MétaCan
Menu
Back to cohort
Record W4386996219 · doi:10.3390/cells12192336

Astrocytes in Parkinson’s Disease: From Role to Possible Intervention

2023· review· en· W4386996219 on OpenAlexaff
Tianyou Wang, Yingqi Sun, Ulf Dettmer

Bibliographic record

VenueCells · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCollège Jean-de-Brébeuf
Fundersnot available
KeywordsAstrocyteNeuroscienceNeuroprotectionNeurotrophic factorsParkinson's diseaseNeurotoxicityDopaminergicGlutamate receptorGlial cell line-derived neurotrophic factorNeurotrophinMedicineBiologyDiseaseCentral nervous systemDopaminePathologyInternal medicine

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons. While neuronal dysfunction is central to PD, astrocytes also play important roles, both positive and negative, and such roles have not yet been fully explored. This literature review serves to highlight these roles and how the properties of astrocytes can be used to increase neuron survivability. Astrocytes normally have protective functions, such as releasing neurotrophic factors, metabolizing glutamate, transferring healthy mitochondria to neurons, or maintaining the blood-brain barrier. However, in PD, astrocytes can become dysfunctional and contribute to neurotoxicity, e.g., via impaired glutamate metabolism or the release of inflammatory cytokines. Therefore, astrocytes represent a double-edged sword. Restoring healthy astrocyte function and increasing the beneficial effects of astrocytes represents a promising therapeutic approach. Strategies such as promoting neurotrophin release, preventing harmful astrocyte reactivity, or utilizing regional astrocyte diversity may help restore neuroprotection.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
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)0.0000.006

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 head0.076
GPT teacher head0.329
Teacher spread0.253 · 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

Citations35
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCellsSame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207