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Related Concept Videos

Linearization and Approximation01:26

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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
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Related Experiment Video

Updated: Jan 21, 2026

Fabrication and Validation of an Organ-on-chip System with Integrated Electrodes to Directly Quantify Transendothelial Electrical Resistance
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Integrated BSI bacteria identifier-on-chip using approximate k-mer matching.

Esteban Garzón1,2, Victor Galindo2, Yuval Harary2

  • 1Department of Computer Engineering, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria (UNICAL), Rende, 87036, Italy.

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|January 19, 2026
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Summary

Rapid detection of bloodstream infection (BSI) bacteria is crucial for patients undergoing fecal microbiota transplantation for graft-versus-host disease (GVHD). A novel system-on-chip, PC-CAM, achieves real-time pathogen identification to prevent deadly BSIs.

Keywords:
Approximate searchCAMContent addressable memoryDNA detection and classificationHamming DistanceRISC-V application processorSoC

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Area of Science:

  • Biomedical Engineering
  • Infectious Disease Diagnostics
  • Genomic Technology

Background:

  • Acute graft-versus-host disease (GVHD) poses a significant mortality risk, often treated with fecal microbiota transplantation.
  • Fecal microbiota transplantation for GVHD can lead to life-threatening bloodstream infections (BSIs), necessitating rapid pathogen detection.

Purpose of the Study:

  • To develop and evaluate PC-CAM, a system-on-chip for real-time detection of bacterial pathogens in GVHD patients.
  • To assess the efficiency, performance, and resource utilization of PC-CAM for BSI prevention.

Main Methods:

  • Utilized a novel pathogen identification system-on-chip (PC-CAM) employing Approximate search-capable Content Addressable Memory (ACAM).
  • Applied PC-CAM for real-time bacterial genome detection in blood and stool samples from GVHD patients.
  • Evaluated PC-CAM performance through silicon measurements, focusing on identification efficiency, speed, area, and power consumption.

Main Results:

  • PC-CAM demonstrated high-speed classification of 960K short DNA reads per second.
  • The system operates within a compact silicon area of 2.38mm² and consumes minimal power (1.27mW).
  • PC-CAM achieved efficient and accurate real-time bacterial identification in clinical samples.

Conclusions:

  • PC-CAM offers a promising solution for rapid, point-of-care pathogen detection to mitigate BSI risks in GVHD patients.
  • The energy-efficient and accurate performance of PC-CAM supports its deployment in clinical settings for improved patient outcomes.
  • This technology facilitates real-time, privacy-preserving, and user-friendly pathogen classification.