LSC Abstract – Toll-like receptor 10: Expression and functional role in LPS mediated inflammation
Bibliographic record
Abstract
Toll-like receptor-10 (TLR10) is a member of innate immune receptors that recognize pathogen-associated molecules and play critical roles in host defense. However, there is very little known about TLR10. We examined TLR10 expression in normal and inflamed lungs from chickens and humans. Immunohistochemistry showed TLR10 in vascular endothelium in human and chicken lungs. Immunohistochemistry and Western blots showed an increase in TLR10 protein in lungs of chicken infected with E. coli or Fowl Adenovirus. Human neutrophils challenged with E. coli lipopolysaccharide (LPS) showed decreased total TLR10 protein and surface expression in 90 minutes. Confocal microscopy showed cytosolic and nuclear distribution of TLR10 in normal neutrophils. In the LPS-activated neutrophils, TLR10 colocalized with flotallin-1, a lipid raft marker, and EEA-1, an early endosomal marker, to suggest the cycling to endocytic compartments. Because LPS signals via TLR4, we examined and found that TLR10 colocalization with TLR4 increased up to 60 minutes followed by a decrease. TLR4 neutralization reduced cytoplasmic localization of TLR10. To determine the role of reactive oxygen species in TLR4-mediated regulation of TLR10 expression, we depleted ROS using FCCP in LPS-treated neutrophils, which led to decreased TLR10 expression and p65 nuclear translocation. Finally, we explored the role of TLR10 in neutrophil chemotaxis. Single cell imaging of live LPS-activated human neutrophils showed the translocation of TLR10 to the leading edge. siRNA-mediated silencing of TLR10 in HL-60 cell line reduced their chemotaxis towards fMLF.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".