Evidence map›Paper›PMID 41407593›Full record

ArticleNeurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics2026

Deep learning analysis of urine-derived stem cell mitochondrial morphology as a non-invasive Alzheimer's disease biomarker.

Ran Yan, Wenhua Zhang, Wenjing Wang, Jiaqi Wu, Jun Zhang, Yingjie Xu, Wei Xu, Wen Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 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

8 authors.

Ran YanDepartment of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: yanran7@alumni.sjtu.edu.cn.
Wenhua ZhangSchool of Future Techonology, Shanghai University, Shanghai, China. Electronic address: wenhua-zhang@shu.edu.cn.
Wenjing WangDepartment of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: wangwj0827@sjtu.edu.cn.
Jiaqi WuDepartment of Biology, Bates College, Lewiston, ME, USA. Electronic address: jwu@bates.edu.
Jun ZhangTencent AI Lab, Shenzhen, 518057, China. Electronic address: junejzhang@tencent.com.
Yingjie XuDepartment of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China; RNAcure Biopharma, Shanghai, China. Electronic address: xuyingjie@shsmu.edu.cn.
Wei XuDepartment of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: xw11246@rjh.com.cn.
Wen YangDepartment of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: yangwen@shsmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD), closely associated with mitochondrial dysfunction, currently lacks convenient and non-invasive biomarkers for mitochondrial assessment. In this study, we developed an artificial intelligence framework leveraging live urine-derived stem cell (USC) mitochondrial fluorescence imaging to investigate differences between cognitively impaired individuals (AD and mild cognitive impairment (MCI)) and cognitively normal (CN) subjects. Mitochondrial fluorescence images from living HeLa cells were first segmented, and two binary classification models based on the ResNet-18 convolutional neural network were trained to identify mitochondrial hyperfission and hyperfusion relative to normal morphology. The models demonstrated robust performance in detecting intermediate mitochondrial states during validation. When applied to USCs, the system effectively distinguished mitochondrial patterns associated with cognitive impairment, highlighting its potential for the early detection of Alzheimer's disease and merits further validation in larger, independent cohorts.

Indexed as

Alzheimer DiseaseDeep LearningMitochondriaStem CellsAgedAged, 80 and overBiomarkersCognitive DysfunctionFemaleHeLa CellsHumansMaleUrineBiomarkersAlzheimer’s diseaseArtificial intelligenceMitochondrial morphologyUrine-derived stem cell

Identifiers

PMID41407593
PMCPMC12976509

What Socratic holds

Textmetadata
LicenceCC BY
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.