Effects of Multiwall Carbon Nanotubes on Premature Kidney Aging: Biochemical and Histological Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have gained much attention due to their superb properties, which make them promising options for the reinforcing composite materials with desirable mechanical properties. However, little is known about the linkage between lung exposure to nanomaterials and kidney disease. In this study, we compared the effects on the kidneys and aging for two different types of multiwall carbon nanotubes (MWCNTs): pristine MWCNTs (PMWCNTs) and acid-treated MWCNTs (TMWCNTs), with TMWCNTs being the preferred form for use as a composite material due to its superior dispersion properties. We used tracheal instillation and maximum tolerated dose (MTD) for both types of CNTs. MTD was determined as a 10% weight loss dose in a 3-month subchronic study, and the appropriate dosage for 1-year exposure was 0.1 mg/mouse. Serum and kidney samples were analyzed using ELISA, Western blot, and immunohistochemistry after 6 months and 1 year of treatment. PMWCNT-administered mice showed the activation of pathways for inflammation, apoptosis, and insufficient autophagy, as well as decreased serum Klotho levels and increased serum levels of DKK-1, FGF-23, and sclerostin, while TMWCNTs did not. Our study suggests that lung exposure to PMWCNTs can induce premature kidney aging and highlights a possible toxic effect of using MWCNTs on the kidneys in the industrial field, further highlighting that dispersibility can affect the toxicity of the nanotubes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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