Concordance between In Vitro and In Vivo Relative Toxic Potencies of Diesel Exhaust Particles from Different Biodiesel Blends
Why this work is in the frame
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Bibliographic record
Abstract
Diesel exhaust particles (DEPs) contribute to air pollution exposure-related adverse health impacts. Here, we examined in vitro, and in vivo toxicities of DEPs from a Caterpillar C11 heavy-duty diesel engine emissions using ultra-low-sulfur diesel (ULSD) and biodiesel blends (20% v/v) of canola (B20C), soy (B20S), or tallow–waste fry oil (B20T) in ULSD. The in vitro effects of DEPs (DEPULSD, DEPB20C, DEPB20S, and DEPB20T) in exposed mouse monocyte/macrophage cells (J774A.1) were examined by analyzing the cellular cytotoxicity endpoints (CTB, LDH, and ATP) and secreted proteins. The in vivo effects were assessed in BALB/c mice (n = 6/group) exposed to DEPs (250 µg), carbon black (CB), or saline via intratracheal instillation 24 h post-exposure. Bronchoalveolar lavage fluid (BALF) cell counts, cytokines, lung/heart mRNA, and plasma markers were examined. In vitro cytotoxic potencies (e.g., ATP) and secreted TNF-α were positively correlated (p < 0.05) with in vivo inflammatory potency (BALF cytokines, lung/heart mRNA, and plasma markers). Overall, DEPULSD and DEPB20C appeared to be more potent compared to DEPB20S and DEPB20T. These findings suggested that biodiesel blend-derived DEP potencies can be influenced by biodiesel sources, and inflammatory process- was one of the potential underlying toxicity mechanisms. These observations were consistent across in vitro and in vivo exposures, and this work adds value to the health risk analysis of cleaner fuel alternatives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it