Concurrent Glycogen and Lactate Imaging with FTIR Spectroscopy To Spatially Localize Metabolic Parameters of the Glial Response Following Brain Ischemia
Bibliographic record
Abstract
Imaging energy metabolites as markers of the energy shuttle between glia and neurons following ischemia is an ongoing challenge. Traditional microscopies in combination with histochemistry reveal glycogen accumulation within glia following ischemia, indicating an altered metabolic profile. Although semiquantitative histochemical glycogen analysis is possible, the method suffers from typical confounding factors common to histochemistry, such as variation in reagent penetration and binding. In addition, histochemical detection of glycogen does not reveal information on the metabolic fate of glycogen (i.e., lactate production). Therefore, validation of a direct semiquantitative method to simultaneously image both brain glycogen and lactate in the same tissue section would benefit this research field. In this study, we demonstrate the first application of Fourier transform infrared (FTIR) spectroscopy for simultaneous direct spectroscopic imaging of brain glycogen and lactate, in situ within ex vivo tissue sections. Serial tissue sections were analyzed with anti-glial fibrillary acidic protein (GFAP) immunohistochemistry to provide a comparison between the glycogen and lactate distribution revealed by FTIR and the glial distribution revealed by GFAP immunohistochemistry. The distribution of glycogen revealed by FTIR spectroscopic imaging has been further compared with histochemical detection of glycogen on the adjacent tissue sections. This approach was then applied to study spatiotemporal disturbances in metabolism, relative to glia and neuronal populations, following cerebral ischemia in a murine model of stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".