History of Innate Immunity in Neurodegenerative Disorders
Bibliographic record
Abstract
The foundations of innate immunity in neurodegenerative disorders were first laid by Del Rio Hortega (1919). He identified and named microglia, recognizing them as cells of mesodermal origin. Van Furth in 1969 elaborated the monocyte phagocytic system with microglia as the brain representatives. Validation of these concepts did not occur until 1987 when HLA-DR was identified on activated microglia in a spectrum of neurological disorders. HLA-DR had already been established as a definitive marker of immunocompetent cells of mesodermal origin. It was soon determined that the observed inflammatory reaction was an innate immune response. A rapid expansion of the field took place as other markers of an innate immune response were found that were made by neurons, astrocytes, oligodendroglia, and endothelial cells. The molecules included complement proteins and their regulators, inflammatory cytokines, chemokines, acute phase reactants, prostaglandins, proteases, protease inhibitors, coagulation factors, fibrinolytic factors, anaphylatoxins, integrins, free radical generators, and other unidentified neurotoxins. The Nimmerjahn movies demonstrated that resting microglia were constantly active, sampling the surround, and responding rapidly to brain damage. Ways of reducing the neurotoxic innate immune response and stimulating a healing response continue to be sought as a means for ameliorating the pathology in a spectrum of chronic degenerative disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".