Evidence map›Paper›PMID 40772350›Full record

ArticleAdvanced healthcare materials2025

Multiscale Profiling of Nanoscale Metal-Organic Framework Biocompatibility and Immune Interactions.

Yunhui Zhuang, Bárbara B Mendes, Dhruv Menon, Jhenifer Oliveira, Xu Chen, Fatma Demir Duman, João Conniot, Sergio Mercado, Xiewen Liu, Shi-Yuan Zhang and 3 more

Abstract read
In one paragraph

Article in Advanced healthcare materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
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  4. DNA-mimic for Specific Surface Functionalization of Zr-MOFs for Bacterial Targeting.Angewandte Chemie (International ed. in English) · 2026
    Article
  5. Review
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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

13 authors.

Yunhui ZhuangAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Bárbara B MendesComprehensive Health Research Centre (CHRC), NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Dhruv MenonAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Jhenifer OliveiraComprehensive Health Research Centre (CHRC), NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Xu ChenComprehensive Health Research Centre (CHRC), NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Fatma Demir DumanAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
João ConniotComprehensive Health Research Centre (CHRC), NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Sergio MercadoAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Xiewen LiuAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Shi-Yuan ZhangAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
João CondeComprehensive Health Research Centre (CHRC), NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Rachel E HewittCellular Imaging and Analysis Facility, Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, CB3 0ES, UK.
David Fairen-JimenezAdsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.ORCID 0000-0002-5013-1194

Funding

Asthma and Lung UKDepartment of Veterinary Medicine, University of CambridgeEngineering and Physical Sciences Research Council EP/S009000/1Engineering and Physical Sciences Research Council EP/S022953/1EPSRC EP/S009000/1EPSRC EP/S022953/1H2020 European Research Council ERC-2016-COG 726380
6 · The paper itself

Abstract

The clinical translation of metal-organic frameworks (MOFs) - a promising class of porous materials for nanomedicine - is hindered by a poor understanding of their complex interactions with the immune system and in vivo immunotoxicity. To address this gap, a hierarchical "Safety-by-Design" pipeline is established and validated, integrating machine learning (ML) with ex vivo human blood studies and targeted in vivo models. This multi-stage workflow enables the systematic profiling of MOF immunotoxicity, de-risking their development. The power of this approach is demonstrated using four clinically relevant MOFs - NU-901, PCN-222, UiO-66, and ZIF-8 - revealing distinct, framework-dependent immune fingerprints. The initial in silico screening correctly flagged NU-901 and ZIF-8 as potential hazards to human health. These predictions are subsequently validated ex vivo, where NU-901 is confirmed to be selectively cytotoxic to CD14

Indexed as

Biocompatible MaterialsMetal-Organic FrameworksAnimalsHumansMachine LearningMiceMonocytesBiocompatible MaterialsMetal-Organic Frameworkscytokinesimmunotoxicitymachine learningmetal‐organic frameworks (MOFs)monocytes

Identifiers

PMID40772350
PMCPMC12616609

What OpenQuestion holds

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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.