A novel method to detect label-free nanoplastics within whole organisms using enhanced dark field hyperspectral imaging
Bibliographic record
Abstract
Anthropogenic activities have led to most generated plastic being landfilled or introduced to the environment.Natural weathering conditions, such as exposure to UV irradiation, mechanical degradation, and temperature changes, break down plastic litter into smaller microplastics and nanoplastics (MNP).These emerging contaminants pose a hazard to the aquatic environment and life because of the risk of consumption and bioaccumulation up the food chain.The omnipresence of MNP compels researchers to better understand their transport, toxicity, and fate in aquatic environments, including freshwaters.However, nanoplastics have proven challenging to detect due to their smaller size, which has limited our understanding of their environmental impacts.This work focuses on detecting internalized MNP in a model freshwater organism (Daphnia magna) using a combination of histological techniques and enhanced darkfield hyperspectral microscopy.The advantage of this method is that it is label-free, meaning that the plastics do not need to be pre-labeled prior to internalization by organisms.This study presents a method to modify the spectral response of organism biomass by staining the biomass with a dye, enabling the detection of ingested MNP.This makes it a promising methodology for ecotoxicology studies since uptaken MNP can be localized inside the organism, thereby helping to understand the observed metabolic impacts.This research will aid in shaping future research on the impact of MNP pollution on freshwater systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".