Evidence map›Paper›PMID 42289538›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Investigating AD and non-AD [

Francesco Piva, Cecilia Boccalini, Nicholas J Ashton, Henrik Zetterberg, Kaj Blennow, Giovanni B Frisoni, Mattia Veronese, Valentina Garibotto, Débora Elisa Peretti

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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Francesco PivaDepartment of Information Engineering, University of Padua, Padua, Italy.ORCID 0009-0002-8174-306X
Cecilia BoccaliniLaboratory of Neuroimaging and Innovative Molecular Tracers, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205, Geneva, Switzerland.ORCID 0000-0002-3518-436X
Nicholas J AshtonCentre for Age-Related Medicine, Stavanger University Hospital, Stravanger, Norway.
Henrik ZetterbergDepartment of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.ORCID 0000-0003-3930-4354
Kaj BlennowDepartment of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Giovanni B FrisoniLaboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland.ORCID 0000-0002-6419-1753
Mattia VeroneseDepartment of Information Engineering, University of Padua, Padua, Italy.ORCID 0000-0003-3562-0683
Valentina GaribottoLaboratory of Neuroimaging and Innovative Molecular Tracers, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205, Geneva, Switzerland.ORCID 0000-0003-2422-698X
Débora Elisa PerettiLaboratory of Neuroimaging and Innovative Molecular Tracers, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205, Geneva, Switzerland. Debora.Peretti@unige.ch.ORCID 0000-0002-3033-8817

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAmyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (AD). Clinical diagnostic protocols frequently rely on the combined use of amyloid (A)- and tau (T)- positron emission tomography (PET) imaging to distinguish AD from non-amyloid-associated neurodegenerative conditions. Many neurodegenerative disorders are characterized by distinct tauopathies, which result in different [

methodsA total of 81 subjects were included in this study: 40 with AD characterized by both A and T accumulation (A + T +), 13 with suspected non-Alzheimer's pathology (A - T +), and 28 without A and T abnormality (A - T -). SSM/PCA was applied on [

resultsThe two identified disease patterns exhibited distinct PET signatures consistent with current disease knowledge. Their representativeness was further supported by significant correlations with plasma biomarkers in nearly all cases. All machine learning models confirmed the capacity of the A + DP to predict AD pathology, correctly classifying at least 36 out of 40 cases. In contrast, the A - DP showed limited discriminative power for non-AD conditions, as indicated by its higher misclassification rate (best performance: 7 correct out of 13).

conclusionSSM/PCA applied on [

Indexed as

Alzheimer DiseaseCarbolinesPositron-Emission TomographyAgedAged, 80 and overCohort StudiesFemaleHumansMalePrincipal Component AnalysisRadiopharmaceuticalstau Proteins7-(6-fluoropyridin-3-yl)-5H-pyrido(4,3-b)indoleCarbolinesRadiopharmaceuticalstau Proteins[18F]flortaucipirAlzheimer’s diseasePETPlasma biomarkersSSM/PCA

Identifiers

PMID42289538
PMCPMC13526141

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.