Biochemical and molecular investigations on Salmonella serovars from seafood

The present investigation was envisaged to determine the prevalence and identify the different Salmonella serovar in seafood from Cochin area. Though, the distribution of Salmonella serovars in different seafood samples of Cochin has been well documented, the present attempt was made to identify the different Salmonella serovars and determine its prevalence in various seafoods. First pan of this investigation involved the isolation and identification of Salmonella strains with the help of different conventional culture methods. The identified isolates were used for the further investigation i.e. serotyping, this provides the information about the prevalent serovars in seafood. The prevalent Salmonella strains have been further characterized based on the utilization of different sugars and amino acids, to identify the different biovar of a serovar.A major research gap was observed in molecular characterization of Salmonella in seafood. Though, previous investigations reported the large number of Salmonella serovars from food sources in India, yet, very few work has been reported regarding genetic characterization of Salmonella serovars associated with food. Second part of this thesis deals with different molecular fingerprint profiles of the Salmonella serovars from seafood. Various molecular typing methods such as plasmid profiling, characterization of virulence genes, PFGE, PCR- ribotyping, and ERIC—PCR have been used for the genetic characterization of Salmonella serovars.The conventional culture methods are mainly used for the identification of Salmonella in seafood and most of the investigations from India and abroad showed the usage of culture method for detection of Salmonella in seafood. Hence, development of indigenous, rapid molecular method is most desirable for screening of Salmonella in large number of seafood samples at a shorter time period. Final part of this study attempted to develop alternative, rapid molecular detection method for the detection of Salmonella in seafood. Rapid eight—hour PCR assay has been developed for detection of Salmonella in seafood. The performance of three different methods viz., culture, ELISA and PCR assays were evaluated for detection of Salmonella in seafood and the results were statistically analyzed. Presence of Salmonella cells in food and enviromnental has been reported low in number, hence, more sensitive method for enumeration of Salmonella in food sample need to be developed. A quantitative realtime PCR has been developed for detection of Salmonella in seafood. This method would be useful for quantitative detection of Salmonella in seafood.

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.