FTIR microscopy reveals distinct biomolecular profile of crustacean digestive glands upon subtoxic exposure to ZnO nanoparticles
Bibliographic record
Abstract
Biomolecular profiling with Fourier-Transform InfraRed Microscopy was performed to distinguish the Zn(2+)-mediated effects on the crustacean (Porcellio scaber) digestive glands from the ones elicited by the ZnO nanoparticles (NPs). The exposure to ZnO NPs or ZnCl2 (1500 and 4000 µg Zn/g of dry food) activated different types of metabolic pathways: some were found in the case of both substances, some only in the case of ZnCl2, and some only upon exposure to ZnO NPs. Both the ZnO NPs and the ZnCl2 increased the protein (∼1312 cm(-1); 1720-1485 cm(-1)/3000-2830 cm(-1)) and RNA concentration (∼1115 cm(-1)). At the highest exposure concentration of ZnCl2, where the effects occurred also at the organismal level, some additional changes were found that were not detected upon the ZnO NP exposure. These included changed carbohydrate (most likely glycogen) concentrations (∼1043 cm(-1)) and the desaturation of cell membrane lipids (∼3014 cm(-1)). The activation of novel metabolic pathways, as evidenced by changed proteins' structure (at 1274 cm(-1)), was found only in the case of ZnO NPs. This proves that Zn(2+) are not the only inducers of the response to ZnO NPs. Low bioavailable fraction of Zn(2+) in the digestive glands exposed to ZnO NPs further supports the role of particles in the ZnO NP-generated effects. This study provides the evidence that ZnO NPs induce their own metabolic responses in the subtoxic range.
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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".