Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure
Abstract: Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant … Show more
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Reagent-free Hyperspectral Diagnosis of SARS-CoV-2 Infection in saliva samples
BackgroundRapid, reagent-free pathogen-agnostic diagnostics that can be performed at the point of need are vital for preparedness against future outbreaks. Yet, many current strategies (polymerase chain reaction, lateral flow immunoassays) are pathogen-specific and require reagents; whereas others such as sequencing-based methods; while agnostic, are not (as yet) conducive for use at the point of need. Herein, we present hyperspectral sensing as an opportunity to overcome these barriers, realizing truly agnostic reagent-free diagnostics. This approach can identify both pathogen and host signatures, without complex logistical considerations, in complex clinical samples. The spectral signature of biomolecules across multiple wavelength regimes provides rich biochemical information, which, coupled with machine learning, can facilitate expedited diagnosis of disease states, the feasibility of which is demonstrated here.InnovationFirst, we present ProSpectral™ V1, a novel, miniaturized (∼8 lbs) hyperspectral platform with ultra-high (2-5 nm full-width, half-max, i.e., FWHM) spectral resolution that incorporates two mini-spectrometers (visual and near-infrared). This engineering innovation has enabled reagent-free biosensing for the first time. To enable expedient outcomes, we developed state-of-the-art machine learning algorithms for near real-time analysis of multi-wavelength spectral signatures in complex samples. Taken together, these innovations enable near-field ready, reagent-free, expedient agnostic diagnostics in complex clinical samples. Herein, we demonstrate the feasibility of this synergy of ProSpectral™ V1 with machine learning to accurately identifySARS-CoV-2 infection status in double-blinded saliva samples in real-time (3 seconds/measurement). The infection status of the samples was validated with the CDC-approved polymerase-chain reaction (PCR). We report accuracies comparable to first-in-class PCR tests. Further, we provide preliminary support that this signal is specific to SARS-CoV-2, and not associated with other respiratory conditions.InterpretationPreparedness against unanticipated pathogens and democratization of diagnostics requires moving away from technologies that demand specific reagents; and relying on intrinsic biochemical properties that can, theoretically, inform onallpathologies. Integration of hyperspectral sensors and in-line machine learning analytics, as reported here, shows the feasibility of such diagnostics. If realized to full potential, the ProSpectral™ V1 platform can enable agnostic diagnostics, thereby improving situational awareness and decision-making at the point of need; especially in resource-limited settings – enabling the distribution of newly developed tests for emerging pathogens with only a simple software update.FundingThe U.S. Department of Energy, the Defense Threat Reduction Agency, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, and Pattern Computer Inc.Research in contextEvidence before this studyOur inability to quickly and effectively deploy and use reliable diagnostics at the point of need is a major limitation in our arsenal against infectious diseases. We searched PubMed and Google Scholar for articles published before May 2024 in English applying hyperspectral sensing technologies of pathogen detection with terms, “hyperspectral,” “pathogens”, and “COVID-19”. Various factors such as speed, sensitivity, availability of reagents, deployability, requirements (expertise, resources), and others determine our choice of diagnostic. Today, diagnosis of infection remains largely pathogen-specific, requiring ligands specific to the target of interest.Indeed, Polymerase Chain Reaction (PCR)-based methods, the gold-standard technology to diagnose COVID-19, are pathogen-specific and have to be re-evaluated with the emergence of new variants. Lateral flow immunoassays, while readily deployable, are associated with lower sensitivity and specificity, and require the development of ligands, which can be time-consuming when addressing unanticipated or new threats. Select pathogen-agnostic methods such as sequencing are evolving and becoming more feasible, but still require sample processing, reagents, cold-chain, and expert handlers - and hence are not (as yet) available for routine point-of-care use. In contrast, the characterization of biochemical signatures across multiple spectral regimes (hyperspectral) can facilitate reagent-free agnostic diagnostics. Yet, many spectroscopic methods are either limited to narrow wavelength ranges; or are too large for use in the point-of-care setting; and may require complex and time-consuming analytics.Added value of this studyThis manuscript presents a paradigm-shifting miniaturized hyperspectral sensor with embedded machine learning-enabled analytics that can overcome the above limitations, making reagent-free agnostic diagnostics achievable. To our knowledge, this establishes the fastest hyperspectral diagnostic platform (3 seconds/measurement), with no preprocessing and in a small form factor, and executable with liquid (clinical) samples, without ligands or reagents. Our data demonstrates that the sensitivity of this assay is comparable to gold-standard PCR-based assays; and that the signatures are specific to COVID-19 and not associated with influenza and other respiratory pathogens – establishing the truly agnostic nature of the platform. The sensor consists of two embedded spectrometers, covering spectral bandwidth 400-1700 nm, which covers spectral patterns associated with relevant biological moieties. With appropriate data processing, we demonstrate balanced accuracies between 0·97 and 1·0 under a 10-fold cross-validation (depending on the ML/AI algorithm used for prediction).Implications of all the available evidenceWith the optimization of algorithms and analytical methods and the development of appropriate spectral databases, the ProSpectral™ hyperspectral diagnostics platform can be a flexible tool for rapid, reagent-free pathogen-agnostic detection/diagnosis of disease at the point of need, which can be a disruptive force in our preparedness to counter emerging diseases and threats.
