Bladder p75NTR-Mediated Anti-Inflammatory Response via the TLR4/TRAF6/NF-κB Axis
Bibliographic record
Abstract
Recurrent bacterial cystitis in women can lead to interstitial cystitis or bladder pain syndrome (IC/BPS). Activation of Toll-like receptor 4 (TLR4) by LPS can upregulate signaling of the pro-inflammatory receptor p75NTR. The aim of the presented study was to assess whether p75NTR antagonist THX-B can modulate LPS-mediated inflammation in bladder cells. In vitro expression and LPS-activation of p75NTR were confirmed in urothelial (URO) and smooth muscle (SMC) cells. In UROs, p75NTR antagonism abolished the LPS-elicited rise in membrane-bound and soluble TNF-α. However, it could not prevent LPS-induced rise in phosphorylated ERK nor decrease in phosphorylated p38MAPK, nor the increase in iNOS and nitric oxide (NO) content. On the other hand, in SMCs, LPS increased phosphorylation of JNK, nuclear translocation of NF-κB, and association of TRAF6 to p75NTR, outcomes prevented by p75NTR antagonism. In UROs, LPS decreased the expression of tight junction proteins, ZO-1 and occludin, with the latter rescued by p75NTR antagonism. Intraurethral instillation of LPS increased inflammation in the lamina propria, activation of JNK, and contractile activity of bladder tissue. Alternatively, intraperitoneal THX-B injections prevented LPS-induced inflammation but not enhanced muscle contraction. Our results suggest that inhibition of p75NTR could help in reducing bladder symptoms during cystitis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".