Bibliographic record
Abstract
It is well accepted that CNS inflammation has a role in the progression of chronic neurodegenerative disease, although the mechanisms through which this occurs are still unclear. The inflammatory response during most chronic neurodegenerative disease is dominated by the microglia and mechanisms by which these cells contribute to neuronal damage and degeneration are the subject of intense study. More recently it has emerged that systemic inflammation has a significant role to play in the progression of these diseases. Well-described adaptive pathways exist to transduce systemic inflammatory signals to the brain, but activation of these pathways appears to be deleterious to the brain if the acute insult is sufficiently robust, as in severe sepsis, or sufficiently prolonged, as in repeated stimulation with robust doses of inflammogens such as lipopolysaccharide (LPS). Significantly, moderate doses of inflammogens produce new pathology in the brain and exacerbate or accelerate features of disease when superimposed upon existing pathology or in the context of genetic predisposition. It is now apparent in multiple chronic disease states, and in ageing, that microglia are primed by prior pathology, or by genetic predisposition, to respond more vigorously to subsequent inflammatory stimulation, thus transforming an adaptive CNS inflammatory response to systemic inflammation, into one that has deleterious consequences for the individual. In this review, the preclinical and clinical evidence supporting a significant role for systemic inflammation in chronic neurodegenerative diseases will be discussed. Mechanisms by which microglia might effect neuronal damage and dysfunction, as a consequence of systemic stimulation, will be highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".