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Related Experiment Video

Updated: Jul 9, 2026

Simultaneous Quantification of T-Cell Receptor Excision Circles (TRECs) and K-Deleting Recombination Excision Circles (KRECs) by Real-time PCR
14:14

Simultaneous Quantification of T-Cell Receptor Excision Circles (TRECs) and K-Deleting Recombination Excision Circles (KRECs) by Real-time PCR

Published on: December 6, 2014

Optimizing TREC- and KREC-based newborn screening: Risk-stratified algorithms significantly reduce referrals.

Maarja Soomann1, Seraina Prader1, Susanna Sluka2

  • 1Division of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.

Pediatric Allergy and Immunology : Official Publication of the European Society of Pediatric Allergy and Immunology
|July 8, 2026
PubMed
Summary

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Improving newborn screening (NBS) with risk-stratified algorithms using T-cell receptor excision circles (TREC) and kappa-deleting recombination excision circles (KREC) reduces unnecessary referrals. Integrating clinical data enhances specificity while maintaining detection of primary immunodeficiencies.

Area of Science:

  • Immunology
  • Genetics
  • Pediatrics

Background:

  • Newborn screening (NBS) uses T-cell receptor excision circles (TREC) and kappa-deleting recombination excision circles (KREC) to detect T- and B-cell lymphopenia.
  • Current TREC/KREC assays have limited specificity, leading to unnecessary referrals and delayed diagnoses.

Purpose of the Study:

  • To compare risk-stratification strategies for NBS to reduce referrals without missing cases.
  • To evaluate the impact of integrating clinical data into TREC/KREC algorithms.

Main Methods:

  • Modeled TREC/KREC NBS algorithms using a 6-year Swiss NBS dataset.
  • Applied stratification approaches including cut-off adjustments and integration of gestational age (GA), postmenstrual age (PMA), and clinical data.
Keywords:
KRECNBSNBS algorithmsSCIDTRECagammaglobulinemianewborn screening

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jul 9, 2026

Simultaneous Quantification of T-Cell Receptor Excision Circles (TRECs) and K-Deleting Recombination Excision Circles (KRECs) by Real-time PCR
14:14

Simultaneous Quantification of T-Cell Receptor Excision Circles (TRECs) and K-Deleting Recombination Excision Circles (KRECs) by Real-time PCR

Published on: December 6, 2014

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Main Results:

  • Lowering cut-offs reduced abnormal results by 42% (TREC) and 64% (KREC).
  • An optimized TREC algorithm reduced referrals by 61% but missed some T-cell lymphopenias.
  • A KREC algorithm incorporating clinical data reduced referrals >10-fold, identifying most agammaglobulinemia cases.

Conclusions:

  • Risk-stratified, multistep NBS algorithms using clinical data significantly reduce unnecessary referrals.
  • Algorithmic adjustments for KREC improve specificity with minimal diagnostic loss, enhancing NBS effectiveness.