Evidence map›Paper›PMID 41970826›Full record

ArticleACS omega2026

Ionic Strength Impact on miR-141 Detection by bioFET.

Francesco Lavecchia di Tocco, Davide Atzei, Anna Rita Bizzarri

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Francesco Lavecchia di ToccoBiophysics and Nanoscience Centre, DEB, Largo dell'Università, Università della Tuscia, 01100 Viterbo, Italy.ORCID https://orcid.org/0009-0003-2509-8307
Davide AtzeiBiophysics and Nanoscience Centre, DEB, Largo dell'Università, Università della Tuscia, 01100 Viterbo, Italy.
Anna Rita BizzarriBiophysics and Nanoscience Centre, DEB, Largo dell'Università, Università della Tuscia, 01100 Viterbo, Italy.ORCID https://orcid.org/0000-0003-3298-6639

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of microRNAs (miRNAs), which are small noncoding RNAs whose deregulation is strongly associated with various diseases, represents a progressively more important issue in clinical diagnostics. Among other approaches, field-effect transistor biosensors (bioFETs), characterized by rapid, accurate, and label-free detection, provide powerful platforms to develop miRNA-devoted detection devices. Biosensing via bioFETs primarily depends on the ability of complementary probes, immobilized on electrodes, to specifically capture miRNAs and transduce the binding into a detectable electrical signal. A critical factor for bioFET performance is the ionic strength. On the one hand, it reduces electrostatic repulsion, facilitating the association between negatively charged strands; on the other hand, it increases charge screening, which affects the detection capabilities. The balance between these opposite effects remains poorly understood, limiting the optimization of assay conditions. Here, we investigate the impact of ionic strength on detecting the clinically relevant miRNA 141 (miR-141) by acquiring the electrical signals over time at progressively higher concentrations of target using a custom-made extended gate (EG) bioFET. We found that both the hybridization affinity and the detected charge of the target are slightly affected by the ionic strength, which essentially modulates the organization of the capturing layer. Optimal analytical performance is achieved at 200-300 mM ionic strength, providing useful insights into reliable label-free miRNA detection by bioFETs under different conditions and supporting their potential future application in clinical settings.

Identifiers

PMID41970826
PMCPMC13063041

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LicenceCC BY-NC-ND
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Registered trials

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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.