This research introduces a new approach based on Raman spectroscopy for quickly and effectively detecting brain tumors at a macroscopic scale, making it suitable for intra-operative use. By focusing on a specific vibrational band at 1440 cm-1 as a cancer biomarker, this method will enable rapid imaging of a field of view spanning several centimeters in approximately 5 seconds. The results of this study demonstrated high sensitivity/specificity for meningioma (97%/95%), brain metastases (95%/91%), and glioblastoma (78%/84%). The performance of this developed imaging system was compared to a custom hyperspectral line-scanning Raman system as the gold standard.
We present a rapid, portable optical system for label-free detection of COVID-19. Raman spectra from an entire liquid drop of saliva supernatant can be obtained within 6 minutes, and the sample is classified as COVID-19 positive or negative using artificial intelligence (AI).
293 COVID negative and 49 COVID positive saliva supernatant samples were analyzed. Positive samples were from hospitalized patients (non-critical and critical) and non-hospitalized testing clinic volunteers (symptomatic and asymptomatic). Our Raman/AI system has an 82% accuracy detecting people with COVID-19 of any severity with any symptom presentation, and 89% accuracy when detecting COVID-19 in hospitalized patients alone. Rapid label-free analysis of biofluids for viruses could provide a low-cost screening solution that could be adapted to respond to viral mutations. This could be invaluable for future pandemics and for reducing infections in hospitals, care homes and workplaces.
Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus.
Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents.
Approach: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique—Raman spectroscopy—to detect changes in the molecular profile of saliva associated with COVID-19 infection.
Results: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.
Conclusion: These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.
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