Altering immune tolerance therapeutically: the power of negative thinking
Bibliographic record
Abstract
The etiology of most human autoimmune diseases remains largely unknown. However, investigators have identified several negative regulatory mechanisms acting at the level of innate and/or adaptive immunity. Mutations resulting in a deficiency of some key regulatory molecules are associated with systemic or organ-specific inflammatory disorders, which often have a prominent autoimmune component. Genetic studies have implicated the negative regulator cytotoxic T-lymphocyte antigen 4 (CTLA-4) and other regulatory molecules in human autoimmune diseases. In addition to CTLA-4, key inhibitory molecules include programmed death 1 and B and T lymphocyte attenuator. Transforming growth factor beta1 and interleukin-10 also play major anti-inflammatory and regulatory roles. Tumor cells and infectious agents use negative regulatory pathways to escape immunity. The therapeutic blockage of negative signaling (particularly of CTLA-4) increases immunity against tumor antigens but also induces or aggravates autoimmune diseases. It appears that under normal conditions, the immune system is under strong "negative influences" that prevent autoimmunity and that release of this suppression results in disease. Regulation involves communication between the immune system and nonlymphoid tissues, and the latter can deliver inhibitory or stimulatory signals. Recent studies reveal that the generation of negative signals by selective engagement of inhibitory molecules is feasible and is likely to be of therapeutic benefit in autoimmune diseases and allograft rejection.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".