Repair Process Impairment by Pseudomonas aeruginosa in Epithelial Tissues: Major Features and Potential Therapeutic Avenues
Bibliographic record
Abstract
Epithelial tissues protecting organs from the environment are the first-line of defense against pathogens. Therefore, efficient repair mechanisms after injury are crucial to maintain epithelial integrity. However, these healing processes can be insufficient to restore epithelial integrity, notably in infectious conditions. Pseudomonas aeruginosa infections in cutaneous, corneal and respiratory tract epithelia are of particular concern because they are the leading causes of hospitalizations, disabilities and deaths worldwide. P. aeruginosa has been shown to alter repair processes, leading to chronic wounds and infections. Because of the current increase in the incidence of multi-drug resistant isolates of P. aeruginosa, complementary approaches to decrease the negative impact of these bacteria on epithelia are urgently needed. Here, we review the recent advances in the understanding of the impact of P. aeruginosa infections on the integrity and repair mechanisms of alveolar, airway, cutaneous and corneal epithelia. Potential therapeutic avenues aimed at counteracting this deleterious impact of infection are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".