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Record W2982783776 · doi:10.1093/geroni/igz038.959

SOLUBLE INTERCELLULAR ADHESION MOLECULE (SICAM-1) AS A BIOMARKER OF VASCULAR COGNITIVE IMPAIRMENT IN OLDER ADULTS

2019· article· en· W2982783776 on OpenAlexaff
Rosalinda Sánchez Arenas, Michael A. Gregory, Leticia Manuel‐Apolinar

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCohortInternal medicineLogistic regressionEndothelial dysfunctionMedicinePathologicalBiomarkerOncologyDementiaGastroenterologyPsychologyDiseaseBiology

Abstract

fetched live from OpenAlex

Abstract Background: Endothelial dysfunction and subsequent inflammation contribute to the development of vascular cognitive impairment (VCI). Soluble intercellular adhesion molecule-1 (sICAM-1) is upregulated in endothelial dysfunction and promotes an inflammatory response; however, the relationship between sICAM-1 and VCI remains equivocal. Objective: To determine whether sICAM-1 contributes to the prediction of VCI. Methods: Community-dwelling older adults (n=172) from the “Cohort of Obesity, Sarcopenia and Frailty of Older Mexican Adults” (COSFOMA) study were identified as VCI or controls using standard neuropsychological evaluations and neuroimaging. sICAM-1 was quantified using ELISA, and multivariate logistic regression determined the association between sICAM-1 and VCI. Results: 31 VCI cases were identified. sICAM-1 was higher in VCI [VCI: 450.7 (241.6) ng/ml vs. Control: 296.9 (140.9) ng/ml]. sICAM-1 concentrations above the 90th percentile (464.1 ng/mL) was associated with VCI group membership in all models [OR = 6.9 (95% CI: 1.1- 42.2)]. The final saturated model explained 64% of the variance in VCI group membership. Conclusion: High concentrations of sICAM-1 are independently associated with VCI group membership. Efforts to further characterize the relationship between indices of endothelial dysfunction and pathological changes to the aging brain should be further pursued.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.019
GPT teacher head0.274
Teacher spread0.255 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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