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Record W2108409243 · doi:10.1002/hbm.22073

Correlation between high-sensitivity C-reactive protein and brain gray matter volume in healthy elderly subjects

2012· article· en· W2108409243 on OpenAlexaff
Yasuyuki Taki, Benjamin Thyreau, Shigeo Kinomura, Kazunori Sato, Ryoi Goto, Kai Wu, Masako Kakizaki, Ichiro Tsuji, Ryuta Kawashima, Hiroshi Fukuda

Bibliographic record

VenueHuman Brain Mapping · 2012
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsInstitute of Aging
FundersNational Institute of Biomedical Imaging and BioengineeringJapan Society for the Promotion of Science
KeywordsMagnetic resonance imagingGrey matterGray (unit)Voxel-based morphometryVoxelMedicinePsychologyBrain sizeInternal medicineCardiologyC-reactive proteinWhite matterCorrelationNeurosciencePathologyInflammationNuclear medicineRadiologyMathematics

Abstract

fetched live from OpenAlex

Although elevated serum high-sensitivity C-reactive protein (hsCRP) is related to atherosclerosis, brain infarction, and cognitive decline, it has not been clarified whether increased hsCRP is associated with the decline in brain gray matter volume. Therefore, the purpose of this study was to determine the relationship between hsCRP levels and brain regional gray matter volume using brain magnetic resonance imaging (MRI) data from 109 community-dwelling healthy elderly subjects. Brain MRIs were processed with voxel-based morphometry using a custom template by applying diffeomorphic anatomical registration using the exponentiated lie algebra (DARTEL) procedure. We found a significant negative correlation between regional gray matter volume of the posterior and lateral aspects of the left temporal cortex and hsCRP level after adjusting for age, gender, and intracranial volume. Our results suggest that subjects who have mild inflammation related to arteriosclerosis have decreased regional gray matter volume in the posterior and lateral aspects of the left temporal cortex. Thus, preventing the progression of arteriosclerosis may be important for preventing a decrease in gray matter volume in healthy elderly subjects.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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 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

Citations47
Published2012
Admission routes1
Has abstractyes

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