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Record W4413572025 · doi:10.1093/braincomms/fcaf310

Noradrenergic therapies in neurodegenerative disease: from symptomatic to disease modifying therapy?

2025· review· en· W4413572025 on OpenAlexfundno aff
Robert Durcan, Claire O’Callaghan, James B. Rowe

Bibliographic record

VenueBrain Communications · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
FundersCambridge Centre for Parkinson-PlusMedical Research CouncilNational Institute for Health and Care ResearchMedical Research Council CanadaNational Health and Medical Research CouncilPSP AssociationNIHR Cambridge Biomedical Research CentreWellcome Trust
KeywordsLocus coeruleusNeurodegenerationNeuroscienceNorepinephrineDiseaseMedicineApathyNeuroinflammationMicrogliaInternal medicinePsychologyInflammationDopamineCentral nervous system

Abstract

fetched live from OpenAlex

Abstract A feature shared by many different neurodegenerative diseases is early pathology and degeneration of the pontine locus coeruleus. The human locus coeruleus contains about 50 000 neurons and is the primary source of the neurotransmitter noradrenaline. We propose the hypothesis that noradrenergic drugs can have broad, transdiagnostic benefit in slowing or preventing the progression of neurodegenerative diseases. There are direct noradrenergic anti-inflammatory effects in vivo, with microglia and astrocytes regulated by adrenoreceptors, and noradrenergic influences on glymphatics. Noradrenaline loss is associated with a pro-inflammatory state, promoting further neurodegeneration. Noradrenergic neuron loss is associated with worsening of both amyloid and tau deposition in animal models. There may be indirect survival benefits arising from alleviating the prognostically detrimental features of apathy and impulsivity, and noradrenergic influences on other neurotransmitters. The evidence base we set out supports the need for clinical trials of noradrenergic treatments for disease-modification.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.160
GPT teacher head0.386
Teacher spread0.227 · 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 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

Citations11
Published2025
Admission routes1
Has abstractyes

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