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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta

Angeli Ambayya1,2, Santina Sahibon3, Thoo Wei Yang4

  • 1Haematology Department, Hospital Ampang, Ampang 68000, Selangor, Malaysia.

Diagnostics (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

A new algorithm using red blood cell (RBC) and reticulocyte parameters can rapidly and cost-effectively differentiate alpha (α) and beta (β)-thalassemia traits. This screening tool shows high accuracy, aiding diagnosis in high-burden regions.

Keywords:
VCS parametersalgorithmcell population dataα-thalassemiaβ-thalassaemia

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Area of Science:

  • Hematology
  • Medical Diagnostics
  • Genetics

Background:

  • Thalassemia is a significant inherited blood disorder prevalent in Southeast Asia.
  • Accurate and efficient screening for α and β-thalassemia traits is crucial for public health initiatives.

Purpose of the Study:

  • To evaluate the efficacy of red blood cell (RBC) and reticulocyte cell population data (CPD) parameters for discriminating between α and β-thalassemia traits.
  • To develop and validate a novel algorithm for rapid and cost-effective screening of thalassemia traits.

Main Methods:

  • A total of 1597 subjects were analyzed, including healthy individuals and those with α or β-thalassemia traits.
  • Red blood cell (RBC) and reticulocyte cell population data (CPD) parameters were obtained using the Unicel DxH800.
  • A novel αβ-algorithm was developed and validated through in-house and interlaboratory prospective studies.

Main Results:

  • The developed αβ-algorithm demonstrated high diagnostic performance with an AUC of 0.966, 92% sensitivity, and 90% specificity.
  • The algorithm effectively discriminated between α and β-thalassemia traits with a cut-off value of 1742.5.
  • Prospective studies confirmed the algorithm's specificity and reproducibility across different cohorts.

Conclusions:

  • The αβ-algorithm serves as a rapid, inexpensive, and accurate surrogate tool for differentiating α and β-thalassemia traits.
  • This algorithm can significantly aid population screening for thalassemia traits in regions with a high prevalence of these disorders.
  • The study highlights the utility of routine hematological parameters in diagnosing inherited blood disorders.