Biology subjects

Boodaghidizaji, M.

Publications and source records attributed to Boodaghidizaji, M..

3 recordsLinked to original sources

bioRxiv2025

Limit of detection of Raman spectroscopy using polystyrene particles from 25 to 1000 nm in aqueous suspensions

Raman spectroscopy is an analytical method capable of detecting various microorganisms and small particles. Here, we used 25-1000 nm polystyrene particles in aqueous suspensions, which are comparable in size to viral particles and viral aggregates, to determine the limit of detection of a confocal Raman microscope. We collected Raman spectra using a 785 nm wavelength laser with a power of 300 mW and a 10 s exposure time, with a 5X objective lens. We detected the most prominent peak of the polystyrene particles at 1001 cm-1, corresponding to the ring breathing mode. We established the minimum and maximum limit of detection (LODmin and LODmax) using a partial least squares (PLS) model. The LOD of the smallest size of 50 nm was identified as 7.47 x 1012 -7.64 x 1012 particle/mL, and for the largest size of 1000 nm, 6.09 x 108 -6.24 x 108 particle/mL. We demonstrated that Raman spectroscopy was non-destructive under these conditions by comparing the particle size before and after collecting Raman spectra using dynamic light scattering. Due to their size similarity to viral particles and viral aggregates, this systematic characterization of polystyrene particles provides detailed information on their Raman spectral signatures in aqueous suspensions. These findings establish a foundation for using Raman spectroscopy for the detection of small particles in aqueous suspensions and highlight its potential as a tool for real-time monitoring in vaccine manufacturing.

bioengineering↗

bioRxiv2024

Real-time monitoring of attenuated cytomegalovirus using Raman spectroscopy allows non-destructive characterization during flow

Real-time monitoring of viral particles can have a crucial impact on vaccine manufacturing and can alleviate public health by supporting continuous supply. Spectroscopic methods such as Raman spectroscopy can provide rapid and non-invasive measurements. Here, we have developed a Raman spectroscopy-based tool to monitor the quality and quantity of viral particles in a continuous flow set-up. The attenuated human cytomegalovirus (CMV) is characterized across a wide range of concentrations (4.50 x 109 to 2.90 x 1011 particles/mL) and flow rates (100 {micro}m/s to 1000 {micro}m/s) within a square quartz capillary. This process analytical technology (PAT) tool enables the detection of viral particles even at high flow rates such as 1000 {micro}m/s. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and dynamic light scattering (DLS) demonstrated that the samples maintain their integrity even after laser exposure, reiterating the non-invasive nature of Raman spectroscopy. To the best of our knowledge, this is the first report on characterizing CMV particles using Raman spectroscopy. We have also demonstrated the limit of detection (LoD) (2.36 x 1010 particles/mL) for CMV particles in continuous flow (via the Raman spectroscopy method) by addressing the effect of flow rate, concentration, and integrity of samples. This technology could enhance our understanding of the quality control in bio-manufacturing processes required in vaccine production.

bioengineering↗

bioRxiv2023

Machine learning-based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases

Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disrupted gut microbiota composition and function (dysbiosis) has been found in multiple studies. Thus, human microbiome data has a potential to be a great source of information for the diagnosis and disease characteristics (phenotypes, disease course, therapeutic response) of diseases with dysbiotic microbiota community. However, multiple attempts to leverage gut microbiota taxonomic data for diagnostic and disease characterization have failed due to significant inter-individual variability of microbiota community and overlap of disrupted microbiota communities among multiple diseases. One potential approach is to look at the microbiota community pattern and response to microbiota modifiers like dietary fiber in different disease states. This approach is now feasible by availability of machine learning that is able to identify hidden patterns in the human microbiome and predict diseases. Accordingly, the aim of our study was to test the hypothesis that application of machine learning algorithms can distinguish stool microbiota pattern and microbiota response to fiber between diseases where overlapping dysbiotic microbiota have been previously reported. Here, we have applied machine learning algorithms to distinguish between Parkinsons disease, Crohns disease (CD), ulcerative colitis (UC), human immune deficiency virus (HIV), and healthy control (HC) subjects in the presence and absence of fiber treatments. We have shown that machine learning algorithms can classify diseases with accuracy as high as 95%. Furthermore, machine learning methods applied to the microbiome data to predict UC vs CD led to prediction accuracy as high as 90%.

bioinformatics↗