Evidence map›Paper›PMID 39373773›Full record

ArticleEuropean biophysics journal : EBJ2024

Application of artificial neural network for the mechano-bactericidal effect of bioinspired nanopatterned surfaces.

Ecren Uzun Yaylacı

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Article in European biophysics journal : EBJ, 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

1 author.

Ecren Uzun YaylacıFaculty of Engineering and Architecture, Recep Tayyip Erdogan University, 53100, Rize, Turkey. ecren.uzunyaylaci@erdogan.edu.tr.ORCID http://orcid.org/0000-0002-2558-2487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to calculate the effect of nanopatterns' peak sharpness, width, and spacing parameters on P. aeruginosa and S. aureus cell walls by artificial neural network and finite element analysis. Elastic and creep deformation models of bacteria were developed in silico. Maximum deformation, maximum stress, and maximum strain values of the cell walls were calculated. According to the results, while the spacing of the nanopatterns is constant, it was determined that when their peaks were sharpened and their width decreased, maximum deformation, maximum stress, and maximum strain affecting the cell walls of both bacteria increased. When sharpness and width of the nano-patterns are kept constant and the spacing is increased, maximum deformation, maximum stress, and maximum strain in P. aeruginosa cell walls increase, but a decrease in S. aureus was observed. This study proves that changes in the geometric structures of nanopatterned surfaces can show different effects on different bacteria.

Indexed as

Neural Networks, ComputerPseudomonas aeruginosaStaphylococcus aureusSurface PropertiesAnti-Bacterial AgentsBiomechanical PhenomenaFinite Element AnalysisMechanical PhenomenaNanostructuresStress, MechanicalAnti-Bacterial AgentsArtificial neural networkMechano-bactericidalNano-patterned surface

Identifiers

What OpenQuestion holds

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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.