Evidence map›Paper›PMID 42808097›Full record

ArticleBiomedical materials & devices (New York, N.Y.)2024

Tuning the pH-Responsive Properties of (2-Diisopropylamino) Ethyl Methacrylate-Based Cationic Nanoparticles: Toward Enhanced Nanoparticle Stability and Cytocompatibility.

Aaliyah Shodeinde, Olivia L Lanier, Shreya Nallaparaju, Michael Lam, Ani Savk, Nicholas A Peppas

Abstract read
In one paragraph

Article in Biomedical materials & devices (New York, N.Y.), 2024. 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

6 authors.

Aaliyah ShodeindeMcKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
Olivia L LanierInstitute for Biomaterials, Drug Delivery, and Regenerative Medicine, The University of Texas at Austin, Austin, TX, USA.
Shreya NallaparajuInstitute for Biomaterials, Drug Delivery, and Regenerative Medicine, The University of Texas at Austin, Austin, TX, USA.
Michael LamInstitute for Biomaterials, Drug Delivery, and Regenerative Medicine, The University of Texas at Austin, Austin, TX, USA.
Ani SavkMcKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
Nicholas A PeppasMcKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.

Funding

pH Sensitive Complex Hydrogels for Protein Drug ReleaseR01EB000246 · NIBIB · UNIVERSITY OF TEXAS AUSTIN · PI LOWMAN, ANTHONY M, PEPPAS, NICHOLAS A · 2002 to 2014
$3.8M
NIBIB NIH HHS R01 EB000246
6 · The paper itself

Abstract

Cationic nanoparticles have been widely investigated for their potent ability to transport therapeutic agents intracellularly. Previous studies in our lab have demonstrated that increasing network hydrophobicity in these polymers induces favorable pH-responsive phase transition and membrane-disruptive behavior for intracellular drug delivery. Building upon this idea, the work presented herein analyzes the physiochemical attributes and cargo delivery efficiency of a cationic nanoparticle system with the introduction with 2-(diisopropylamino)ethyl methacrylate (henceforth designated as DPAEMA), a comonomer heightening the hydrophobic content of the inner particle core. DPAEMA is a cationic, amine-containing monomer often introduced as an ionizable hydrophobic block to nanoparticles due to its high biocompatibility and ability to impart pH-sensitivity with a pK

Indexed as

BiomaterialsHydrogelsNanoparticlesProtein delivery

Identifiers

PMID42808097
PMCPMC13619317

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.