Evidence map›Paper›PMID 41668444›Full record

ReviewCurrent topics in medicinal chemistry2025

Thermally-Induced Self-Agglomeration: Method and First Approaches to the Structural, Mechanical and Biological Characterization of Nanofiber Scaffolds.

Betul Topcu Ince, Maria Helena Vaz Fernandes, Samuel Guieu, Selin Seda Timur, Hakan Eroglu

Abstract readReview
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Review in Current topics in medicinal chemistry, 2025. 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

5 authors.

Betul Topcu InceHacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Technology, Ankara, Turkey.
Maria Helena Vaz FernandesUniversity of Aveiro, CICECO-Aveiro Institute of Materials, Department of Materials and Ceramic Engineering, Aveiro, Portugal.
Samuel GuieuUniversity of Aveiro, CICECO-Aveiro Institute of Materials, Department of Materials and Ceramic Engineering, Aveiro, Portugal.
Selin Seda TimurHacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Technology, Ankara, Turkey.
Hakan ErogluHacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Technology, Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The production of extracellular matrix (ECM) - like scaffolds for bone regeneration has been a topic of interest in the field of bone tissue engineering in recent years. Nanofiber structures stand out in terms of morphological similarity with the ECM structure. However, the nanofibrous membranes produced by electrospinning do not have sufficient thickness for clinical applications such as bone regeneration and cannot support cell growth sufficiently due to their structural properties. To mitigate this issue, three-dimensional (3D) nanofiber-based scaffolds made of shortnanofiber membranes are an emerging research topic in the field of bone tissue engineering, as they can present higher porosity and more appropriate mechanical properties. In this review, the details of the thermally-induced self-agglomeration (TISA) method for 3D nanofiber-based scaffold fabrication are discussed, together with its development for scaffold production, characterization, and biological applications. This review is expected to provide helpful guidance for future studies in designing 3D fiber scaffolds with the TISA method.

Indexed as

NanofibersTemperatureTissue EngineeringTissue ScaffoldsBone RegenerationHumansPorosity3D scaffoldbone tissue engineeringelectrospinningNanofiberthermally-induced self-agglomeration.

Identifiers

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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.