Toll Like Receptors 4 and 2 Expression in the Bronchial Mucosa of Patients with Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) is a lung disease characterized by chronic infection with Gram-negative bacteria Pseudomonas aeruginosa and Gram-positive bacteria Staphylococcus aureus. Recently, toll-like receptor (TLR) 4 has been shown to be responsible for the lipopolysaccharide (LPS)-mediated immune response. While TLR2 mediates responses driven by bacterial lipoproteins and peptidoglycans from Gram-positive bacteria, LPS derived from P aeruginosa may stimulate the immune response in the airways of patients with CF via activation of TLR4. OBJECTIVES: To investigate TLR4 and TLR2 expression in the bronchial mucosa of patients with CF and normal control subjects. PATIENTS AND METHODS: Endoscopic bronchial biopsies from seven patients with CF and six healthy control subjects were obtained. TLR4 and TLR2 expression was assessed using immmunocytochemistry. Real-time polymerase chain reaction was used to detect TLR4 messenger RNA in blood cells from patients with CF and to compare TLR4 expression in CF bronchial epithelial cells with non-CF bronchial epithelial cells. RESULTS: In patients with CF, the number of TLR4-positive cells was significantly increased in their submucosa (P<0.05) but significantly reduced in their epithelium compared with control subjects (P<0.05). The majority of TLR4-positive cells were neutrophils. Patients with CF (n=4) and control subjects (n=4) had a similar percentage of TLR4-expressing neutrophils and monocytes/lymphocytes in peripheral blood. CF cells (IB-3) had significantly decreased basal TLR4 messenger RNA expression compared with non-CF cells (Calu-3) (P<0.05). Although there was a trend toward reduced TLR2 expression in the airway epithelium of patients with CF (P=0.07), there was no significant difference in TLR2 expression in the submucosa of patients with CF compared with that of control subjects. CONCLUSIONS: Both TLR4 and TLR2 expression in the bronchial epithelium of patients with CF were significantly reduced compared with healthy control subjects. In contrast, the number of TLR4-positive neutrophils in the submucosa of patients with CF was higher than in control subjects. This may suggest that the loss of epithelial TLR expression may contribute to the impaired defense against LPS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".