Evidence map›Paper›PMID 39297315›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

Shuqing Wang, Yan Zhu, Zhongzeng Zhou, Yong Luo, Yan Huang, Yibiao Liu, Tailin Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Shuqing WangSchool of Biomedical Engineering, College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, P. R. China.
Yan ZhuSchool of Biomedical Engineering, College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, P. R. China.
Zhongzeng ZhouSchool of Biomedical Engineering, College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, P. R. China.
Yong LuoBeijing Key Laboratory for Bioengineering and Sensing Technology, University of Science and Technology Beijing, Beijing, 100083, P. R. China.
Yan HuangBeijing Key Laboratory for Bioengineering and Sensing Technology, University of Science and Technology Beijing, Beijing, 100083, P. R. China.
Yibiao LiuLonggang District Central Hospital of Shenzhen, Shenzhen, Guangdong, 518116, P. R. China.
Tailin XuSchool of Biomedical Engineering, College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, P. R. China.ORCID 0000-0003-4037-2856

Funding

National Natural Science Foundation of China 22234006Shenzhen University 2035 Program for Excellent Research 86901-00000221Synthetic Biology Research Center of Shenzhen University
6 · The paper itself

Abstract

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical application. In this work, a machine learning (ML)-optimized colorimetric LFA with ultrasound enrichment is developed to achieve the sensitive and accurate detection of tau proteins for early screening of Alzheimer's disease (AD). The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre-enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. The results of the undiluted serum sample testing show that the protocol enables efficient classification and accurate quantification of the AD biomarker tau protein concentration with an average classification accuracy of 98.11% and an average prediction accuracy of 99.99%, achieving a limit of detection (LOD) as sensitive as 10.30 pg mL

Indexed as

Alzheimer DiseaseBiomarkersColorimetryMachine LearningMetal NanoparticlesGoldHumansPoint-of-Care TestingSensitivity and Specificitytau ProteinsBiomarkersGoldtau Proteinsalzheimer's diseasecolorimetric biosensinglateral flow assaymachine learningultrasound enrichment

Identifiers

PMID39297315
PMCPMC11558096

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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.