High-resolution x-ray absorption spectroscopy studies of metal compounds in neurodegenerative brain tissue
Bibliographic record
Abstract
Fluorescence mapping and microfocus X-ray absorption spectroscopy are used to detect, locate and identify iron biominerals and other inorganic metal accumulations in neurodegenerative brain tissue at sub-cellular resolution (<5 microns). Recent progress in developing the technique is reviewed. Synchrotron X-rays are used to map tissue sections for metals of interest, and XANES and XAFS are used to characterise anomalous concentrations of the metals in-situ so that they can be correlated with tissue structures and disease pathology. Iron anomalies associated with biogenic magnetite, ferritin and haemoglobin are located and identified in an avian tissue model with a pixel resolution ∼5 microns. Subsequent studies include brain tissue sections from transgenic Huntington's mice, and the first high-resolution mapping and identification of iron biominerals in human Alzheimer's and control autopsy brain tissue. Technical developments include use of microfocus diffraction to obtain structural information about biominerals in-situ , and depositing sample location grids by lithography for the location of anomalies by conventional microscopy. The combined techniques provide a breakthrough in the study of both intra- and extra-cellular iron compounds and related metals in tissue. The information to be gained from this approach has implications for future diagnosis and treatment of neurodegeneration, and for our understanding of the mechanisms involved.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".