Evidence map›Paper›PMID 42120555›Full record

ReviewMolecular psychiatry2026

Revolutionizing MDD diagnosis: the integrated role of circRNAs and biosensor technology.

Wangang Zhu, Yuchen Wang, Chenghui Yu, Nuno Pires, Zhaochu Yang, Haakon Karlsen, Weixuan Jing, Honghong Yao, Bing Han, Jinsong Ouyang and 5 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular psychiatry, 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

15 authors.

Wangang Zhu *School of Instrument Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Yuchen Wang *School of Instrument Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Chenghui YuChongqing Key Laboratory of Micro-Nano Transduction and Intelligent Microsystem, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-NanoSystems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Nuno PiresChongqing Key Laboratory of Micro-Nano Transduction and Intelligent Microsystem, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-NanoSystems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Zhaochu YangChongqing Key Laboratory of Micro-Nano Transduction and Intelligent Microsystem, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-NanoSystems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Haakon KarlsenChongqing Key Laboratory of Micro-Nano Transduction and Intelligent Microsystem, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-NanoSystems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Weixuan JingSchool of Instrument Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Honghong YaoDepartment of Pharmacology, School of Medicine, Southeast University, Nanjing, 210009, China.
Bing HanDepartment of Pharmacology, School of Medicine, Southeast University, Nanjing, 210009, China.
Jinsong OuyangInstrumentation Technology and Economy Institute, Beijing, 96500, China.
Minjie ZhuInstrumentation Technology and Economy Institute, Beijing, 96500, China.
Jose Higino CorreiaDepartment of Industrial Electronics, University of Minho, Campus Azurem, 4800, Guimaraes, Portugal.
Danilo DemarchiDepartment of Electronics and Telecommunications, Politecnico di Torino, Torino, 10129, Italy.
Zhuangde JiangSchool of Instrument Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Tao DongSchool of Instrument Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China. tao.dong@xjtu.edu.cn.ORCID http://orcid.org/0000-0002-6229-1013

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Major Depressive Disorder (MDD) is a globally widespread mental health disorder that frequently remains underdiagnosed and inadequately treated. Recent advancements in circular RNAs (circRNAs) have illuminated their potential as biomarkers for a variety of diseases, including MDD. This review emphasizes the advantages of circRNA enrichment methodologies over traditional techniques, particularly isotachophoresis. Furthermore, the intricate role of circRNAs in the pathophysiological processes underlying MDD, as well as their integration with biosensor technology to improve diagnostic accuracy and efficiency, are synthesised. However, the clinical translation of circRNA-based diagnostics faces significant challenges, including the low abundance of circRNAs in bodily fluids, the need for highly sensitive and rapid detection platforms, and the lack of standardized, point-of-care compatible methods. A comprehensive overview of current circRNA detection methods, delineating their similarities and differences, is discussed. Insights for the anticipated advancements in quantitative and rapid circRNA detection is proposed. This review not only presents a thorough assessment of emerging trends in circRNA detection but also elaborates on primary techniques, traditional approaches, and recent innovations within the field of biosensor-based MDD diagnostics.

Indexed as

Biosensing TechniquesMajor Depressive DisorderRNA, CircularBiomarkersHumansBiomarkersRNA, Circular

Identifiers

PMID42120555

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.