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Record W2763721270 · doi:10.1042/cs20171265

The perivascular pathways for influx of cerebrospinal fluid are most efficient in the midbrain

2017· article· en· W2763721270 on OpenAlexaff
Howard Dobson, Matthew MacGregor Sharp, Richard Cumpsty, Theodore P. Criswell, Tyler J. Wellman, Ciara Finucane, Jenna Sullivan‐Stack, Roy O. Weller, Ajay Verma, Roxana O. Carare

Bibliographic record

VenueClinical Science · 2017
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsUniversity of GuelphWestern University
FundersUniversity of Southampton
KeywordsGlymphatic systemCerebrospinal fluidPerivascular spaceSubarachnoid spaceMidbrainPia materParenchymaPathologyInterstitial fluidAnatomyMedicineChemistrySubarachnoid hemorrhageCentral nervous systemAnesthesiaEndocrinology

Abstract

fetched live from OpenAlex

Although there are no conventional lymphatic vessels in the brain, fluid and solutes drain along basement membranes (BMs) of cerebral capillaries and arteries towards the subarachnoid space and cervical lymph nodes. Convective influx/glymphatic entry of the cerebrospinal fluid (CSF) into the brain parenchyma occurs along the pial-glial BMs of arteries. This project tested the hypotheses that pial-glial BM of arteries are thicker in the midbrain, allowing more glymphatic entry of CSF. The in vivo MRI and PET images were obtained from a 4.2-year-old dog, whereas the post-mortem electron microscopy was performed in a 12-year-old dog. We demonstrated a significant increase in the thickness of the pial-glial BM in the midbrain compared with the same BM in different regions of the brain and an increase in the convective influx of fluid from the subarachnoid space. These results are highly significant for the intrathecal drug delivery into the brain, indicating that the midbrain is better equipped for convective influx/glymphatic entry of the CSF.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.105
GPT teacher head0.383
Teacher spread0.278 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations36
Published2017
Admission routes1
Has abstractyes

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