Trial Watch
Bibliographic record
Abstract
Toll-like receptors (TLRs) are prototypic pattern recognition receptors (PRRs) best known for their ability to activate the innate immune system in response to conserved microbial components such as lipopolysaccharide and double-stranded RNA. Accumulating evidence indicates that the function of TLRs is not restricted to the elicitation of innate immune responses against invading pathogens. TLRs have indeed been shown to participate in tissue repair and injury-induced regeneration as well as in adaptive immune responses against cancer. In particular, TLR4 signaling appears to be required for the efficient processing and cross-presentation of cell-associated tumor antigens by dendritic cells, which de facto underlie optimal therapeutic responses to some anticancer drugs. Thus, TLRs constitute prominent therapeutic targets for the activation/intensification of anticancer immune responses. In line with this notion, long-used preparations such as the Coley toxin (a mixture of killed Streptococcus pyogenes and Serratia marcescens bacteria) and the bacillus Calmette-Guérin (BCG, an attenuated strain of Mycobacterium bovis originally developed as a vaccine against tuberculosis), both of which have been associated with consistent anticancer responses, potently activate TLR2 and TLR4 signaling. Today, besides BCG, only one TLR agonist is FDA-approved for therapeutic use in cancer patients: imiquimod. In this Trial Watch, we will briefly present the role of TLRs in innate and cognate immunity and discuss the progress of clinical studies evaluating the safety and efficacy of experimental TLR agonists as immunostimulatory agents for oncological indications.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.300 | 0.090 |
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".