Evidence map›Paper›PMID 41129167›Full record

ArticleTranslational vision science & technology2025

Atomic Force Microscopy and Scanning Electron Microscopy for Characterization of Interface Surface Roughness After ELITA Femtosecond Laser Treatments.

Hajime Minoguchi, Miguel A Teus, Hong Fu

Abstract read
In one paragraph

Article in Translational vision science & technology, 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

3 authors.

Hajime MinoguchiJohnson and Johnson Surgical Vision, Inc., Milpitas, CA, USA.
Miguel A TeusUniversidad de Alcalá, Madrid, Spain.
Hong FuJohnson and Johnson Surgical Vision, Inc., Milpitas, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To characterize and compare the corneal interface surface roughness of the ELITA femtosecond laser flap and smooth incision lenticular keratomileusis (SILK) to the iFS femtosecond laser flap with atomic force microscopy (AFM) and scanning electron microscopy (SEM). Methods: The iFS flap with 700 nJ pulse energy, ELITA flap with 50 nJ, and ELITA SILK with 50 nJ were performed on a total of 12 ex vivo porcine eyes. After laser treatment and mechanical separation, the posterior surface of the laser-treated interface was trephined, fixated, and dehydrated. The corneal interface surface roughness was assessed with AFM in contact mode. For AFM, 15 eye locations (three eyes, 5 locations each) for each treatment were evaluated with a 20 × 20-µm scanning area and 512 × 512-pixel resolution. The root mean square (RMS) roughness for each treatment method was measured. With SEM, representative images were taken with 100× and 250× magnification. Results: The RMS roughness of the iFS flap, ELITA flap, and ELITA SILK was 236 ± 64 nm, 114 ± 33 nm, and 203 ± 84 nm, respectively. ELITA flap interface surface roughness was significantly less than that of the iFS flap (P < 0.001) and ELITA SILK (P < 0.001). Conclusions: The ELITA flap produced a smoother corneal interface surface compared to the iFS flap and ELITA SILK, while the iFS flap and ELITA SILK produced similar corneal interface surface roughness. Translational Relevance: This study suggests that the ELITA femtosecond laser's ability to create smoother corneal interfaces may enhance visual acuity recovery time.

Indexed as

CorneaCorneal StromaKeratomileusis, Laser In SituLasers, ExcimerMicroscopy, Atomic ForceMicroscopy, Electron, ScanningSurgical FlapsAnimalsSurface PropertiesSwine

Identifiers

PMID41129167
PMCPMC12553472

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LicenceCC BY-NC-ND
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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.