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Record W4407669159 · doi:10.1177/13872877251317659

Clinical relevance of plasma ADAM-17 with cognition and neurodegeneration in Alzheimer's disease

2025· article· en· W4407669159 on OpenAlexaboutno aff
Zu-Qi Chen, Meng Ting Wang, Cheng‐Rong Tan, Shan Huang, Faying Zhou, Ying‐Ying Shen, Gui‐Hua Zeng, Dong‐Yu Fan, Yan‐Jiang Wang

Bibliographic record

VenueJournal of Alzheimer s Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNeurodegenerationAlzheimer's diseaseCognitionClinical significanceDiseaseDementiaMedicineClinical Dementia RatingPsychologyInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

Background A disintegrin and metalloproteinase 17 (ADAM-17) has multiple pathophysiological functions in Alzheimer's disease (AD). However, the clinical relevance of ADAM-17 in AD is not clear yet. Objective This study aims to investigate the levels of circulating ADAM-17 and their association with AD. Methods This cross-sectional study recruited 40 normal cognition (NC) participants and 36 AD patients. Plasma ADAM-17 and biomarkers of neurodegeneration were determined. The association of plasma ADAM-17 with cognitive functions and biomarkers of neurodegeneration was analyzed. Results Plasma ADAM-17 levels were elevated in AD patients in comparison with NC subjects. Plasma ADAM-17 was positively associated with Clinical Dementia Rating (CDR) scores, but negatively associated with the Mini-Mental State Examination scores and Montreal Cognitive Assessment scores. Plasma ADAM-17 levels were positively associated with the levels of Aβ 40 , Aβ 42 , and p-Tau181. Conclusions These findings suggest a link between ADAM-17 and the pathogenesis of AD from a clinical perspective.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.353
Teacher spread0.315 · 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
Published2025
Admission routes1
Has abstractyes

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