Innate Immunity and Organ Transplantation: The Potential Role of Toll-like Receptors
Bibliographic record
Abstract
Traditionally, the recognition and tolerance of transplanted grafts has been considered to be within the realm of the adaptive immune system. Innate immunity, on the other hand, as the first line of host defense, plays a role in fighting against invading microorganisms. Recently, with the discovery of the Toll-like receptors (TLRs), the role of innate immune responses in the control of adaptive immunity has become a new area of interest. Emerging evidence suggests that in addition to responding to pathogen-associated molecular patterns of microorganisms, TLRs can be activated by endogenous ligands, expressed by mammalian cells. These 'danger signals' may participate in ischemia-reperfusion related organ damage and subsequently influence function and survival of transplanted grafts. Furthermore, it has been suggested that adaptive immune responses can enhance the acute inflammatory responses controlled by innate immunity in organ transplantation. This review addresses the potential involvement of TLRs in different stages of organ transplantation. Intriguing and controversial findings are presented and discussed in order to stimulate more attention to this emerging and potentially important area of research in organ transplantation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".