Atomic Force Microscopy and Scanning Electron Microscopy for Characterization of Interface Surface Roughness After ELITA Femtosecond Laser Treatments
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".