<i>In vitro</i>methods of assessing ocular biocompatibility using THP-1-derived macrophages
Bibliographic record
Abstract
Macrophages play an important role in the elimination of infections, the removal of debris and in tissue repair after infection and trauma. In vitro models that assess ocular biomaterials for toxicity typically focus on the effects of these materials on epithelial or fibroblast cells. This investigation evaluated known ocular toxins deposited on model materials for their effects on the viability and activation of macrophages. THP-1-derived macrophages were cultured onto silicone films (used as a base biomaterial) deposited with chemical toxins (benzalkonium chloride (BAK), zinc diethyldithiocarbamate (ZDEC) and lipopolysaccharide (LPS)). Utilizing three fluorescent dyes calcein, ethidium homodimer-1 (EthD-1) and annexin V, the viability of macrophages attached to the biomaterial was determined using confocal microscopy. Propidium iodide (PI) staining and alamarBlue® (resazurin) reduction were used to assess cell death and metabolic activity. CD14, CD16, CD33, CD45, and CD54 expression of adherent macrophages, were also evaluated to detect LPS activation of macrophages using flow cytometry. The sensitivity of this test battery was demonstrated as significant toxicity from treated surfaces with ZDEC (0.001-0.01%), and BAK (0.001%-0.1%) was detected. Also, macrophage activation could be detected by measuring CD54 expression after exposure to adsorbed LPS. These in vitro methods will be helpful in determining the toxicity potential of new ocular biomaterials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".