Determination of L-selectin in blood plasma using DNA aptamer-based surface-enhanced Raman spectroscopy assay.

In the human body, tumor cell occurrence can be indirectly monitored using the L-selectin concentration in the blood, since selectin ligands are present on the surface of tumor cells, and with tumor progression, a decrease in L-selectin levels can be expected and observed. In this study, we present a selective DNA-based surface-enhanced Raman spectroscopy (SERS) assay for the detection and determination of L-selectin in biological samples. Two calibration curves (linear in the 40-190 ng mL-1 region and exponential in the 40-500 ng mL-1 region) are fitted to the obtained SERS experimental data, i.e., the ratio of I732/I1334 band intensities (LOQ = 46 ng mL-1). Calculated determination coefficients are found to be R2 = 0.997 for the linear region of the calibration curve and R2 = 0.977 for the exponential region. Moreover, we demonstrate very good selectivity of the assay even in the presence of P- and E-selectin in a sample containing L-selectin. With our SERS assay, the L-selectin concentration in biological samples can be estimated directly from the calibration curves.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Photopolymer-Based Composite with Substance Release Capability Manufactured Additively with DLP Method.

In this study, caffeine-loaded photoresin composites with homogeneous structures, suitable for additive manufacturing of transdermal microneedle systems, were obtained. The properties of the composites with varying caffeine concentrations (0.1-0.4% w/w) were investigated for carbon-carbon double bond conversion using Fourier Transform Infrared Spectroscopy, surface wettability and mechanical properties using a static tensile test and nanoindentation, and caffeine release in ethanol using UV-Vis. The caffeine concentration did not affect the final degree of double bond conversion, which was confirmed in tensile tests, where the strength and Young's modulus of caffeine-loaded samples had comparable values to control ones. Samples with 0.1 and 0.2% caffeine content showed an increase in nanohardness and reduced elastic modulus of 50 MPa and 1.5 MPa, respectively. The good wettability of the samples with water and the increase in surface energy is a favorable aspect for the dedicated application of the obtained composite materials. The amount of caffeine released into the ethanol solution at 1, 3 and 7 days reached a maximum value of 81%, was higher for the lower concentration of caffeine in the sample and increased over time. The conducted research may enhance the potential application of composite materials obtained through the digital light processing method in additive manufacturing.