Temporal Change in Pro-Inflammatory Cytokine Expression from Immortalized Human Corneal Epithelial Cells Exposed to Hyperosmotic Stress
Bibliographic record
Abstract
Purpose To determine the metabolic activity, and cytokine expression over time from immortalized human corneal epithelial cells (HCECs) exposed to hyperosmotic stress.Methods HCECs were cultured and expanded in DMEM/F-12 with 10% FBS. The cells were exposed to either normal media (295 mmol/kg) or hyperosmolar media (500 mmol/kg) for 0.25, 3, 6, and 12 hours. After each exposure duration, metabolic activity was quantified using alamarBlue, and a panel of pro-inflammatory cytokines (IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-α, IFN-γ, and IL-17A) was quantified using multiplexed electrochemiluminescence (Meso Scale Diagnostics, Rockville, MD).Results Metabolic activity of the HCEC exposed to hyperosmolar conditions was significantly reduced at the 3-, 6-, and 12-hour mark compared to the control (all p < 0.01). There was no significant difference in cytokine expression between the hyperosmolar media and control at the 0.25- and 3-hour mark for all cytokines (all p ≥ 0.28). The difference in cytokine expression between the hyperosmolar media and the control was significant for IL-1β, IL-4, IL-6, IL-8, IL-12p70, IL-13, and TNF-α at the 6-hour mark (all p ≤ 0.02). No significant change in cytokine expression between the hyperosmolar media and control was noted for IL-2, IL-10, IL-17A, and IFN-γ (all p ≥ 0.74) at the 6-hour mark.Conclusion Hyperosmolar stress reduced cell metabolic activity and increased expression of IL-1β, IL-4, IL6, IL8, IL-12p70, IL-13, and TNF-α over a 6-hour period in an immortalized HCEC line.
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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 | 0.001 |
| 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".