Quantification of regional murine ozone-induced lung inflammation using [18F]F-FDG microPET/CT imaging
Bibliographic record
Abstract
Abstract Ozone (O 3 ) is a highly potent and reactive air pollutant. It has been linked to acute and chronic respiratory diseases in humans by inducing inflammation. Our studies have found evidence that 0.05 ppm of O 3 , within the threshold of air quality standards, is capable of inducing acute lung injury. This study was undertaken to examine O 3 -induced lung damage using [ 18 F]F-FDG (2-deoxy-2-[ 18 F]fluoro-D-glucose) microPET/CT in wild-type mice. [ 18 F]F-FDG is a known PET tracer for inflammation. Sequential [ 18 F]F-FDG microPET/CT was performed at baseline (i.e. before O 3 exposure), immediately (0 h), at 24 h and at 28 h following 2 h of 0.05 ppm O 3 exposure. The images were quantified to determine O 3 induced spatial standard uptake ratio of [ 18 F]F-FDG in relation to lung tissue density and compared with baseline values. Immediately after O 3 exposure, we detected a 72.21 ± 0.79% increase in lung [ 18 F]F-FDG uptake ratio when compared to baseline measures. At 24 h post-O 3 exposure, the [ 18 F]F-FDG uptake becomes highly variable (S.D. in [ 18 F]F-FDG = 5.174 × 10 –4 units) with a 42.54 ± 0.33% increase in lung [ 18 F]F-FDG compared to baseline. At 28 h time-point, [ 18 F]F-FDG uptake ratio was similar to baseline values. However, the pattern of [ 18 F]F-FDG distribution varied and was interspersed with zones of minimal uptake. Our microPET/CT imaging protocol can quantify and identify atypical regional lung uptake of [ 18 F]F-FDG to understand the lung response to O 3 exposure.
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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) | 0.001 | 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 | 0.001 | 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".