Study of chemical properties and evaluation of collagen in mantle, epidermal connective tissue and tentacle of Indian Squid, Loligo duvauceli Orbigny

The chemical composition and evaluation of Indian squid (Loligo duvauceli) mantle, epidermal connective tissue and tentacle is investigated in this current study. It is observed that squid mantle contains 22.2% total protein; 63.5% of the total protein is myofibrillar protein. The unique property of squid myofibrillar protein is its water solubility. Squid mantle contains 12.0% total collagen. Epidermal connective tissue has highest amounts of total collagen (17.8%). SDS-PAGE of total collagen identified high molecular weight α-, β- and γ- sub-chains. Amino acid profile analysis indicates that mantle and tentacle contain essential amino acids. Arginine forms a major portion of mantle collagen (272.5 g/100 g N). Isoleucine, glutamic acid and lysine are other amino acids that are found in significantly high amounts in the mantle. Sulphur containing cystine is deficit in mantle collagen. Papain digest of mantle and epidermal connective tissue is rich in uronic acid, while papain digest, collagenase digest and urea digest of epidermal connective tissue has significant amounts of sialic acid (25.2, 33.2 and 99.8 μmol /100 g, respectively). PAS staining of papain digest, collagenase digest and urea digest also identify the association of hexoses with low molecular weight collagen fragments. Histochemical sectioning also emphasized the localized distribution of collagen in epidermal and dermal region and very sparse fibres traverse the myotome bundles

Histological, Histochemical and Biochemical characterisation of male morphotypes of Macrobrachium rosenbergii (de Man)

This thesis entitled “Histological ,Histochemical and biochemical characterisation of male morphotypes of macrobrachium rosenbergii(De Man). The giant fresh water prawn Macrobrachium rosenbergii (de Man) is emerging as a prime candidate species in fresh water aquaculture as a global basis and therefore, receiving much attention in recent years.the present work was aimed at to study the histological variations, if any, in the reproductive system viz. testes, vas deferens including androgenic gland, hepatopancreas and the neurosecretory system viz. eye stalk, brain and thoracic ganglion among the male morphotypes and their transitional stages of M.rosenbergii from growouts. This study was also aimed at to bring out the histochemical variations, if any, in the reproductive system comprising of testes, vas deferens including androgenic gland and the hepatopancreas among the male morphotypes and their transitional stages collected from growouts. Biochemical characterisation of various male morphotyes and their transitional stages have also been attempted in order to find out biochemical evidence, if any, in the morphotypic transformation.Histological study of the testes of three male morphotypes viz., SM, SOC and SBC and their 'transitional stages viz., WOC, tSOC, WBC and OBC have been carried out with a view to unravel the structural and functional differences of the testes, if any, of these morphotypes. Studies on the lipid components viz., cholesterol, phospholipid and triglyceride In the muscle tissue and hepatopancreas have been carried out.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.