Addressing uncertainties in correlative imaging of exogenous particles with the tissue microanatomy with synchronous imaging strategies
Bibliographic record
Abstract
Exposure to exogenous particles is of increasing concern to human health. Characterizing the concentrations, chemical species, distribution, and involvement of the stimulus with the tissue microanatomy is essential in understanding the associated biological response. However, no single imaging technique can interrogate all these features at once, which confounds and limits correlative analyses. Developments of synchronous imaging strategies, allowing multiple features to be identified simultaneously, are essential to assess spatial relationships between these key features with greater confidence. Here, we present data to first highlight complications of correlative analysis between the tissue microanatomy and elemental composition associated with imaging serial tissue sections. This is achieved by assessing both the cellular and elemental distributions in three-dimensional space using optical microscopy on serial sections and confocal X-ray fluorescence spectroscopy on bulk samples, respectively. We propose a new imaging strategy using lanthanide-tagged antibodies with X-ray fluorescence spectroscopy. Using simulations, a series of lanthanide tags were identified as candidate labels for scenarios where tissue sections are imaged. The feasibility and value of the proposed approach are shown where an exposure of Ti was identified concurrently with CD45 positive cells at sub-cellular resolutions. Significant heterogeneity in the distribution of exogenous particles and cells can be present between immediately adjacent serial sections showing a clear need of synchronous imaging methods. The proposed approach enables elemental compositions to be correlated with the tissue microanatomy in a highly multiplexed and non-destructive manner at high spatial resolutions with the opportunity for subsequent guided analysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".