Reagent-free Hyperspectral Diagnosis of SARS-CoV-2 Infection in saliva samples
BackgroundRapid, reagent-free pathogen-agnostic diagnostics that can be performed at the point of need are vital for preparedness against future outbreaks. Yet, many current strategies (polymerase chain reaction, lateral flow immunoassays) are pathogen-specific and require reagents; whereas others such as sequencing-based methods; while agnostic, are not (as yet) conducive for use at the point of need. Herein, we present hyperspectral sensing as an opportunity to overcome these barriers, realizing truly agnostic reagent-free diagnostics. This approach can identify both pathogen and host signatures, without complex logistical considerations, in complex clinical samples. The spectral signature of biomolecules across multiple wavelength regimes provides rich biochemical information, which, coupled with machine learning, can facilitate expedited diagnosis of disease states, the feasibility of which is demonstrated here.InnovationFirst, we present ProSpectral™ V1, a novel, miniaturized (∼8 lbs) hyperspectral platform with ultra-high (2-5 nm full-width, half-max, i.e., FWHM) spectral resolution that incorporates two mini-spectrometers (visual and near-infrared). This engineering innovation has enabled reagent-free biosensing for the first time. To enable expedient outcomes, we developed state-of-the-art machine learning algorithms for near real-time analysis of multi-wavelength spectral signatures in complex samples. Taken together, these innovations enable near-field ready, reagent-free, expedient agnostic diagnostics in complex clinical samples. Herein, we demonstrate the feasibility of this synergy of ProSpectral™ V1 with machine learning to accurately identifySARS-CoV-2 infection status in double-blinded saliva samples in real-time (3 seconds/measurement). The infection status of the samples was validated with the CDC-approved polymerase-chain reaction (PCR). We report accuracies comparable to first-in-class PCR tests. Further, we provide preliminary support that this signal is specific to SARS-CoV-2, and not associated with other respiratory conditions.InterpretationPreparedness against unanticipated pathogens and democratization of diagnostics requires moving away from technologies that demand specific reagents; and relying on intrinsic biochemical properties that can, theoretically, inform onallpathologies. Integration of hyperspectral sensors and in-line machine learning analytics, as reported here, shows the feasibility of such diagnostics. If realized to full potential, the ProSpectral™ V1 platform can enable agnostic diagnostics, thereby improving situational awareness and decision-making at the point of need; especially in resource-limited settings – enabling the distribution of newly developed tests for emerging pathogens with only a simple software update.FundingThe U.S. Department of Energy, the Defense Threat Reduction Agency, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, and Pattern Computer Inc.Research in contextEvidence before this studyOur inability to quickly and effectively deploy and use reliable diagnostics at the point of need is a major limitation in our arsenal against infectious diseases. We searched PubMed and Google Scholar for articles published before May 2024 in English applying hyperspectral sensing technologies of pathogen detection with terms, “hyperspectral,” “pathogens”, and “COVID-19”. Various factors such as speed, sensitivity, availability of reagents, deployability, requirements (expertise, resources), and others determine our choice of diagnostic. Today, diagnosis of infection remains largely pathogen-specific, requiring ligands specific to the target of interest.Indeed, Polymerase Chain Reaction (PCR)-based methods, the gold-standard technology to diagnose COVID-19, are pathogen-specific and have to be re-evaluated with the emergence of new variants. Lateral flow immunoassays, while readily deployable, are associated with lower sensitivity and specificity, and require the development of ligands, which can be time-consuming when addressing unanticipated or new threats. Select pathogen-agnostic methods such as sequencing are evolving and becoming more