Bibliographic record
Abstract
免疫反応には自然免疫と獲得免疫がある。この違いは抗原が必要かどうかであって，獲得免疫ではダニやスギといった抗原が必要であり，この抗原特異的な免疫反応が引き起こされる1)。獲得免疫に関与する細胞の例としては，T helper 1 cell(Th1細胞)やTh2細胞がある。これに対して，自然免疫とは，抗原が必要ない，非特異的な免疫反応である。自然免疫の代表的な白血球の例は，２型自然リンパ球(group 2innate lymphoid cells：ILC2)2)である。ILC2は，獲得免疫でいうところのTh2細胞に類似したサイトカインを産生し，アトピー性皮膚炎(atopic dermatitis：AD)の病態に関与する2010年に発見された細胞である。ILC2の活性化に抗原は不要で，抗原の代わりにインターロイキン(interleukin：IL)-4 3)4)やIL-33 5)などのサイトカインの刺激で増殖・活性化する。ILC2はADの病態に深く関与しており，たとえばアトピー性皮膚炎診療ガイドライン2018 6)にもその病態が記載されるに至っている。本稿では，IL-4やIL-33で活性化されるILC2がADにどのように関与しているか概説する。
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".