Evidence map›Paper›PMID 35568919›Full record

ArticleCell & bioscience2022

Nanotechnological engineering of extracellular vesicles for the development of actively targeted hybrid nanodevices.

Bianca Dumontel, Francesca Susa, Tania Limongi, Veronica Vighetto, Doriana Debellis, Marta Canta, Valentina Cauda

Abstract read
In one paragraph

Article in Cell & bioscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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  4. Endogenous Engineering Reprograms Extracellular Vesicles for Enhanced Therapeutic Function.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

7 authors.

Bianca Dumontel *Department of Applied Science and Technology, Politecnico di Torino, Turin, Italy.
Francesca Susa *Department of Applied Science and Technology, Politecnico di Torino, Turin, Italy.
Tania Limongi *Department of Applied Science and Technology, Politecnico di Torino, Turin, Italy.
Veronica VighettoDepartment of Applied Science and Technology, Politecnico di Torino, Turin, Italy.
Doriana DebellisElectron Microscopy Facility, Istituto Italiano di Tecnologia (IIT), Genoa, Italy.
Marta CantaDepartment of Applied Science and Technology, Politecnico di Torino, Turin, Italy.
Valentina CaudaDepartment of Applied Science and Technology, Politecnico di Torino, Turin, Italy. valentina.cauda@polito.it.ORCID http://orcid.org/0000-0003-2382-1533

Funding

H2020 European Research Council 678151H2020 European Research Council 957563
6 · The paper itself

Abstract

backgroundWe propose an efficient method to modify B-cell derived EVs by loading them with a nanotherapeutic stimuli-responsive cargo and equipping them with antibodies for efficient targeting of lymphoma cells.

resultsThe post-isolation engineering of the EVs is accomplished by a freeze-thaw method to load therapeutically-active zinc oxide nanocrystals (ZnO NCs), obtaining the so-called TrojanNanoHorse (TNH) to recall the biomimetism and cytotoxic potential of this novel nanoconstruct. TNHs are further modified at their surface with anti-CD20 monoclonal antibodies (TNH

conclusionThis work demonstrates the efficient re-engineering of EVs, derived from healthy cells, with inorganic nanoparticles and monoclonal antibodies. The obtained hybrid nanoconstructs can be on-demand activated by an external stimulation, here acoustic pressure waves, to exploit a cytotoxic effect conveyed by the ZnO NCs cargo against selected cancer cells.

Indexed as

BioengineeringExtracellular vesiclesTargeting antibodiesUltrasound shock wavesZinc oxide nanocrystals

Identifiers

PMID35568919
PMCPMC9107671

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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.