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Combinatorial Treatment Using Umbilical Cord Perivascular Cells and Aβ Clearance Rescues Vascular Function Following Transient Hypertension in a Rat Model of Alzheimer Disease

2019· article· en· W2972301686 on OpenAlexaff
Paolo Bazzigaluppi, Tina L. Beckett, Margaret M. Koletar, Mary Hill, Aaron Y. Lai, Arunachala U. Trivedi, Lynsie A.M. Thomason, Adrienne Dorr, Denis Gallagher, Clifford Librach, Illsung L. Joo, JoAnne McLaurin, Bojana Stefanovic

Bibliographic record

VenueHypertension · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCReATe Fertility CentreSunnybrook Health Science CentreUniversity of TorontoSunnybrook Hospital
FundersNational Institute on Aging
KeywordsMedicineUmbilical cordPathologyVascular diseaseInternal medicineEndocrinologyAnatomy

Abstract

fetched live from OpenAlex

Transient hypertension is a risk factor for Alzheimer disease (AD), but the effects of this interaction on brain vasculature are understudied. Addressing vascular pathology is a promising avenue to potentiate the efficacy of treatments for AD. We used arterial spin labeling magnetic resonance imaging to longitudinally assess brain vascular function and immunohistopathology to examine cerebrovascular remodeling and amyloid load. Hypertension was induced for 1 month by administration of l -N G -nitroarginine-methyl-ester in TgF344-AD rats at the prodromal stage. Following hypertension, nontransgenic rats showed transient cerebrovascular changes, whereas TgF344-AD animals exhibited sustained alterations in cerebrovascular function. Human umbilical cord perivascular cells in combination with scyllo-inositol, an inhibitor of Aβ oligomerization, resulted in normalization of hippocampal vascular function and remodeling, in contrast to either treatment alone. Prodromal stage hypertension exacerbates latter AD pathology, and the combination of human umbilical cord perivascular cells with amyloid clearance promotes cerebrovascular functional recovery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.278
Teacher spread0.237 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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