Evidence map›Paper›PMID 40224444›Full record

ArticleACS omega2025

Integrating CRISPR-Cas12a with Aptamer as a Logic Gate Biosensing Platform for the Detection of CD33 and CD123.

Xinyi Yang, Xiaolong Shi, Chenyu Lv, Wenbin Liu, Fengyue Zhang, Bo Liu

Abstract read
In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Xinyi YangInstitute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, Shandong, PR China.ORCID https://orcid.org/0009-0006-5726-480X
Xiaolong ShiInstitution of Computational Science and Technology, Guangzhou University, Guangzhou 510006, Guangdong, PR China.
Chenyu LvInstitute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, Shandong, PR China.
Wenbin LiuInstitution of Computational Science and Technology, Guangzhou University, Guangzhou 510006, Guangdong, PR China.ORCID https://orcid.org/0000-0001-9091-3177
Fengyue ZhangInstitute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, Shandong, PR China.
Bo LiuInstitute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, Shandong, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular logic gates, as biomolecule-based computational systems, are highly suitable for multitarget detection due to their programmability and modularity. However, existing systems are primarily limited to nucleic acid detection and have not been widely applied to disease-related sensing, particularly for disease antigens. CD33 and CD123 are critical biomarkers for acute myeloid leukemia (AML), yet conventional detection methods rely on expensive equipment and complex procedures, limiting their accessibility and practicality. This study designs a DNA logic gate system integrating nucleic acid aptamers, catalytic hairpin assembly (CHA), and CRISPR-Cas12a, pioneering its use for AML antigen detection. The system comprises three modules: input recognition, signal amplification, and signal transduction. Nucleic acid aptamers specifically identify CD33 and CD123, while CHA enables efficient signal amplification and CRISPR-Cas12a generates a fluorescent output via trans-cleavage activity. The system operates stably at room temperature and implements multiple logic gate models, including YES, OR, AND, NOR, and INHIBIT, enabling the simultaneous detection of CD33 and CD123. Experimental results are visually distinguishable under blue light, and the system requires only standard fluorescence detection instruments. In serum samples, it exhibits excellent selectivity and stability, with a detection limit of 0.5 ng/mL. This study pioneers the application of logic gate technology for disease antigen detection, addressing a critical gap in AML biomarker sensing. Our study indicates that this logic detection platform, characterized by its simplicity in operation, high sensitivity, and versatility in logic functions, holds promise as a potent sensing system for the intelligent multiplex target detection of disease antigens, environmental pollutants, and heavy metals.

Identifiers

PMID40224444
PMCPMC11983205

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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.