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Record W1986731427 · doi:10.1155/2014/103780

Potential Benefits of Therapeutic Use of<b><i>β</i>2</b>-Adrenergic Receptor Agonists in Neuroprotection and Parkinson’s Disease

2014· review· en· W1986731427 on OpenAlexaff
Lynda J. Peterson, Kathleen P. Ismond, Elisha Chapman, Patrick M. Flood

Bibliographic record

VenueJournal of Immunology Research · 2014
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReceptorNeuroprotectionStimulationImmune systemG protein-coupled receptorDiseaseParkinson's diseaseNeuroscienceInnate immune systemBiologyMedicineImmunologyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

The β2-adrenergic receptor (β2AR) is a seven-transmembrane (7TM) G-protein coupled receptor that is expressed on cells of the pulmonary, cardiac, skeletal muscle, and immune systems. Previous work has shown that stimulation of this receptor on immune cells has profound effects on the regulatory activity of both adaptive and innate immune cells. This review examines the functional dichotomy associated with stimulation of β2AR and microglial cells. As well, recent studies targeting these receptors with long-acting agonists are considered with respect to their therapeutic potential in management of Parkinsonμs disease.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.160
GPT teacher head0.373
Teacher spread0.213 · 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

Citations46
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Immunology ResearchSame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207