Evidence map›Paper›PMID 37968295›Full record

ArticleScientific reports2023

Aptamer-based gold nanoparticle aggregates for ultrasensitive amplification-free detection of PSMA.

Giulia Matteoli, Stefano Luin, Luca Bellucci, Riccardo Nifosì, Fabio Beltram, Giovanni Signore

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In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

6 authors.

Giulia MatteoliFondazione Pisana Per La Scienza ONLUS, Via Ferruccio Giovanetti 13, 56017, San Giuliano Terme, PI, Italy.
Stefano LuinNational Enterprise for Nanoscience and Nanotechnology (NEST), Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy. s.luin@sns.it.
Luca BellucciNational Enterprise for Nanoscience and Nanotechnology (NEST), Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy.
Riccardo NifosìNational Enterprise for Nanoscience and Nanotechnology (NEST), Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy.
Fabio BeltramNational Enterprise for Nanoscience and Nanotechnology (NEST), Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy.
Giovanni SignoreFondazione Pisana Per La Scienza ONLUS, Via Ferruccio Giovanetti 13, 56017, San Giuliano Terme, PI, Italy. g.signore@fpscience.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early diagnosis is one of the most important factors in determining the prognosis in cancer. Sensitive detection and quantification of tumour-specific biomarkers have the potential to improve significantly our diagnostic capability. Here, we introduce a triggerable aptamer-based nanostructure based on an oligonucleotide/gold nanoparticle architecture that selectively disassembles in the presence of the biomarker of interest; its optimization is based also on in-silico determination of the aptamer nucleotides interactions with the protein of interest. We demonstrate this scheme for the case of Prostate Specific Membrane Antigen (PSMA) and PSMA derived from PSMA-positive exosomes. We tested the disassembly of the system by diameter and count rate measurements in dynamic light scattering, and by inspection of its plasmon resonance shift, upon addition of PSMA, finding appreciable differences down to the sub-picomolar range; this points towards the possibility that this approach may lead to sensors competitive with diagnostic biochemical assays that require enzymatic amplification. More generally, this scheme has the potential to be applied to a broad range of pathologies with specific identified biomarkers.

Indexed as

Aptamers, NucleotideMetal NanoparticlesProstatic NeoplasmsBiomarkers, TumorGoldHumansMaleAptamers, NucleotideBiomarkers, TumorGold

Identifiers

PMID37968295
PMCPMC10651859

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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.