Expression and activation of toll-like receptor 3 and toll-like receptor 4 on human corneal epithelial and conjunctival fibroblasts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Toll-like receptors (TLRs) are recognized as important contributors to the initiation and modulation of the inflammatory response in the eye. This study investigated the precise expression patterns and functionality of TLRs in human corneal epithelial cells (HCE) and in conjunctival fibroblasts (HCF). METHODS: The cell surface expression of TLRs 2-4, TLR7 and TLR9 in HCE and HCF was examined by flow cytometry with or without stimulation with lipopolysaccharide (LPS) or polyinosinic:polycytidylic acid (poly I:C). The mRNA expression of the TLRs was determined by real-time PCR. The protein content levels of interleukin (IL)-6, IL-8, IL-1β and tumor necrosis factor-α (TNF-α) were measured in HCE and HCF using multiplex fluorescent bead immunoassay (FBI). RESULTS: The surface expression of TLR3 and TLR4 was detected on both HCE and HCF. Following incubation with LPS, the percentage of HCE cells staining for TLR4 decreased from 10.18% to 0.62% (P < 0.001). Incubation with poly I:C lowered the percentage of HCE cells positive for TLR3 from 10.44% to 2.84% (P < 0.001). The mRNA expression of TLRs2, 4, 7 and 9 was detected in HCE only. Activation of HCE with LPS complex elicited protein secretion up to 4.51 ± 0.85-fold higher levels of IL-6 (P < 0.05), 2.5 ± 0.36-fold IL-8 (P > 0.05), 4.35 ± 1.12-fold IL-1β (P > 0.05) and 29.35 ± 2.3-fold TNFα (P < 0.05) compared to cells incubated in medium. CONCLUSIONS: HCF and HCE both express TLRs that respond to specific ligands by increasing cytokine expression. Following activation, the surface expression of TLR3 and TLR4 on HCE is decreased, thus creating a negative feedback loop, mitigating the effect of TLR activation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it