Detection of acute brain injury by Raman spectral signature
Bibliographic record
Abstract
Brain injury can lead to irreversible tissue loss and functional deficit along with significant health care costs. Raman spectroscopy can be used as a non-invasive technique to provide detailed information on the molecular composition of diseased and damaged tissues. This technique was used to examine acute mouse brain injury, focusing on the motor cortex, a region directly involved in controlling execution of movement. The spectral profile obtained from the injured brain tissue revealed a markedly different signature, particularly in the amide I and amide III vibrational region when compared to that of healthy brain tissue. Most noticeably, there was a significant reduction of the amide I vibration at the acute injury site and the appearance of two distinct features at 1586 and 1618 cm(-1). Complementary immunohistochemical analysis of the injured brain tissue showed an abundant expression of Caspase 3 (a cysteine protease marker used for apoptosis), suggesting that the injury-induced specific Raman shifts may be correlated with cell death. Taken together, this study demonstrates that Raman spectroscopy can play an important role in detecting the changes that occur in the injured brain and provide a possible technology for monitoring the recovery process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".