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Record W7066201947

Flexing the innate immune arm within the human central nervous system : implications for multiple sclerosis

2007· dissertation· en· W7066201947 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2007
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicElectrical and Electromagnetic Research
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthVA Greater Los Angeles Healthcare SystemGemeinnützige Hertie-StiftungBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftFonds de Recherche du Québec - SantéMultiple Sclerosis Society of CanadaMultiple Sclerosis SocietyCanadian Institutes of Health ResearchU.S. Department of Veterans Affairs
KeywordsCentral nervous systemMultiple sclerosisNeuroinflammationMicrogliaImmune systemContext (archaeology)Antigen presentationMajor histocompatibility complex
DOInot available

Abstract

fetched live from OpenAlex

In the inflammatory brain lesions characteristic of multiple sclerosis (MS), both infiltrating macrophages and their central nervous system counterparts, the resident microglia, are present in large numbers and, along with reactive astrocytes, are implicated as potential antigen presenting cells (APC)s. The demyelinating pathology associated with the infiltration of self-reactive T cells in MS requires local antigenic re-stimulation in the context of major histocompatibility complex (MHC) molecules. Human microglia and, less commonly, astrocytes, have been found to express APC molecules in MS brains. This, combined with the potential for production of immunomodulatory and toxic mediators upon activation, highlights the central role for these glial cells in neuroinflammation and, particularly, in MS.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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