The inflammatory function of human IgA
Bibliographic record
Abstract
The prevailing concept regarding the immunological function of immunoglobulin A (IgA) is that it binds to and neutralizes pathogens to prevent infection at mucosal sites of the body. However, recently, it has become clear that in humans IgA is also able to actively contribute to the initiation of inflammation, both at mucosal and non-mucosal sites. This additional function of IgA is initiated by the formation of immune complexes, which trigger Fc alpha Receptor I (FcαRI) to synergize with various other receptors to amplify inflammatory responses. Recent findings have demonstrated that co-stimulation of FcαRI strongly affects pro-inflammatory cytokine production by various myeloid cells, including different dendritic cell subsets, macrophages, monocytes, and Kupffer cells. FcαRI-induced inflammation plays a crucial role in orchestrating human host defense against pathogens, as well as the generation of tissue-specific immunity. In addition, FcαRI-induced inflammation is suggested to be involved in the pathogenesis of various chronic inflammatory disorders, including inflammatory bowel disease, celiac disease, and rheumatoid arthritis. Combined, IgA-induced inflammation may be used to either promote inflammatory responses, e.g. in the context of cancer therapy, but may also provide new therapeutic targets to counteract chronic inflammation in the context of various chronic inflammatory disorders.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".