Evidence map›Paper›PMID 38884120›Full record

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

Dynamic Addressing Molecular Robot (DAMR): An Effective and Efficient Trial-and-Error Approach for the Analysis of Single Nucleotide Polymorphisms.

Tianshu Chen, Guifang Chen, Siyu Cao, Xiaochen Tang, Wenxing Li, Chenbin Liu, Hongquan Gou, Pei Sun, Yichun Mao, Qiuhui Pan and 2 more

Abstract read
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  4. Article
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

12 authors.

Tianshu ChenClinical Laboratory, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
Guifang ChenCenter for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China.
Siyu CaoCenter for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China.
Xiaochen TangClinical Laboratory, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
Wenxing LiDepartment of Clinical Laboratory Medicine, Shanghai Tenth People's Hospital of Tongji University, Shanghai, 200072, P. R. China.
Chenbin LiuDepartment of Clinical Laboratory Medicine, Shanghai Tenth People's Hospital of Tongji University, Shanghai, 200072, P. R. China.
Hongquan GouDepartment of Clinical Laboratory Medicine, Shanghai Tenth People's Hospital of Tongji University, Shanghai, 200072, P. R. China.
Pei SunCenter for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China.
Yichun MaoCenter for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China.
Qiuhui PanClinical Laboratory, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
Penghui ZhangDepartment of Laboratory Medicine, Shanghai Pudong New Area People's Hospital, Shanghai, 201299, P. R. China.
Xiaoli ZhuDepartment of Clinical Laboratory Medicine, Shanghai Tenth People's Hospital of Tongji University, Shanghai, 200072, P. R. China.ORCID 0000-0001-5497-4538

Funding

Key project of Shanghai "Science and Technology Innovation Action Plan" 21JC1406602Key project of Shanghai "Science and Technology Innovation Action Plan" 22JC1402300National Natural Science Foundation of China 82172357National Natural Science Foundation of China 82293662National Natural Science Foundation of China 82370516Natural Science Foundation of Shanghai Municipality 23ZR1480200Sanya Science and Technology Special Fund 2022KJCX38Shanghai Key Laboratory of Clinical Molecular Diagnostics for Pediatrics 20dz2260900Shanghai Sailing Program 22YF1426600
6 · The paper itself

Abstract

Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial-and-error-based molecular recognition approaches have always been challenged in distinguishing minimal differences between targets and non-targets, such as single nucleotide polymorphisms (SNPs) of oligonucleotides. To address these challenges, here, a novel concept of dynamic addressing analysis is proposed. In this concept, by dissecting the regions of the target and creating a corresponding recognizer, it is possible to eliminate the inaccuracy and inefficiency of recognition. To achieve this concept, a Dynamic Addressing Molecular Robot (DAMR), a DNA-based dynamic addressing device is developed which is capable of dynamically locating targets. DAMR is designed to first bind to the conserved region of the target while addressing the specific region dynamically until accurate recognition is achieved. DAMR has provided an approach for analyzing low-resolution targets and has been used for analyzing SNP of miR-196a2 in both cell and serum samples, which has opened new avenues for effective and efficient molecular recognition.

Indexed as

Polymorphism, Single NucleotideRoboticsDNAHumansMicroRNAsDNAMicroRNAsDNA frameworkmicroRNAmolecular recognitionmolecular robotsingle nucleotide polymorphism

Identifiers

PMID38884120
PMCPMC11336946

What OpenQuestion holds

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LicenceCC BY
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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.