Evidence map›Paper›PMID 40670416›Full record

ArticleScientific reports2025

Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm.

Fatemeh Hataminia, Anahita Azinfar

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Fatemeh HataminiaDepartment of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Hataminiaf2@mums.ac.ir.
Anahita AzinfarSchool of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. These parameters serve as input variables for the model, while the relaxation rate R

Indexed as

Iron oxide nanoparticlesMachine learningMagnetic resonance imaging (MRI)RBF neural network

Identifiers

PMID40670416
PMCPMC12267469

What OpenQuestion holds

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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.