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Record W3195941072 · doi:10.3390/cells10082164

Triggering Innate Immune Receptors as New Therapies in Alzheimer’s Disease and Multiple Sclerosis

2021· review· en· W3195941072 on OpenAlexafffund
Pierre-Alexandre Piec, Vincent Pons, Serge Rivest

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsWilfrid Laurier UniversityUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsInnate immune systemMultiple sclerosisDiseaseNeuroscienceImmune systemMedicineImmunityNeurodegenerationNeuroinflammationAlzheimer's diseaseImmunologyBiologyPathology

Abstract

fetched live from OpenAlex

Multiple sclerosis and Alzheimer's disease are two complex neurodegenerative diseases involving the immune system. So far, available treatments provide at best mild improvements to patients' conditions. For decades now, a new set of molecules have been used to modulate and regulate the innate immunity in these pathologies. Most studies have been carried out in rodents and some of them have reported tremendous beneficial effects on the disease course. The modulation of innate immune cells is of great interest since it provides new hope for patients. In this review, we will briefly overview the therapeutic potential of some molecules and receptors in multiple sclerosis and Alzheimer's disease and how they could be used to exploit new therapeutic avenues.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.150
GPT teacher head0.309
Teacher spread0.159 · 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 teacher head, not a consensus.

Study designOther design
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

Citations9
Published2021
Admission routes2
Has abstractyes

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