Regulation of contact sensitivity in non‐obese diabetic (NOD) mice by innate immunity
Bibliographic record
Abstract
Background Genetic background influences allergic immune responses to environmental stimuli. Non‐obese diabetic (NOD) mice are highly susceptible to environmental stimuli. Little is known about the interaction of autoimmune genetic factors with innate immunity in allergies, especially skin hypersensitivity. Objectives To study the interplay of innate immunity and autoimmune genetic factors in contact hypersensitivity (CHS) by using various innate immunity‐deficient NOD mice. Methods Toll‐like receptor (TLR) 2‐deficient, TLR9‐deficient and MyD88‐deficient NOD mice were used to investigate CHS. The cellular mechanism was determined by flow cytometry in vitro and adoptive cell transfer in vivo. To investigate the role of MyD88 in dendritic cells (DCs) in CHS, we also used CD11cMyD88+ MyD88−/− NOD mice, in which MyD88 is expressed only in CD11c+ cells. Results We found that innate immunity negatively regulates CHS, as innate immunity‐deficient NOD mice developed exacerbated CHS accompanied by increased numbers of skin‐migrating CD11c+ DCs expressing higher levels of major histocompatibility complex II and CD80. Moreover, MyD88−/− NOD mice had increased numbers of CD11c+ CD207− CD103+ DCs and activated T effector cells in the skin‐draining lymph nodes. Strikingly, re‐expression of MyD88 in CD11c+ DCs (CD11cMyD88+ MyD88−/− NOD mice) restored hyper‐CHS to a normal level in MyD88−/− NOD mice. Conclusion Our results suggest that the autoimmune‐prone NOD genetic background aggravates CHS regulated by innate immunity, through DCs and T effector cells.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".