NATURAL KILLER CELL KILLING OF EXTRACELLULAR PSEUDOMONAS AERUGINOSA
Bibliographic record
Abstract
Abstract Pseudomonas aeruginosa is an opportunistic pathogen that commonly infects individuals with the genetic illness, Cystic Fibrosis and contributes to airway blockage and loss of lung function. NK cells are cytotoxic, granular lymphocytes that are part of the innate immune system. NK cell secretory granules contain the cytolytic proteins granulysin, perforin and granzymes. NK cells, in addition to their cytotoxic effects on cancer and virally infected cells have been shown to play a role in an innate defense against microbes. Hypothesis NK cells will kill P. aeruginosa using cytolytic effector protein(perforin, granulysin or granzymes) alone or working synergistically. Results Live-cell imaging of a co-culture of YT cells, a human NK cell line, incubated with GFP P. aeruginosa in the presence of the viability dye, propidium iodide, demonstrated that YT cell killing of P. aeruginosa is contact-mediated. CRISPR knockout of granulysin or perforin in YT cells had no significant affect on NK cell killing of P. aeruginosa, as determined by CFU counts. Pre-treatment of YT and NK cells with the serine protease inhibitor 3,4-Dichloroisocoumarin (DCI) to inhibit granzymes, resulted in an inhibition of killing. CRISPR knockout of granzyme B in YT cells, in addition to treatment with the granzyme A inhibitor Futhan, significantly inhibited killing of P. aeruginosa, as determined by CFU counts. These results suggest that NK cells induce membrane damage in P. aeruginosa through a contact dependent process, and that killing requires granzymes.
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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) | 0.000 | 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 | 0.001 |
| 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".