Evidence map›Paper›PMID 40082707›Full record

ArticleCommunications engineering2025

Allele-specific electrical genotyping for diagnosis of transthyretin amyloidosis.

Muhammad Tayyab, Neeru Gandotra, Jianye Sui, Curt Scharfe, Mehdi Javanmard

Abstract read
In one paragraph

Article in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muhammad Tayyab *Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA.ORCID http://orcid.org/0000-0002-4372-504X
Neeru Gandotra *Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Jianye SuiDepartment of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA.
Curt ScharfeDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA. curt.scharfe@yale.edu.ORCID http://orcid.org/0000-0001-6322-2039
Mehdi JavanmardDepartment of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA. mehdi.javanmard@rutgers.edu.ORCID http://orcid.org/0000-0002-6297-392X

Funding

Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders.R01HD102537 · NICHD · YALE UNIVERSITY · PI SCHARFE, CURT · 2020 to 2024
$3.0M
National Science Foundation (NSF) 1556253NICHD NIH HHS R01 HD102537
6 · The paper itself

Abstract

Clinical genetic testing often takes days to weeks, but rapid, affordable tests during outpatient visits could significantly benefit patients. This is crucial for detecting common, actionable point mutations, such as those linked to hereditary transthyretin (TTR) amyloidosis, which is often underdiagnosed in individuals of West African ancestry with congestive heart failure. Here we developed a method for detecting known DNA variants using allele-specific polymerase chain reaction (ASPCR) and electrical impedance. Oligonucleotide primers were designed to selectively amplify the ancestral and variant allele of TTR c.424G>A (p.Val142Ile). PCR products were detected in solution using passive-flow microfluidic impedance cytometry across multiple excitation frequencies. Impedance responses correlated with DNA concentration, allowing for the calculation of a robust DNA quantification score. TTR V142I genotypes from six patients (four heterozygous, two wildtype) matched results from gel electrophoresis and sequencing. Future improvements will focus on reducing DNA input requirements and enabling multiplex variant detection.

Identifiers

PMID40082707
PMCPMC11906782

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.