Direct label-free imaging of brain tissue using synchrotron light: a review of new spectroscopic tools for the modern neuroscientist
Bibliographic record
Abstract
The incidence of brain disease and brain disorders is increasing on a global scale. Unfortunately, development of new therapeutic strategies has not increased at the same rate, and brain diseases and brain disorders now inflict substantial health and economic impacts. A greater understanding of the fundamental neurochemistry that underlies healthy brain function, and the chemical pathways that manifest in brain damage or malfunction, are required to enable and accelerate therapeutic development. A previous limitation to the study of brain function and malfunction has been the limited number of techniques that provide both a wealth of biochemical information, and spatially resolved information (i.e., there was a previous lack of techniques that provided direct biochemical or elemental imaging at the cellular level). In recent times, a suite of direct spectroscopic imaging techniques, such as Fourier transform infrared spectroscopy (FTIR), X-ray fluorescence microscopy (XFM), and X-ray absorption spectroscopy (XAS) have been adapted, optimized and integrated into the field of neuroscience, to fill the above mentioned capability-gap. Advancements at synchrotron light sources, such as improved light intensity/flux, increased detector sensitivities and new capabilities of imaging/optics, has pushed the above suite of techniques beyond "proof-of-concept" studies, to routine application to study complex research problems in the field of neuroscience (and other scientific disciplines). This review examines several of the major advancements that have occurred over the last several years, with respect to FTIR, XFM and XAS capabilities at synchrotron facilities, and how the increases in technical capabilities have being integrated and used in the field of neuroscience.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".