Graduate Thesis Or Dissertation

 

Classification of toxins using orthogonal sensing techniques Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0c483n92f

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  • High specificity to certain class of chemical and biological agents makes biosensors unreliable for the detection of unknown agents. Also, the analytical techniques are subject to systematic errors based on the mechanism of detection leading to false negative and false positive results. Therefore, the results of these analyses must be corroborated with independent data and information from other orthogonal methods of detection. This work aims to improve the reliability of detecting potential harmful agents by using two different cell based sensing systems, fish (Betta splendens) chromatophores and algal (MesotaenIum. caidarioruin) cells. The algal sensor readout is based on well-known principles of fluorescence of living photosynthetic cell whereas the response from fish chromatophores was quantified using optical density. The cells were challenged with paraquat, mercuric chloride, sodium arsenite and clonidine. An increase in fluorescence was observed for the algal cells when dosed with paraquat due to inhibition of photosynthesis. Clonidine did not elicit substantial response from the algal cells and reduction of fluorescence was observed for the remaining two toxins owing to disruption of the chlorophyll pigment. Fish chromatophores were effective in detecting nanomolar concentrations of clonidine and micro molar concentrations of mercuric chloride and sodium arsenite. The response curves were fit using simple exponential models of the form, F(x) = a exp(-b x)+c, where a is the intercept, b refers to the decay constant ,c denotes the steady state response and x refers to the time. The parameters a, b, c were employed in the subsequent statistical analyses. The two systems were independently investigated for classification of the toxin set by performing discriminant analyses. The fish chromatophore system was able to classify 91% the toxins into their actual group whereas the algal system classification efficiency was only 72%. The combination of parameters for the two systems yielded a 100% correct toxin classification. Therefore the statistical analyses prove the hypothesis that a combination of two orthogonal sensing systems facilitates better classification of the toxins.
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