

Mangroves are considered to play a significant role in global carbon cycling. Themangrove forests would fix CO2 by photosynthesis into mangrove lumber and thus decrease the possibility of a catastrophic series of events - global warming by atmospheric CO2, melting of the polar ice caps, and inundation of the great coastal cities of the world. The leaf litter and roots are the main contributors to mangrove sediments, though algal production and allochthonous detritus can also be trapped (Kristensen et al, 2008) by mangroves due to their high organic matter content and reducing nature are excellent metal retainers. Environmental pollution due to metals is of major concern. This is due to the basic fact that metals are not biodegradable or perishable the way most organic pollutants are. While most organic toxicants can be destroyed by combustion and converted into compounds such as C0, C02, SOX, NOX, metals can't be destroyed. At the most the valance and physical form of metals may change. Concentration of metals present naturally in air, water and soil is very low. Metals released into the environment through anthropogenic activities such as burning of fossils fuels, discharge of industrial effluents, mining, dumping of sewage etc leads to the development of higher than tolerable or toxic levels of metals in the environment leading to metal pollution. Of course, a large number of heavy metals such as Fe, Mn, Cu, Ni, Zn, Co, Cr, Mo, and V are essential to plants and animals and deficiency of these metals may lead to diseases, but at higher levels, it would lead to metal toxicity. Almost all industrial processes and urban activities involve release of at least trace quantities of half a dozen metals in different forms. Heavy metal pollution in the environment can remain dormant for a long time and surface with a vengeance. Once an area gets toxified with metals, it is almost impossible to detoxify it. The symptoms of metal toxicity are often quite similar to the symptoms of other common diseases such as respiratory problems, digestive disorders, skin diseases, hypertension, diabetes, jaundice etc making it all the more difficult to diagnose metal poisoning. For example the Minamata disease caused by mercury pollution in addition to affecting the nervous system can disturb liver function and cause diabetes and hypertension. The damage caused by heavy metals does not end up with the affected person. The harmful effects can be transferred to the person's progenies. Ironically heavy metal pollution is a direct offshoot of our increasing ability to mass produce metals and use them in all spheres of existence. Along with conventional physico- chemical methods, biosystem approachment is also being constantly used for combating metal pollution

NEUROBIOLOGY OF PYRIDOXINE

Pyridoxal 5'-phosphate (PLP) is the major coenzymatic form of pyridoxine. There are over one hundred known pyridoxal 5'-phosphate-dependent reactions, most of which are involved in the metabolism of various amino acids . Pyridoxamine 5'-phosphate can function in aminotransf erase reactions by the cyclic regeneration of the two active phosphate forms. Pyridoxal 5'-phosphate-dependent reactions studied in the nervous system are involved in the catabolism of various amino acids. The putative neurotransmitters , dopamine, norepinephrine , serotonin , histamine , aminobutyric acid and taurine , as well as the sphingoiipids and poly amines are synthesized by PLP-dependent enzymes. Of these enzymes, three ( glutamic acid decarboxylase , 5-hydroxytryptophan decarboxylase and crnithine decarboxylase) seem to have crucial roles (Fig. '). The clinical effects of pyridoxine deficiency can be explained on the basis of the known decreases in the activities of these enzymes

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.