🦠 The method allows for the continuous monitoring of infection progression.
🔍 It is a highly accurate method, with a 98.9% correlation to the standard MTT assay.
⏱️ The diffraction method can provide results within two hours, compared to the 40 hours of the MTT assay.
🌍 The researchers hope to refine and broaden the use of this method for detecting viral diseases in humans, animals, and livestock.
Introduction:
A team of researchers from Harvard University and Jiangsu University has developed a novel lensless light diffraction method for detecting viral infections in cells. The method not only identifies infected cells but also tracks the progression of the infection over time. This technique could provide a more accessible, rapid, and affordable method for virus detection, with implications for understanding viral disease transmission and pathogenesis.
- The researchers used the known morphological changes in cells caused by viral infection to distinguish between infected and non-infected cells in culture.
- The standard method for identifying infected cells, the methyl thiazolyl tetrazolium (MTT) assay, is time-consuming and requires the use of reagents and chemical reactions.
- The lensless light diffraction platform developed by the researchers allows for the detection of diffraction patterns, which can be used to create unique diffraction fingerprints.
- The diffraction fingerprints can be continuously monitored in the same samples without causing damage to the cells, providing real-time information about the progression of the infection.
- The lensless light diffraction method showed a high degree of accuracy, with a 98.9% correlation to the MTT method results. It can be automated, requires shorter analysis time, and uses low-cost materials.
Conclusion:
This novel lensless light diffraction method offers a promising approach to virus detection and monitoring the progression of viral infections in cells. It eliminates the need for time-consuming and destructive reagent treatments, providing real-time information about the infection without compromising the integrity of the sample. The method has potential applications in various fields, including veterinary science, pandemic preparedness, and drug screening. Further refinements and integration with other scientific disciplines could enhance its accuracy and expand its use in industry settings.
