Evidence map›Paper›PMID 41307575›Full record

ArticleMikrochimica acta2025

Early diagnosis of transient ischemic attack facilitated by SERS-based artificial intelligence sensors.

Shan Guo, Xue Han, Zelong Li, Zengshan Yu, Hao Chen, Jiahao Cui, Kuihua Li, Mingli Wang, Guochao Shi

Abstract read
PubMed Publisher
In one paragraph

Article in Mikrochimica acta, 2025. 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.

Shan Guo *Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China.
Xue Han *Department of Neurology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, 067000, China.
Zelong LiHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China.
Zengshan YuHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China.
Hao ChenHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China.
Jiahao CuiHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China.
Kuihua LiHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China. likuihua@cdmc.edu.cn.
Mingli WangState Key Laboratory of Metastable Materials Science and Technology, Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao, 066000, China. wml@ysu.edu.cn.
Guochao ShiHebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, 067000, China. sgc@cdmc.edu.cn.

Funding

Science Research Project of Hebei Education Department BJ2025135
6 · The paper itself

Abstract

Transient ischemic attack (TIA) serves as a critical early warning sign for ischemic stroke. Its timely identification holds significant clinical value in reducing recurrence risk and improving patient prognosis. However, existing detection methods exhibit limitations in sensitivity and specificity. This study developed a surface-enhanced Raman scattering (SERS) detection platform based on a lotus leaf (LL)/copper (Cu)/silicon dioxide (SiO

Indexed as

Artificial IntelligenceIschemic Attack, TransientSpectrum Analysis, RamanCopperEarly DiagnosisHumansInterleukin-6Neural Networks, ComputerPlant LeavesSilicon DioxideSilverCopperInterleukin-6Silicon DioxideSilverDeep learningEarly diagnosisIntelligent algorithm modelSERSTransient ischemic attack

Identifiers

PMID41307575

What OpenQuestion 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 OpenQuestion graph.