Phosphatases in toll-like receptors signaling: the unfairly-forgotten
Bibliographic record
Abstract
Over the past 2 decades, pattern recognition receptors (PRRs) have been shown to be on the front line of many illnesses such as autoimmune, inflammatory, and neurodegenerative diseases as well as allergies and cancer. Among PRRs, toll-like receptors (TLRs) are the most studied family. Dissecting TLRs signaling turned out to be advantageous to elaborate efficient treatments to cure autoimmune and chronic inflammatory disorders. However, a broad understanding of TLR effectors is required to propose a better range of cures. In addition to kinases and E3 ubiquitin ligases, phosphatases emerge as important regulators of TLRs signaling mediated by NF-κB, type I interferons (IFN I) and Mitogen-Activated Protein Kinases signaling pathways. Here, we review recent knowledge on TLRs signaling modulation by different classes and subclasses of phosphatases. Thus, it becomes more and more evident that phosphatases could represent novel therapeutic targets to control pathogenic TLRs signaling. Video Abstract.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".