A molecularly imprinted polymer (MIP)-based impedimetric biosensor was developed for the electrochemical analysis of low-weight biological molecules. Synthetic polymeric matrices with specific and selective recognition sites, which are complementary to the shapes and sizes of the functional groups of analytes, can be prepared using the molecular imprinting method. In this study, a small molecule, tris(hydroxymethyl)aminomethane (TRIS), was used to coat a graphite pencil tip with a TRIS-containing polyacrylamide gel to fabricate a working electrode. The electrode modification and performance were evaluated using cyclic voltammetry an . . .d electrochemical impedance spectroscopy. The electrochemical properties of the modified electrodes were observed using an electrochemical cell comprising a Ag/AgCl reference electrode, a Pt wire as the counter electrode, and a pencil graphite tip as the working electrode using a redox-phosphate buffer solution with different concentrations of TRIS and Ethylenediaminetetraacetic acid (EDTA). The I–V and impedance performance of the chemically modified graphite pencil-tip electrodes exhibited decreased conductance and increased impedance correlating with the increase in TRIS concentration. Thus, MIP-based small-molecule biosensor prototypes can be promising economical replacements over other expensive sensors
Melanocytic lesions are the main cause of death from skin cancer, and early diagnosis is the key to decreasing the mortality rate. This studyassesses the role of input-vector encoding in neural network-based classification of melanocytic lesions in dermoscopy. Twelve dermoscopicmeasures from 200 melanocytic lesions are encoded by compact encoding, ACD encoding, 1-of-N encoding, normalized encoding, and rawencoding, resulting in five different input-vector sets. Feed-forward neural networks with one hidden layer and one output layer are designedwith several neurons in the hidden layer, ranging from two to twenty-two for each type of . . .input-vector set, to classify a melanocytic lesion intocommon nevus, atypical nevus, and melanoma. Accordingly, 105 networks are designed and trained using supervised learning and then testedby performing a 10-fold cross validation. All the neural networks achieve high sensitivities, specificities, and accuracies in classification. However,the network with seven neurons in the hidden layer and raw encoded dermoscopic measures as the input vector realizes the highest sensitivity(97.0%), specificity (98.1%), and accuracy (98.0%). The practical use of the network can facilitate lesion classification by retaining the neededexpertise and minimizing diagnostic variability among dermatologists
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.