Synchrotron Radiation-Based FTIR Microspectroscopic Imaging of Traumatically Injured Mouse Brain Tissue Slices
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a health problem of global concern because of its serious adverse effects on public health and social economy. A technique that can be used to precisely detect TBI is highly demanded. Here, we report on a synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopic imaging technique that can be exploited to identify TBI-induced injury by examining model mouse brain tissue slices. The samples were first examined by conventional histopathological techniques including hematoxylin and eosin (H&E) staining and 2,3,5-triphenyltetrazolium chloride staining and then spectroscopically imaged by SR-FTIR. SR-FTIR results show that the contents of protein and nucleic acid in the injured region are lower than their counterparts in the normal region. The injured and normal regions can be unambiguously distinguished from each other by the principle component analysis of the SR-FTIR spectral data corresponding to protein or nucleic acid. The images built from the spectral data of protein or nucleic acid clearly present the injured region of the brain tissue, which is in good agreement with the H&E staining image and optical image of the sample. Given the label-free and fingerprint features, the demonstrated method suggests potential application of SR-FTIR spectroscopic mapping for the digital and intelligent diagnosis of TBI by providing spatial and chemical information of the sample simultaneously.
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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 | 0.000 |
| 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".