TILRR Promotes Migration of Immune Cells Through Induction of Soluble Inflammatory Mediators
Bibliographic record
Abstract
TILRR has been identified as an important modulator of inflammatory responses. It is associated with NF-κB activation, and inflammation. Our previous study showed that TILRR significantly increased the expression of many innate immune responsive genes and increased the production of several pro-inflammatory cytokines/chemokines by cervical epithelial cells. In this study, we evaluated the effect of TILRR-induced pro-inflammatory cytokines/chemokines on the migration of immune cells. The effect of culture supernatants of TILRR-overexpressed cervical epithelial cells on the migration of THP-1 monocytes and MOLT-4 T-lymphocytes was evaluated using Transwell assay and a novel microfluidic device. We showed that the culture supernatants of TILRR-overexpressed HeLa cells attracted significantly more THP-1 cells (11-40%, p= 0.0004 to 0.0373) and MOLT-4 cells (14-17%, p= 0.0010 to 0.0225) than that of controls. The microfluidic device-recorded image analysis showed that significantly higher amount with longer mean cell migration distance of THP-1 (p< 0.0001 to 0.0180) and MOLT-4 (p< 0.0001 to 0.0025) cells was observed towards the supernatant of TILRR-overexpressed cervical epithelial cells compared to that of the controls. Thus, the cytokines/chemokines secreted by the TILRR-overexpressed cervical epithelial cells attracted immune cells, such as monocytes and T cells, and may potentially influence immune cell infiltration in tissues.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".