feasible, but still require sample processing, reagents, cold-chain, and expert handlers - and hence are not (as yet) available for routine point-of-care use. In contrast, the characterization of biochemical signatures across multiple spectral regimes (hyperspectral) can facilitate reagent-free agnostic diagnostics. Yet, many spectroscopic methods are either limited to narrow wavelength ranges; or are too large for use in the point-of-care setting; and may require complex and time-consuming analytics.Added value of this studyThis manuscript presents a paradigm-shifting miniaturized hyperspectral sensor with embedded machine learning-enabled analytics that can overcome the above limitations, making reagent-free agnostic diagnostics achievable. To our knowledge, this establishes the fastest hyperspectral diagnostic platform (3 seconds/measurement), with no preprocessing and in a small form factor, and executable with liquid (clinical) samples, without ligands or reagents. Our data demonstrates that the sensitivity of this assay is comparable to gold-standard PCR-based assays; and that the signatures are specific to COVID-19 and not associated with influenza and other respiratory pathogens – establishing the truly agnostic nature of the platform. The sensor consists of two embedded spectrometers, covering spectral bandwidth 400-1700 nm, which covers spectral patterns associated with relevant biological moieties. With appropriate data processing, we demonstrate balanced accuracies between 0·97 and 1·0 under a 10-fold cross-validation (depending on the ML/AI algorithm used for prediction).Implications of all the available evidenceWith the optimization of algorithms and analytical methods and the development of appropriate spectral databases, the ProSpectral™ hyperspectral diagnostics platform can be a flexible tool for rapid, reagent-free pathogen-agnostic detection/diagnosis of disease at the point of need, which can be a disruptive force in our preparedness to counter emerging diseases and threats.
Reagent-Free Hyperspectral Diagnosis of SARS-CoV-2 Infection in Saliva Samples
No abstract
Wound Healing Potential of Herbal Hydrogel Formulations of Cedrus brevifolia Extracts in Mice
Wound healing stands as a paramount therapeutic pursuit, imposing significant challenges on healthcare, particularly for vulnerable populations. Cedrus brevifolia, a species endemic to Cyprus, thrives in the Tripylos region, commonly known as Cedar Valley, within the Paphos forest. Despite its endemism, this species exhibits negligible genetic divergence from its Mediterranean related species. This study aims to investigate the potential of C. brevifolia resin and bark extracts in promoting wound healing in a mouse model. Previous in vitro investigations have elucidated the antioxidant and anti-inflammatory potential of extracts and isolates derived from the title plant, warranting further exploration in an in vivo setting. This experimental design employed 40 male SKH-hr2 black and brown mice aged 2–4 months. Wounds measuring 1 cm2 were meticulously induced in the anesthetized mice and the potential healing effect of the herbal hydrogel formulations was evaluated. The healing potential of the C. brevifolia extracts was rigorously assessed through the daily application of gel formulations containing resin concentrations of 5% and 10% w/w, alongside sapwood and heartwood extracts at concentrations of 0.5% and 1% w/w. The evaluation of the treatments encompassed a multifaceted approach, incorporating clinical observations, skin biophysical parameter assessments utilizing an Antera 3D camera, and FT-IR spectroscopy, in addition to histopathological examination. The chemical compositions were also investigated through NMR and bio-guided isolation. The most prominent herbal hydrogel preparation proved to be the 10% resin, followed by the sapwood at 1%. The chemical analysis unveiled abietic acid, manool, and lariciresinol derivatives that potentially contributed to the observed results. Bridging the gap between in vitro observations and in vivo outcomes attempts to shed light on the potential therapeutic benefits of C. brevifolia hydrogels in wound care.
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