Proteomic Analyses of Lung Lysates from Short-Term Exposure of Fischer 344 Rats to Cigarette Smoke
Bibliographic record
Abstract
A short-term 5 day mainstream cigarette smoke exposure study was conducted in Fischer 344 rats to identify changes in lung proteins. Groups of 10 male and female rats at 5 weeks of age were assigned to one of four exposure groups. Animals received either nose-only filtered air (Air Control) or 75, 200, or 400 mg total particulate matter (TPM)/m(3) of diluted cigarette smoke. Exposures were conducted for 3 h per day, for 5 consecutive days. One lung per animal was frozen in liquid nitrogen and processed for proteomic analyses. Lung lysates from control verses treated animals were screened with 650 antibodies for changes in signaling protein levels and phosphorylation using antibody microarray technology, and then over 100 of the top protein hits were assessed by immunoblotting. The top smoke-altered proteins were further evaluated using reverse lysate microarrays. Major protein changes showed medium to strong bands on Western blots, depended on dose and gender, and included protein-serine kinases (Cot/Tpl2, ERK1/2, GSK3α/β, MEK6, PKCα/γ, RSK1), protein phosphatases (PP4/A'2, PP1Cβ), and other proteins (caspase 5, CRMP2, Hsc70, Hsp60, Rac1 and STAT2). The most pronounced changes occurred with 75 mg TPM/m(3) exposed females and 200 mg TPM/m(3) exposed males. Smoke-altered proteins regulate apoptosis, stress response, cell structure, and inflammation. Changes in identified proteins may serve as early indicators of lung damage.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".