Evidence map›Paper›PMID 40234455›Full record

ArticleScientific reports2025

Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications.

Reyhaneh Fatahian, Rasool Erfani

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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

2 authors.

Reyhaneh FatahianDepartment of Textile Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.
Rasool ErfaniDepartment of Engineering, Manchester Metropolitan University, Manchester, M1 5GD, UK. r.erfani@mmu.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blending poly (lactic acid) (PLA) with poly (vinyl alcohol) (PVA) improves the strength and hydrophilicity of nanofibers, making them suitable for biomedical applications like wound dressings. This study explores how electrospinning parameters-applied voltage, flow rate, and needle-to-collector distance-affect PVA/PLA nanofiber properties, optimizing them using a Taguchi design of experiment (DoE) approach to enhance their mechanical and surface properties for clinical use. Given the high costs and time associated with conducting extensive experimental tests, an artificial neural network based surrogate model is developed to predict experimental outcomes more efficiently, facilitating faster identification of optimal design configurations. Analysis of Variance reveals flow rate as the most significant determinant of fiber diameter. The optimal electrospinning configuration yields nanofibers with an average diameter of 127.6 ± 19.8 nm. These fibers exhibit exceptional tensile strength, flexibility, and a water contact angle of 37°, demonstrating superior hydrophilicity conducive to cell adhesion and proliferation-key factors in promoting wound healing. Comparative analyses confirm that the optimized scaffold (18 cm needle-to-collector distance, 0.6 ml/h flow rate, and 18 kV applied voltage) significantly outperforms alternative configurations, such as 10 cm needle-to-collector distance, 1.2 ml/h flow rate, and 22 kV applied voltage, which display larger diameters, reduced hydrophilicity (contact angle of 72°), and diminished suitability for medical use. Validation experiments affirm the accuracy and reproducibility of the Taguchi optimization, substantiating the methodological rigor and reliability of the findings. This work contributes novel insights into the tunable design of electrospun nanofibers, providing a pathway to developing advanced wound dressings that facilitate tissue integration and accelerate healing. The optimized PVA/PLA nanofibers have the potential to revolutionize wound care by offering a cost-effective and clinically viable solution for enhancing patient recovery, reducing treatment durations, and improving global healthcare outcomes.

Indexed as

NanofibersNeural Networks, ComputerPolyestersPolyvinyl AlcoholBiocompatible MaterialsHumansHydrophobic and Hydrophilic InteractionsTensile StrengthTissue EngineeringTissue ScaffoldsWound HealingBiocompatible MaterialsPolyesterspoly(lactide)Polyvinyl AlcoholArtificial neural network (ANN)HealthcareHydrophilicitySurrogate modelingWound healing

Identifiers

PMID40234455
PMCPMC12000562

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.