Abstract 102: Imaging Markers Of Energy Metabolism In The Post-stroke Mouse Brain
Bibliographic record
Abstract
Introduction: Cerebral ischemia results in a lesion comprised of dying cells surrounded by a band of tissue containing cells at risk of dying, called the penumbra. Understanding the status of energy metabolism in the stroke is necessary to get a clearer picture of the formation of the lesion, and may provide insights into potential therapeutic targets. The detection and distribution of energy metabolism biomarkers, particularly in ischemic brain tissue, presents significant challenges for conventional laboratory techniques due to the labile nature of these markers and the lack of unique chemical or spectroscopic handles. Fourier Transform infrared (FTIR) spectroscopic imaging can be used to map the distribution of different biochemical parameters, such as lipids, proteins, aggregated proteins, glycogen, lactate, pyruvate, ATP/ADP, NADH and glutamate. Methods: We employed the photothrombotic stroke model in adult mice to produce a permanent focal stroke lesion. Brain tissue was collected at multiple time points (1h to 4 weeks post-stroke). FTIR imaging was employed to map the distribution of unique biochemical fingerprints for over a dozen key metabolic markers. Immunohistochemistry was also performed on adjacent tissues to correlate biomarkers revealed by FTIR with the distribution of astrocytes, myelin and macrophages. Results: During the first week poststroke, the lesion shows decreased lipid esters, protein, ATP/ADP, NADH content while showing an increase in glutamate, aggregated protein, lactate and pyruvate. Glycogen can be seen to accumulate in the penumbra starting at 1 day post-stroke until 1 week post-stroke, which correlates with the appearance of astrocytes around the border of the stroke lesion. From 1 week to 4 weeks post-stroke, we observe an increase in lipid esters in the lesion, as well as a decrease in glycogen, lactate and pyruvate, while an increase in ATP/ADP and NADH can be observed. Conclusion: We are showing for the first time novel images of multiple energy metabolism biomarkers across a range of post-stroke time points in a mammalian brain. This type of imaging will be important in studying post-stroke interventions, particularly drugs that are known to alter energy metabolism.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".