Istanbul University Reports Findings in Essential Thrombocythemia (Raman Spectro scopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with G enetic Mutations and Optimization of Laser Wavelengths)
Istanbul University Reports Findings in Essential Thrombocythemia (Raman Spectro scopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with G enetic Mutations and Optimization of Laser Wavelengths)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Myeloproliferative Dis eases and Conditions - Essential Thrombocythemia is the subject of a report. Acc ording to news reporting originating in Elazig, Turkey, by NewsRx journalists, r esearch stated, "Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the an alysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended." The news reporters obtained a quote from the research from Istanbul University, "Disease poses diagnostic challenges due to overlapping mutations with other neo plasms and the presence of triple-negative cases. This study explores the potent ial of Raman spectroscopy combined with machine learning for ET diagnosis. We as sessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, pa tients having no mutations were also observed in our group, making diagnosis cha llenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confir med that both wavelengths could distinguish ET from healthy subjects. Support Ve ctor Machine (SVM) analysis revealed that the 800-1800 cm range provided the hig hest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with mult ivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Princip al Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm."
Key words
Elazig/Turkey/Eurasia/Cardiovascular Diseases and Conditions/Cyborgs/Diagnostics and Screening/Embolism and Thromb osis/Emerging Technologies/Essential Thrombocythemia/Genetics/Health and Med icine/Machine Learning/Myeloproliferative Diseases and Conditions/Risk and Pr evention/Support Vector Machines/Vascular Diseases and Conditions/Vector Mach ines
NETL
NSTL