Renal impairment assessment on adults living nearby a landfill: Early kidney dysfunction biomarkers linked to the environmental exposure to heavy metals
Bibliographic record
Abstract
The aim of this study was to assess the integrity and kidney overall functional capacity of subjects exposed to landfill emissions. Urine and blood levels of Pb and Cd, and several of the newly biomarkers of nephrotoxicity (Kim Injury Molecule 1 (KIM-1), alpha-1 Microglobulin (α1 M), beta-2 Microglobulin (β2 M), Cystatin-C (Cyst C), Clusterin, alpha-glutathione S-transferase (GSTα), pi-glutathione S-transferase (GSTπ), Tissue Inhibitor of Metalloproteinase-1 (TIMP1), Calbindin, Neutrophil Gelatinase-Associated Lipocalin (NGAL), Osteopontin (OPN), (Retinol Binding Protein(RBP), Liver-type Fatty Acid-Binding Protein (FABP-1), Trefoil Factor 3 (TFF3), Collagen VI) were measured in order to assess glomerular and tubule damage in adults living near a landfill. Our results indicate glomerular dysfunction in exposed subjects, and supported evidence of necrosis of proximal and distal tubule epithelial cells as specific biomarkers began to appear in the urine. Positive correlation by Pearson test were obtained between : blood Pb and B-OPN, B-Cyst C, Calbindin, U-KIM-1, TIMP1, U-OPN, and U-Clusterin; and also, between urinary Cd and TIMP1, B-Clusterin, U-OPN, FABP-1, Albumin, and U-Clusterin. The relation between biomarkers of Cd/Pb exposure and early effect biomarkers in this study clearly predicts the future risk of severe kidney injury in subjects living close to the landfill.
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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 | 0.000 | 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) | 0.001 | 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".