Novel Noncompetitive IL-1 Receptor–Biased Ligand Prevents Infection- and Inflammation-Induced Preterm Birth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preterm birth (PTB) is firmly linked to inflammation regardless of the presence of infection. Proinflammatory cytokines, including IL-1β, are produced in gestational tissues and can locally upregulate uterine activation proteins. Premature activation of the uterus by inflammation may lead to PTB, and IL-1 has been identified as a key inducer of this condition. However, all currently available IL-1 inhibitors are large molecules that exhibit competitive antagonism properties by inhibiting all IL-1R signaling, including transcription factor NF-κB, which conveys important physiological roles. We hereby demonstrate the efficacy of a small noncompetitive (all-d peptide) IL-1R-biased ligand, termed rytvela (labeled 101.10) in delaying IL-1β-, TLR2-, and TLR4-induced PTB in mice. The 101.10 acts without significant inhibition of NF-κB, and instead selectively inhibits IL-1R downstream stress-associated protein kinases/transcription factor c-jun and Rho GTPase/Rho-associated coiled-coil-containing protein kinase signaling pathways. The 101.10 is effective at decreasing proinflammatory and/or prolabor genes in myometrium tissue and circulating leukocytes in all PTB models independently of NF-κB, undermining NF-κB role in preterm labor. In this work, biased signaling modulation of IL-1R by 101.10 uncovers a novel strategy to prevent PTB without inhibiting NF-κB.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it