Neuroinflammation in Alzheimer's Disease and Mild Cognitive Impairment: A Field in Its Infancy
Bibliographic record
Abstract
Neuroinflammation is a prominent feature of Alzheimer disease (AD) and other chronic neurodegenerative disorders. It exacerbates the fundamental pathology by generating a plethora of inflammatory mediators and neurotoxic compounds. Inflammatory cytokines, complement components, and toxic free radicals are among the many species that are generated. Microglia attack the pathological entities and may inadvertently injure host neurons. Recent evidence indicates that microglia can be stimulated to assume an antiinflammatory state rather than a proinflammatory state which may have therapeutic potential. Proinflammatory cytokines include IL-1, IL-6 and TNF, while antiinflammatory cytokines include IL-4 and IL-10. Complement activation is a separate process which causes extensive neuronal damage in AD through assembly of the membrane attack complex. Aggregated amyloid-beta is a potent activator of human complement but not of mouse complement. This is an important difference between AD and transgenic mouse models of AD. Many so far unexplored molecules may contribute to neuroinflammation or act to inhibit it. Stable isotope labeling by amino acids in cell culture (SILAC) analysis identified 174 proteins that were upregulated by two-fold or more, and 189 that were downregulated by 2-fold or more following inflammatory stimulation of microglial-like THP-1 cells. Neurotoxicity may result from any combination of these and further exploration is clearly warranted. In addition, many small molecules may play a significant role. One example is hydrogen sulfide which appears to be an endogenous antiinflammatory agent.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".