Evidence map›Paper›PMID 41977285›Full record

ReviewInternational journal of molecular sciences2026

The Regulatory Potential of Long Non-Coding RNAs in Bipolar Disorder.

Siqi Li, Yuhan Fu, Zhenzhen Wang, Yan Zhang, Tao Sun, Nan Miao

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 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

6 authors.

Siqi LiCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.ORCID 0009-0002-5999-6239
Yuhan FuCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.
Zhenzhen WangCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.
Yan ZhangCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.
Tao SunCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.ORCID 0000-0003-2969-2226
Nan MiaoCenter for Precision Medicine, School of Medicine, Huaqiao University, Xiamen 361021, China.ORCID 0000-0003-3818-985X

Funding

Fundamental Research Funds for the Central Universities ZQN-1020, N.M.National Natural Science Foundation of China National Natural Science Foundation of ChinaNatural Science Foundation of Fujian Province, China 2025J01170, N.M.Scientific Research Funds of Huaqiao University 16BS815 (N.M.), 19BS303 (N.M.)Scientific Research Funds of Huaqiao University Z16Y0017(T.S.)Youth Innovation Foundation of Xiamen 3502Z202571029, N.M.
6 · The paper itself

Abstract

Bipolar disorder (BD) is characterized by mood swings between mania and depression, sharing overlapping symptomatic and genetic risk factors with other mood disorders. Long non-coding RNAs (lncRNAs) show specific spatiotemporal precision in distinct cell types in the human brain, and understanding the precise mechanisms of lncRNAs in mood switching in BD is fundamental to deciphering the key molecular networks underlying BD diagnosis and therapy. In this review, we summarize the classification of BD subtypes, the differences between BD and multiple mood disorders, and the functional potential of lncRNAs in BD. Future studies of these lncRNAs will facilitate the development of RNA-based diagnosis for BD.

Indexed as

Bipolar DisorderRNA, Long NoncodingAnimalsBrainGene Expression RegulationHumansRNA, Long NoncodingbipolarlncRNAsmood disorderneural system

Identifiers

PMID41977285
PMCPMC13073052

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.