Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.

Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation of glioblastoma G4 (GBM) and two types of meningiomas (atypical - MAtyp and angiomatous - MAng) were done using Fourier Transform InfraRed (FTIR) spectroscopy, combined with statistical, multivariate, machine learning and rate of spectrum changes methods.

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses.

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore, there is a need for new methods that will clearly indicate the marker qualifying the patient for surgery.

Modeling Absorption Dynamics of Differently Shaped Gold Glioblastoma and Colon Cells Based on Refractive Index Distribution in Holotomographic Imaging.

Herein, it is demonstrated that the toxic effect of gold nanoparticles (Au NPs) on three different cancer cell lines (U-118 and LN-299 glioblastoma and HCT-116 colon) depends on their absorption dynamics by cells, related to the shapes of the NPs. This hypothesis is confirmed by showing that i) based on refractive index (RI) values, typical for cell components and gold nanoparticles, it is possible to show the absorption dynamics and accumulation locations of the latter ones inside and outside of the cells. Moreover, ii) the saturation of the accumulated Au NPs volume in the cells depends on the nanoparticle shape and is reached in the shortest time for star-shaped Au NPs (AuS NPs) and in the longest time for spherical Au NPs (AuSph NPs) and on the cancer cells, where the longest and the shortest saturation are noticed for HCT-116 and LN-229 cells, respectively.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis.

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia.

Optical monitoring of hemodialysis using noninvasive measurement of uric acid in the dialysate.

The aim of this study was to present a methodology for predicting changes in uric acid concentrations in the blood of chronically hemodialyzed patients based on an optical measurement of the intensity of selected wavelengths in the dialysate. Blood samples were taken from the arterial line every 30 min throughout the hemodialysis period, to measure uric acid levels. Simultaneously, optical measurements were made on dialysate flowing from the dialyzer.