Evidence map›Paper›PMID 41147065›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2025

From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.

M Brittany Johnson, Sankalp Jain, Jessica McMillan Shea, Quinton Krueger, Erwin Doe, Daniel Miller, Katelynn Pranger, Hannah Hayth, Sable Thornburgh, Justin Halman and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. 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

13 authors.

M Brittany JohnsonDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, USA.
Sankalp JainNational Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.
Jessica McMillan SheaNanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
Quinton KruegerDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, USA.
Erwin DoeDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Daniel MillerDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Katelynn PrangerDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Hannah HaythDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Sable ThornburghDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Justin HalmanNanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
Emil F KhisamutdinovDepartment of Chemistry, Ball State University, Muncie, IN, 47306, USA.
Alexey V ZakharovNational Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.
Kirill A AfoninNanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.ORCID 0000-0002-6917-3183

Funding

SMART NANPs: new molecular platform for communication with human immune system and modulation of therapeutic responsesR35GM139587 · NIGMS · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI AFONIN, KIRILL A · 2021 to 2025
$1.8M
Nucleic Acid Nanoparticle-based Monoclonal Antibody MimicsR15EB031388 · NIBIB · BALL STATE UNIVERSITY · PI KHISAMUTDINOV, EMIL · 2021 to 2025
$1.0M
NIBIB NIH HHS R15 EB031388NIBIB NIH HHS R15EB031388NIGMS NIH HHS R35 GM139587NIGMS NIH HHS R35GM139587
6 · The paper itself

Abstract

Nucleic acid nanoparticles (NANPs) represent a versatile platform for drug delivery and modulation of therapeutic responses. To expedite NANPs' translation from bench to bedside, rapid coordination of their design principles with immunostimulatory assessment is essential. Here, a deep learning framework is presented to predict cytokine responses, specifically interferon-beta (IFN-β) and interleukin-6 (IL-6), induced by NANPs in human microglial cells based solely on their sequences. Using a transformer-based architecture augmented through systematic strand permutation trained on 176 structurally diverse, individually assembled, and experimentally characterized NANPs, the model achieved high predictive performance in cross-validation (R

Indexed as

NanoparticlesNucleic AcidsDeep LearningHumansInterferon-betaInterleukin-6MicrogliaNeural Networks, ComputerQuantitative Structure-Activity RelationshipInterferon-betaInterleukin-6Nucleic Acidsartificial intelligenceimmune stimulationmicroglianew approach methodologies (NAMs)nucleic acid nanoparticles (NANPs)quantitative structure‐activity relationship (QSAR)

Identifiers

PMID41147065
PMCPMC12710182

What OpenQuestion 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 OpenQuestion graph.