Publications by authors named "Desiree Requena-Lancharro"

Article Synopsis
  • A study was conducted to test a new method for detecting SARS-CoV-2 using polarimetric imaging, focusing on engineered virus models in different biofluids.
  • The researchers analyzed samples with a polarimetric camera, measuring the angles of maximum linear polarization of scattered light to differentiate viral particles.
  • Findings suggest that polarimetric imaging could enhance rapid detection of SARS-CoV-2 and other pathogens in dry fluid samples, but further testing with real viral particles is needed for practical application.
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Background: Obstructive failure of implanted shunts is the most common complication in the treatment of hydrocephalus. Biological material and debris accumulate in the inner walls of the valve and catheters block the normal flow of the drained cerebrospinal fluid causing severe symptoms with high morbidity and mortality. Unfortunately, at present, there is no effective preventive protocol or cleaning procedure available.

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Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels.

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Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks.

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