Abstract:
Mechanical and chemical alterations are the signature of most diseases, such as endometriosis, sickle cell, malaria, and cancer. Detecting such alterations has vital importance for early diagnosis and efficient treatments. In the first project, we aimed to establish a non-invasive diagnostic technique for endometriosis disease by detect ing chemical changes in blood serum using Raman spectroscopy and machine learning algorithms. We measured 49 endometriosis patients’ and 45 healthy women’s blood sera for this project. A diagnostic tool was built-up by applying machine learning algorithms (kNN and SVM) to the Raman spectral data. The results of this study indicated diagnosis sensitivity and specificity values of 80.5% and 89.7%, respectively. In the following study, we investigated the effect of repetitive bi-axial stretch-relaxation cycles on the individual erythrocyte cells with optical tweezers to assess their mechan ical response. According to the findings, the cells became stiffer with each stretch and became completely undeformable after several stretch-relaxation cycles. Also, we showed that stiffness changes with each stretching cycle as a sign of weak power law. Therefore, we showed that cell rheology is scale-free at a single and whole-cell level for the first time. In the last project, we studied the camel erythrocytes compared to the human erythrocytes in their mechanochemical features since camel erythrocytes have the most distinctive cell features among the vertebrates. Using optical tweezers, we quantified that camel erythrocytes are almost ten times more rigid than human ery throcytes. The chemical analysis by the Raman spectroscopy revealed the difference in the lipid:protein ratio of these two cell types.
