Syk inhibitor R406 downregulates inflammation in an in vitro model of <i>Pseudomonas aeruginosa</i> infection
Bibliographic record
Abstract
As Pseudomonas aeruginosa infections are characterized by strong inflammation of infected tissues, anti-inflammatory therapies in combination with antibiotics have been considered for the treatment of associated diseases. Syk tyrosine kinase is an important regulator of inflammatory responses, and its specific inhibition was explored as a therapeutic option in several inflammatory conditions; however, this has not been studied in bacterial infections. We used a model of in vitro infection of human monocytic cell line THP-1 and lung epithelial cell line H292 with both wild-type and flagella-deficient mutant of P. aeruginosa strain K, as well as with clinical isolates from cystic fibrosis patients, to study the effect of a small molecule Syk inhibitor R406 on inflammatory responses induced by this pathogen. One-hour pretreatment of THP-1 cells with 10 μmol/L R406 resulted in a significant downregulation of the expression of the adhesion molecule ICAM-1, pro-inflammatory cytokines TNF-α and IL-1β, and phosphorylated signaling proteins ERK2, JNK, p-38, and IκBα, as well as significantly decreased TNF-α release by infected H292 cells. The results suggest that Syk is involved in the regulation of inflammatory responses to P. aeruginosa, and R406 may potentially be useful in dampening the damage caused by severe inflammation associated with this infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".