P.061 Multi-modal synchrotron imaging techniques to quantify elemental and molecular changes after stroke in an animal model
Bibliographic record
Abstract
Background: Effects of stroke at the cellular and sub-cellular level remain poorly understood by conventional techniques. We use synchrotron-based imaging techniques to study elemental and biochemical changes in the infarct and penumbra after stroke in an experimental model. Methods: Ischemic stroke is induced in mice using the previously validated photothrombotic model. Animals are sacrificed at various time-points after stroke. Fourier transform infrared spectroscopic imaging (FTIRI) is used to gather sub-cellular (<1 µm spatial resolution) imaging data of lipid oxidation and protein aggregation in the areas of interest. X-ray fluorescence (XRF) imaging is used to image the distribution of bio-important elements at the cellular and sub-cellular spatial resolutions. Routine histology and immunohistochemistry are used to co-localize cell-types to areas of interest. Results: Preliminary XRF results indicate significant reduction in the concentration of multiple elements in the infarct, compared to the penumbra, at day 1 post-stroke. Some elements begin to return to normal concentration in the penumbra at day 3. FTIRI data shows that lipid and total protein levels decrease, while aggregate protein levels increase in the penumbra. Conclusions: Multi-modality synchrotron imaging can be used to map elements as well as bio-molecules in a stroke model. A better understanding of these changes can guide therapeutic interventions after stroke.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".