A Light Emitting Mouse to Image Skin Inflammation
Bibliographic record
Abstract
BACKGROUND: The mouse ear swelling test is a well-accepted method for quantitating the inflammatory response to contact irritants and sensitizing agents. However, this assay measures edema rather than the cellular component of skin inflammation. OBJECTIVE: To develop a quantitative and noninvasive assay of inflammatory cell infiltration in contact dermatitis. METHODS: We bred a transgenic bioluminescent mouse that emits light proportional to cutaneous infiltration of inflammatory cells. We characterized this model by correlating luminescence with edema and histologic analysis of affected skin. A mouse strain expressing cyclization recombinase enzyme (cre) recombinase exclusively in myeloid cells was crossed with a reporter strain containing an inactivated form of the luciferase gene. In progeny mice, cre-mediated recombination repaired the luciferase gene, causing light emission from myeloid cells. Light emission and swelling from the inflamed ear was quantitated and compared to the contralateral ear. RESULTS: Light intensity correlated with the inflammatory cell infiltration in the dermis. In sensitized mice challenged with squaric acid, luminescence increased about 2.2-fold while swelling increased about 1.5-fold. CONCLUSION: Our model may serve as a useful screening assay for topical antiinflammatory molecules. Moreover, this approach allows real-time imaging of skin infiltration by specific inflammatory cell lineages in living animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".