Interaction of primary human trabecular meshwork cells with metal alloy candidates for microinvasive glaucoma surgery
Bibliographic record
Abstract
BACKGROUND: Microinvasive glaucoma surgery (MIGS) is a relatively new addition to the glaucoma treatment paradigm. Small metallic stents are inserted into the trabecular meshwork in order to increase aqueous humour drainage. MIGS procedures are rapidly being adopted owing to a more favourable side effect profile when compared with traditional surgery. Remarkably, this rapid rate of utilization has occurred without any published studies on the effect of metal alloys used in these stents on human trabecular meshwork cells (HTMCs). Therefore, this study aimed to determine the effect of candidate metal alloys for MIGS on HTMC morphology, viability and function. METHODS: Human trabecular meshwork cells were cultured on the surfaces of titanium (polished and sandblasted), a titanium-nickel (nitinol) alloy and glass (as control substratum). Fluorescence imaging was used to assess cell morphology and spreading. A lactate dehydrogenase cytotoxicity assay, cell death detection ELISA, MTT cell viability assay, BrdU cell proliferation assay and fibronectin ELISA were also conducted. RESULTS: Cells cultured on sandblasted titanium exhibited significantly greater spreading than cells cultured on other substrata. In comparison, HTMCs cultured on nitinol displayed poor spreading. Significantly more cell death, by both necrosis and apoptosis, occurred on nitinol than on titanium and glass. Also, cell viability and proliferation were suppressed on nitinol compared with titanium or glass. Finally, HTMCs on both titanium and nitinol produced greater amounts of fibronectin than cells grown on glass. CONCLUSIONS: Substratum topography and metal alloy composition were found to impact morphology, viability and function of primary HTMC cultures.
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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 | 0.000 | 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) | 0.002 | 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".