Tissue Clearing To Localize Microplastics via Three-Dimensional Imaging of Whole Organisms
Bibliographic record
Abstract
Understanding the biological impacts of plastic pollution requires an effective methodology to detect unlabeled microplastics in environmental samples. Detecting unlabeled microplastics in an organism generally requires a digestion protocol, which results in the loss of spatial information on the distribution of microplastic within the organism and could lead to the disappearance of the smaller plastics. Fluorescence microscopy allows visualization of ingested microplastics but many labeling strategies are nonspecific and label biomass, thus limiting our ability to distinguish internalized plastics. While prelabeled plastics can be used to avoid nonspecific labeling, this approach precludes the detection of environmental microplastics in organisms. Also, using prelabeled microplastics can affect the viability of the organism and impact plastic uptake. Thus, a method was developed that employs nonspecific labeling with a tissue-clearing technique. Briefly, unlabeled microplastics are stained with a fluorescent dye after ingestion by the organism. The tissue-clearing technique then removes tissue-bound dye while rendering the structurally intact organism transparent. The internalized plastics remain stained and can be visualized in the cleared tissue with fluorescence microscopy. The technique is demonstrated using polystyrene beads in living aquatic organisms Tigriopus californicus and Daphnia magna and by spiking a model vertebrate ( Cephalochordata ) with different microplastics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".