Login / Signup

A smartphone-integrated low-cost, reagent-free, non-destructive dried blood spot-based paper sensor for hematocrit measurement.

Smriti SinhaAkashlina BasuJai ShuklaShirin DasguptaGorachand DuttaSoumen Das
Published in: Analytical methods : advancing methods and applications (2023)
The blood hematocrit (Hct) level provides vital information about a person's health. Traditional Hct measurement equipment relies heavily on infrastructure and skilled manpower, limiting its broad implementation in resource-limited contexts. Therefore, we developed a simple, reagent-free, non-destructive, smartphone-integrated paper-based device for Hct measurement by analyzing blood-spreading area on a paper substrate. Blood spreading area was found to be dependent on the Hct value, paper properties, and assay duration. This device was calibrated using a custom-made Python algorithm with 10 μl of blood, which produced a sensitivity of -1.90 ± 0.03 mm 2 /Hct (%) with a LOD as low as 2.17% Hct. The device linear range (8.8 to 58% Hct) is wide enough to cover the clinically relevant range of blood Hct (%). Furthermore, this Python algorithm was coupled with a user-friendly and clinically beneficial Android application (app) to establish an automated tool for quantitative estimation. Comparing the app performance with the result obtained from the gold standard hematology analyzer using blood from 87 subjects reveals a strong correlation ( r = 0.99), an average bias of 0.15 with limits of agreement of -2.5 to 2.79 at 95% CI. The device exhibits an accuracy of 96.85% and acceptable reproducibility, with CV ranging from 0.8 to 7.5%. An integrated detection cum readout guiding pattern may allow this device to be suitable for simultaneous quantitative and qualitative estimation and to be employed in both developed and resource-limited clinical settings for Hct measurement in routine checkups and regular monitoring during critical care, as well as in the initial screening of large anemic populations.
Keyphrases
  • cell cycle arrest
  • healthcare
  • low cost
  • cell death
  • machine learning
  • mental health
  • deep learning
  • high throughput
  • social media
  • health information
  • pi k akt
  • single cell
  • quality improvement
  • amino acid
  • label free