Circulating cathelicidin LL-37 in adult patients with pulmonary infectious diseases
Bibliographic record
Abstract
PURPOSE: The antimicrobial peptide cathelicidin LL-37 plays a role in the immune response in the course of lung infections; however, the exact role of LL-37 in defense mechanisms against bacteria within the respiratory tract is has not been precisely described. The aim of our study was to evaluate LL-37 concentrations in the serum of pulmonary tuberculosis (TB) patients, patients with pneumonia caused by Gram-positive and Gram-negative bacteria and to compare them with those of healthy subjects. METHODS: Thirty TB patients, 30 patients with pneumonia caused by Gram-positive bacteria, 30 patients with pneumonia caused by Gram-negative bacteria, and 30 healthy control subjects were enrolled in the study. Serum LL-37 concentration was measured using an enzyme-linked immunosorbent assay (ELISA). RESULTS: The mean (± SEM) LL-37 concentration in patients with TB (13.94±5.13 ng/mL) was significantly higher than that in patients with Gram-positive bacteria-induced pneumonia (7.87±4.58 ng/mL, P=0.00077), in patients with Gram-negative bacteria-induced pneumonia (10.27±3.60 ng/mL, P=0.00730), and in control healthy subjects (1.75±0.71 ng/mL, P=0.00004). CONCLUSION: Our data suggest that cathelicidin LL-37 is an important element of host defense in the course of bacterial diseases within the respiratory tract, particularly when the infection is caused by an intracellular pathogen.
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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 | 0.000 | 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) | 0.001 | 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".