Evidence map›Paper›PMID 40181081›Full record

ArticleScientific reports2025

Preparation and preliminary studies of porous fish collagen and chitosan materials enriched with microcapsules containing an active ingredient.

Justyna Kozlowska, Agnieszka Ciesielska

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Justyna KozlowskaFaculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, Toruń, 87-100, Poland. justynak@umk.pl.
Agnieszka CiesielskaFaculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, Toruń, 87-100, Poland.

Funding

Narodowe Centrum Nauki UMO-2016/21/D/ST8/01705
6 · The paper itself

Abstract

This study explores the development and characterization of advanced composite materials combining fish collagen and chitosan, enhanced with chitosan-based microcapsules encapsulating an active ingredient-Calendula officinalis flower extract-hrough ionic gelation using various surfactants (Span 80, Tween 80 and Span80/Tween 80). Collagen was successfully extracted from northern pike scales and integrated with chitosan to create porous, three-dimensional matrices by the lyophilization process. Various amounts of microcapsules were incorporated into the matrices, and the structure of the obtained materials, their mechanical properties, swelling capacity, and susceptibility to degradation were assessed. Matrices with microcapsules exhibited high porosity, substantial swelling capacity, and improved mechanical properties compared to matrices without them. Microcapsules enabled the controlled release of active ingredients, demonstrating potential applications cosmetic industry. This research aligns with current trends in the cosmetics industry, such as the use of sustainable and eco-friendly materials derived from renewable resources like fish waste, the emphasis on natural and bioactive ingredients such as plant extracts, and the development of advanced delivery systems for controlled release of active compounds. The study addresses consumer demand for biodegradable and non-toxic materials, reducing environmental impact while enhancing product efficacy and safety.

Indexed as

ChitosanCollagenAnimalsCapsulesFishesPlant ExtractsPorosityCapsulesChitosanCollagenPlant ExtractsCalendula officinalis flower extractChitosanControlled releaseEnvironmental sustainabilityFish collagenMicrocapsules

Identifiers

PMID40181081
PMCPMC11968787

What OpenQuestion holds

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LicenceCC BY
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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.