Study of erythrocyte sedimentation in human blood through the photoacoustic signals analysis

Introduction

In this study, we utilized the pulsed photoacoustic (PA) technique to analyze globular sedimentation in whole human blood, with a focus on distinguishing between healthy individuals and those with hemolytic anemia.

Methods

Blood samples were collected from both healthy individuals (women and men) and those with hemolytic anemia, and temporal and spectral parameters of PA signals were employed for analysis.

Results

Significant differences (p < 0.05) were observed in PA metrics between the two groups. The proposed spectral analysis allowed significant differentiation within a 25-minute measurement window. Anemic blood samples exhibited higher erythrocyte sedimentation rate (ESR) values, indicating increased erythrocyte aggregation.

Discussion

This study underscores the potential of PA signal analysis in ESR assessment as an efficient method for distinguishing between healthy and anemic blood, surpassing traditional approaches. It represents a promising contribution to the development of precise and sensitive techniques for analyzing human blood samples in clinical settings.

Dynamic modeling of photoacoustic sensor data to classify human blood samples

The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios.

Study of Polyvinyl Alcohol Hydrogels Applying Physical-Mechanical Methods and Dynamic Models of Photoacoustic Signals

This study aims to analyze the physical-mechanical properties and dynamic models of tissue-simulating hydrogels, specifically the photoacoustic (PA) response signals, by varying the concentrations of polyvinyl alcohol (PVA) and molecular weight (MW). A state-space model (SSM) is proposed to study the PVA hydrogels to retrieve the PA-related signal's damping ratio and natural frequency. Nine box-shaped PVA hydrogels containing saline solution were used, with five concentrations of PVA (7, 9, 12, 15, 20%) for MW1 and four for MW2. The results indicated that the concentration of PVA and MW played an important role in the PA wave's amplitude, arrival time, and speed of sound over the hydrogels. The SSM parameters showed that increasing PVA and MW concentrations improved the hydrogels' ability to absorb and transfer energy under the PA effect. These parameters were also found to be correlated with density and modulus of elasticity. Additionally, the concentrations of PVA and MW affected the absorption and optical scattering coefficients. The physical-mechanical properties, including porosity, density, and modulus of elasticity, improved as the concentration of PVA and MW increased. The ultimate goal of this study is to develop hydrogels as phantoms that can be used for tissue simulation and imaging.

Loop-mediated isothermal amplification-based electrochemical sensor for detecting SARS-CoV-2 in wastewater samples

The current pandemic COVID-19 caused by the coronavirus SARS-CoV-2, has generated different economic, social and public health problems. Moreover, wastewater-based epidemiology could be a predictor of the virus rate of spread to alert on new outbreaks. To assist in epidemiological surveillance, this work introduces a simple, low-cost and affordable electrochemical sensor to specifically detect N and ORF1ab genes of the SARS-CoV-2 genome. The proposed sensor works based on screen-printed electrodes acting as a disposable test strip, where the reverse transcription loop-mediated isothermal amplification (RT-LAMP) reaction takes place. Electrochemical detection relies upon methylene blue as a redox intercalator probe, to provide a diffusion-controlled current encoding the presence and concentration of RT-LAMP products, namely amplicons or double-stranded DNA. We test the performance of the sensor by testing real wastewater samples using end-point and time course measurements. Results show the ability of the electrochemical test strip to specifically detect and quantify RT-LAMP amplicons below to ~ 2.5 × 10^-6 ng/μL exhibiting high reproducibility. In this sense, our RT-LAMP electrochemical sensor is an attractive, efficient and powerful tool for rapid and reliable wastewater-based epidemiology studies.