High Resolution Dissection of Reactive Glial Nets in Alzheimer’s Disease
Bibliographic record
Abstract
Fixed human brain samples in tissue repositories hold great potential for unlocking complexities of the brain and its alteration with disease. However, current methodology for simultaneously resolving complex three-dimensional (3D) cellular anatomy and organization, as well as, intricate details of human brain cells in tissue has been limited due to weak labeling characteristics of the tissue and high background levels. To expose the potential of these samples, we developed a method to overcome these major limitations. This approach offers an unprecedented view of cytoarchitecture and subcellular detail of human brain cells, from cellular networks to individual synapses. Applying the method to AD samples, we expose complex features of microglial cells and astrocytes in the disease. Through this methodology, we show that these cells form specialized 3D structures in AD that we refer to as reactive glial nets (RGNs). RGNs are areas of concentrated neuronal injury, inflammation, and tauopathy and display unique features around β-amyloid plaque types. RGNs have conserved properties in an AD mouse model and display a developmental pattern coinciding with the progressive accumulation of neuropathology. The method provided here will help reveal novel features of the healthy and diseased human brain, and aid experimental design in translational brain research.
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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 | 0.001 | 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 | 0.000 | 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".