Air Pollution Aggravates Ischemia-Reperfusion-Induced AKI in Mice
Bibliographic record
Abstract
Background: The biggest city in Latin America is São Paulo (SP), where disorganized urbanization has had a negative impact on air quality and vehicle emissions are the main source of fine particulate matter (PM2.5). Epidemiological studies have linked PM2.5 exposure to an increased risk of CKD. The mechanisms mediating the adverse health effects of PM2.5 include epigenetic changes, oxidative stress and inflammation. The role of PM2.5 in AKI has yet to be described. We hypothesized that PM2.5 exposure would aggravate renal ischemia/reperfusion (I/R) injury in mice. Methods: In temperature-/humidity-controlled chambers within an ambient particle concentrator, animals were exposed to a concentrated PM2.5 stream (PM2.5) or to high-efficiency particulate air-filtered clean air (CA). Mass concentrations of PM were measured with an airborne particulate monitor, and the target dose was 600 μg m-3/day (equivalent of the daily exposure in SP). After 12 weeks, some PM2.5 and CA mice underwent bilateral 30-min clamping of the kidney hila and subsequent reperfusion. All studies were performed 48 h after I/R. Groups: CA, PM2.5, CA+I/R and PM2.5+I/R. Data are mean±SEM. Results: Renal TLR4 protein expression was higher in CA+I/R and PM2.5+I/R than in CA and PM2.5 (128±2.1 and 146±2.0 vs. 97.5±2.1 and 98.0±0.9%; P<0.05), also being much higher in PM2.5+I/R than in CA+I/R (P<0.05). Manganese superoxide dismutase levels were higher in PM2.5+I/R than in CA+I/R, AF and PM2.5 (146±12 vs. 99±3.6, 102±3.9 and 96±2.8; P<0.05). Conclusions: PM2.5 aggravates I/R-induced AKI by decreasing renal Klotho protein, leading to increased renal TLR4 expression and inflammatory cell infiltration. (FAPESP, NWO) Funding: Government Support - Non-U.S.Biochemistry and histology
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".