Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria.

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.

Raman spectroscopy as a non-invasive diagnostic technique for endometriosis.

Endometriosis is a condition in which the endometrium, the layer of tissue that usually covers the inside of the uterus, grows outside the uterus. One of its severe effects is sub-fertility. The exact reason for endometriosis is still unknown and under investigation. Tracking the symptoms is not sufficient for diagnosing the disease. A successful diagnosis can only be made using laparoscopy. During the disease, the amount of some molecules (i.e., proteins, antigens) changes in the blood. Raman spectroscopy provides information about biochemicals without using dyes or external labels. In this study, Raman spectroscopy is used as a non-invasive diagnostic method for endometriosis. The Raman spectra of 94 serum samples acquired from 49 patients and 45 healthy individuals were compared for this study. Principal Component Analysis (PCA), k- Nearest Neighbors (kNN), and Support Vector Machines (SVM) were used in the analysis. According to the results (using 80 measurements for training and 14 measurements for the test set), it was found that kNN-weighted gave the best classification model with sensitivity and specificity values of 80.5% and 89.7%, respectively. Testing the model with unseen data yielded a sensitivity value of 100% and a specificity value of 100%. To the best of our knowledge, this is the first study in which Raman spectroscopy was used in combination with PCA and classification algorithms as a non-invasive method applied on blood sera for the diagnosis of endometriosis.

Spectroscopic analysis of embryo culture media for predicting reproductive potential in patients undergoing in vitro fertilization.

OBJECTIVE: To predict the reproductive potential of embryos via Raman spectroscopy evaluation of the spent culture media as well as with a conventional morphologic evaluation. MATERIALS AND METHODS: Women of reproductive age (n=31) who were treated for unexplained infertility and scheduled for single embryo transfer were invited to participate in this prospective study. After the embryos were removed from the culture, the spent culture media were stored at -80 °C after snap-freezing in liquid nitrogen. RESULTS: Fifteen patients were clinically pregnant, and 16 patients were clinically non-pregnant. Clinical pregnancy was predicted using Raman spectroscopy in 93% (14/15) of clinically pregnant patients, and in 62.5% (10 out of 16) of clinically non-pregnant patients. The sensitivity of the Raman spectroscopic analysis was 93% and the specificity was 62.5%. CONCLUSION: Metabolomic evaluation of spent embryo culture media is an emerging technique with promising objective results. However, there is clearly room for improvement.