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Record W2981237077 · doi:10.1016/j.jalz.2019.06.4209

IC‐P‐047: VENOUS COLLAGENOSIS AS PATHOGENESIS OF WHITE MATTER HYPERINTENSITIES

2019· article· en· W2981237077 on OpenAlexaff
David Lahna, Erin L. Boespflug, Randall L. Woltjer, Daniel L. Schwartz, Sandra E. Black, Natalie Roese, Julia Keith, Fuqiang Gao, Joel Ramirez, Lisa C. Silbert

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsSunnybrook HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsHyperintensityMedicineMasson's trichrome stainTrichromeWhite matterPathologyAnatomyLumen (anatomy)CardiologyMagnetic resonance imagingRadiologyInternal medicineFibrosisH&E stain

Abstract

fetched live from OpenAlex

T2 MRI White matter hyperintensities (WMH) are commonly observed in older individuals and are associated with cognitive and motor decline. While many previous studies have found associations between WMH and small vessel arterial disease (i.e. arteriolosclerosis), the full spectrum of WMH etiology remains unknown. Some studies implicate vascular insufficiency, a consequence of venous collagenosis, as a significant contributor to WMH burden. Few have examined both arterial and venous collagenosis in relation to pre-mortem MRI WMH. Brain tissue from 25 Oregon Alzheimer's Disease Center subjects were selected based on availability of in vivo 1.5 Tesla MRI (Table 1). Three paraffin embedded 6μm thick coronal blocks of tissue per subject from anterior, middle and posterior white matter abutting the ventricle were stained with Masson's Trichrome and Smooth Muscle Actin (SMA). Slides were scanned at a native resolution of .773μm2/pixel and downsampled to 13.4μm2/pixel. An automated algorithm identified blue collagen in vessel walls on the basis of hue and the lumen hole within them in trichrome images (Figure 1). Objects smaller than 0.0134mm2 were excluded and resulting segmentations manually inspected. 414 remaining vessels were classified as arteries or veins after review of coregistered SMA slides (Figure 2). Occlusion of each vessel was calculated as the ratio of the volume of collagen to the total volume (collagen + lumen). T-tests were used to determine characteristic differences between vessel types. Linear regressions, accounting for age at death and MRI to death interval, determined relationships between WMH and occlusions in veins and arteries. WMH, segmented from T2-weighted images, was log-transformed and corrected for intracranial volume. 414 total vessels were analyzed of which 60.6% (n=251) were arteries. Veins were larger (p<.001), less occluded (p<.001) and closer to the ventricular wall (p<.005) than arteries. Veins in the most anterior slice were more occluded than those in posterior slices (p=.014). The degree of occlusion of veins due to collagenosis was correlated with WMH volume (p=.007). Arterial occlusion was not related to WMH volume (p=.799).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.226
Teacher spread0.216 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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