The Combination of Gold and Silver Food Nanoparticles with Gluten Peptides Alters the Autophagic Pathway in Intestinal Crypt-like Cells
Bibliographic record
Abstract
Metallic nanoparticles (mNPs) are widely used as food additives and can interact with gliadin triggering an immune response, but evaluation of the effects on crypts, hypertrophic in celiac subjects, is still lacking. This study evaluated the effects of gold and silver mNPs in combination with gliadin on crypt-like cells (HIEC-6). Transmission electron microscopy (TEM) was used to evaluate gliadin-mNP aggregates in cells. Western blot and immunofluorescence analysis assessed autophagy-related molecule levels (p62, LC3, beclin-1, EGFR). Lysosome functionality was tested with acridine orange (AO) and Magic Red assays. TEM identified an increase in autophagic vacuoles after exposure to gliadin + mNPs, as also detected by significant increments in LC3-II and p62 expression. Immunofluorescence confirmed the presence of mature autophagosomes, showing LC3 and p62 colocalization, indicating an altered autophagic flux, further assessed with EGFR degradation, AO and Magic Red assays. The results showed a significant reduction in lysosomal enzyme activity and a modest reduction in acidity. Thus, gliadin + mNPs can block the autophagic flux inducing a lysosomal defect. The alteration of this pathway, essential for cell function, can lead to cell damage and death. The potential effects of this copresence in food should be further characterized to avoid a negative impact on celiac disease subjects.
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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".