Evidence map›Paper›PMID 41473318›Full record

ArticlebioRxiv : the preprint server for biology2025

Machine learning-based development of Gadolinium binding peptides.

Nir A Dayan, Makayla Long, Nicolas Scalzitti, Iliya Miralavy, Daniel Holmes, Mark Kocherovsky, Wolfgang Banzhaf, Assaf A Gilad

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

8 authors.

Nir A DayanDepartment of Chemical Engineering & Material Science, Michigan State University, East Lansing, Michigan 48824, USA.ORCID 0000-0002-3695-0986
Makayla LongMichigan State University College of Human Medicine, Grand Rapids, Michigan 49503, USA.
Nicolas ScalzittiDepartment of Chemical Engineering & Material Science, Michigan State University, East Lansing, Michigan 48824, USA.ORCID 0000-0002-2477-3054
Iliya MiralavyDepartment of Computer Science and Engineering, Michigan state university, 48824 East Lansing, Michigan 48824, USA.ORCID 0000-0003-2247-5253
Daniel HolmesDepartment of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA.ORCID 0000-0001-8275-9237
Mark KocherovskyDepartment of Computer Science and Engineering, Michigan state university, 48824 East Lansing, Michigan 48824, USA.ORCID 0000-0002-7313-2325
Wolfgang BanzhafDepartment of Computer Science and Engineering, Michigan state university, 48824 East Lansing, Michigan 48824, USA.ORCID 0000-0002-6382-3245
Assaf A GiladDepartment of Chemical Engineering & Material Science, Michigan State University, East Lansing, Michigan 48824, USA.ORCID 0000-0002-7272-7570

Funding

Clinically Translatable MRI Reporter Genes and Imaging Methods with Ultra-High Specificity and SensitivityR01EB031008 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · 2023 to 2025
$2.0M
NIBIB NIH HHS R01 EB030565NIBIB NIH HHS R01 EB031008NIBIB NIH HHS R01 EB031936
6 · The paper itself

Abstract

Gadolinium-based contrast agents (GBCAs) are indispensable tools in magnetic resonance imaging (MRI), yet their clinical use is limited by non-specific tissue accumulation, low molecular specificity, and safety concerns. Protein and peptide scaffolds provide a promising alternative because they can bind metal ions with high selectivity and enable precise molecular targeting. However, identifying short peptide motifs with optimal gadolinium (Gd

Indexed as

Gadolinium-based contrast-agentsMachine-learningMagnetic resonance imagingSynthetic biology

Identifiers

PMID41473318
PMCPMC12746035

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.