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Involvement of non-neuronal brain cells in AVP-mediated regulation of water space at the cellular, organ, and whole-body level

2000· review· en· W2046442661 on OpenAlexaff
Leif Hertz, Maria Spatz

Bibliographic record

VenueJournal of Neuroscience Research · 2000
Typereview
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChoroid plexusVasopressinEpendymal CellCell biologyParenchymaIntracellularBiologyAstrocyteCell typeBlood–brain barrierExtracellularCircumventricular organsChemistryEndocrinologyInternal medicineNeuroscienceCentral nervous systemCellMedicineBiochemistry

Abstract

fetched live from OpenAlex

Vasopressin (AVP) influences non-neuronal brain cells in cell-type specific manners: (1) it regulates water balance at the cellular level of brain parenchyma by adjusting astrocytic water permeability; (2) it contributes to the control of extracellular K(+) concentration ([K(+)](e)) in brain by stimulation of K(+) transfer from blood to brain, due to activation of an inwardly directed Na(+),K(+),Cl(-) cotransporter at the luminal membrane of capillary endothelial cells and opening of K(+) channels at their abluminal membrane; (3) it decreases formation of cerebrospinal fluid (CSF) by decreasing Cl(-) secretion into CSF by epithelial cells of the choroid plexus, probably by inhibition of Cl(-)/HCO(-)(3) exchange at their basolateral membrane; (4) it contributes to regulation of intracellular volume within the brain by regulation of water permeability in ependymal cells and subpial astrocytes; and (5) it exerts effects on specialized astrocytes in circumventricular organs, their adjacent glia limitans, and the neural pituitary, which regulate AVP release to the systemic circulation by altering the spatial relationship between neurons and their adjacent glial cells. A unified mechanism is proposed, which integrates most of the effects of AVP and may be of considerable importance for neuronal excitability and, thus, for behavior.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.950
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.205
GPT teacher head0.447
Teacher spread0.242 · 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.

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

Citations32
Published2000
Admission routes1
Has abstractyes

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