Indian- American Scientist Sangeeta Bhatia developed cheap paper diagnostic for cancer

Screenshot_2The Indian-American scientist, Ms. Sangeeta Bhatia developed a simple, cheap, paper test that could improve cancer diagnosis rates and help people to get treated earlier. Ms. Bhatia is professor at Massachusetts Institute of Technology (MIT) and Howard Hughes Medical Institute.
About the paper diagnostic test for cancer
The diagnostic, which works much like a pregnancy test, could reveal within minutes, based on a urine sample, whether a person has cancer. The synthetic biomarker technology relies on nano-particles that interact with tumour proteins called proteases, each of which can trigger release of hundreds of biomarkers that are then easily detectable in a patient’s urine.

  • Concept of synthetic biomarker technology: To amplify signals from tumor proteins (that would be hard to detect on their own) by injecting special nanoparticles into the body.
  • These proteins are known as Matrix Metallo Proteinase (MMP) that help cancer cells to escape from their original locations by cutting through proteins of the extra cellular matrix, which normally holds cells in place.
  • These nanoparticles, coated with peptides (short protein fragments), can interact with tumour proteins called proteases. In a patient’s body, these particles congregate at tumour sites, where cancer proteases cleave the peptides, which then accumulate in the kidneys and are excreted in the patient’s urine.
  • These biomarkers are easily detectable using an approach known as a lateral flow assay, the same technology used in pregnancy tests. In tests in mice, the researchers were able to accurately identify colon tumours, as well as blood clots. 

Note: As per the current version of the technology, patients would first receive an injection of the nanoparticles, and then urinate onto the paper test strip. To make the process more convenient, the researchers are now working on a nanoparticle formulation that could be implanted under the skin for longer-term monitoring.



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