Targeting Toll‐like receptor‐4 to tackle preterm birth and fetal inflammatory injury
Bibliographic record
Abstract
Abstract Every year, 15 million pregnancies end prematurely, resulting in more than 1 million infant deaths and long‐term health consequences for many children. The physiological processes of labour and birth involve essential roles for immune cells and pro‐inflammatory cytokines in gestational tissues. There is compelling evidence that the mechanisms underlying spontaneous preterm birth are initiated when a premature and excessive inflammatory response is triggered by infection or other causes. Exposure to pro‐inflammatory mediators is emerging as a major factor in the ‘fetal inflammatory response syndrome’ that often accompanies preterm birth, where unscheduled effects in fetal tissues interfere with normal development and predispose to neonatal morbidity. Toll‐like receptors (TLRs) are critical upstream gatekeepers of inflammatory activation. TLR4 is prominently involved through its ability to sense and integrate signals from a range of microbial and endogenous triggers to provoke and perpetuate inflammation. Preclinical studies have identified TLR4 as an attractive pharmacological target to promote uterine quiescence and protect the fetus from inflammatory injury. Novel small‐molecule inhibitors of TLR4 signalling, specifically the non‐opioid receptor antagonists (+)‐naloxone and (+)‐naltrexone, are proving highly effective in animal models for preventing preterm birth induced by bacterial mimetic LPS, heat‐killed Escherichia coli , or the TLR4‐dependent pro‐inflammatory lipid, platelet‐activating factor (PAF). Here, we summarise the rationale for targeting TLR4 as a master regulator of inflammation in fetal and gestational tissues, and the potential utility of TLR4 antagonists as candidates for preventative and therapeutic application in preterm delivery and fetal inflammatory injury.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".