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Record W2050206306 · doi:10.1196/annals.1332.007

Inflammation and the Degenerative Diseases of Aging

2004· review· en· W2050206306 on OpenAlexafffund
Patrick L. McGeer, Edith G. McGeer

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2004
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of British Columbia
FundersAlzheimer Society
KeywordsInflammationMedicineImmunologyRheumatoid arthritisMicrogliaChemokine

Abstract

fetched live from OpenAlex

Chronic inflammation is associated with a broad spectrum of neurodegenerative diseases of aging. Included are such disorders as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis, the Parkinson-dementia complex of Guam, all of the tauopathies, and age-related macular degeneration. Also included are such peripheral conditions as osteoarthritis, rheumatoid arthritis, atherosclerosis, and myocardial infarction. Inflammation is a two-edged sword. In acute situations, or at low levels, it deals with the abnormality and promotes healing. When chronically sustained at high levels, it can seriously damage viable host tissue. We describe this latter phenomenon as autotoxicity to distinguish it from autoimmunity. The latter involves a lymphocyte-directed attack against self proteins. Autotoxicity, on the other hand, is determined by the concentration and degree of activation of tissue-based monocytic phagocytes. Microglial cells are the brain representatives of the monocyte phagocytic system. Biochemically, the intensity of their activation is related to a spectrum of inflammatory mediators generated by a variety of local cells. The known spectrum includes, but is not limited to, prostaglandins, pentraxins, complement components, anaphylotoxins, cytokines, chemokines, proteases, protease inhibitors, adhesion molecules, and free radicals. This spectrum offers a huge variety of targets for new anti-inflammatory agents. It has been suggested, largely on the basis of transgenic mouse models, that stimulating inflammation rather than inhibiting it can be beneficial in such diseases as AD. If this were the case, administration of NSAIDs, or other anti-inflammatory drugs, would be expected to exacerbate conditions such as AD, PD, and atherosclerosis. However, epidemiological evidence overwhelmingly demonstrates that the reverse is true. This indicates that, at least in these diseases, the inflammation is harmful. So far, advantage has not been taken of opportunities indicated by these epidemiological studies to treat AD and PD with appropriate anti-inflammatory agents. Based on this evidence, classical NSAIDs are the most logical choice. Dosage, though, must be sufficient to combat the inflammation. Analysis of mRNA levels of inflammatory mediators indicates that the intensity of inflammation is considerably higher in AD hippocampus and in PD substantia nigra than in osteoarthritic joints. Thus, full therapeutic doses of NSAIDs, or combinations of anti-inflammatory agents, are needed to achieve the suggested neurological benefits.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.173
GPT teacher head0.379
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations522
Published2004
Admission routes2
Has abstractyes

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