Evidence map›Paper›PMID 39201637›Full record

ArticleInternational journal of molecular sciences2024

Combined Diagnostic Value of Hsa-miR-592 and Hsa-miR-9-3p in Plasma for Methamphetamine Addicts.

Wenbo Li, Diandian Liu, Xiaokun Liu, Yun Lu, Ludi Zhang, Feng Yu, Hailei Yu, Chunling Ma, Bin Cong, Di Wen and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Review
  3. 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

11 authors.

Wenbo LiHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.ORCID 0009-0000-6903-2594
Diandian LiuHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Xiaokun LiuHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Yun LuHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Ludi ZhangHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Feng YuHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Hailei YuHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Chunling MaHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Bin CongHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Di WenHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.
Bing XieHebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang 050017, China.ORCID 0009-0009-4014-4854

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A number of studies have reported that drug addiction is associated with microRNAs (miRNAs). However, the roles of plasma miRNAs in methamphetamine (METH) addicts have not been clearly explained. This study aimed to profile a panel of miRNAs as non-invasive predictive biomarkers and therapeutic targets for METH addiction. Differentially expressed miRNAs were derived from next-generation sequencing technology (NGS) and were validated by quantitative real-time PCR (RT-qPCR). The diagnostic value of specific altered miRNAs was evaluated by receiver operating characteristic (ROC) analysis and area under the curve (AUC). NGS results revealed that 63 miRNAs were significantly altered in the METH-exposed paradigm. The levels of hsa-miR-592, hsa-miR-9-3p, hsa-miR-206 and hsa-let-7b-3p were significantly elevated in the plasma of METH addicts. Hsa-miR-9-3p was a useful biomarker discriminating METH addicts from normal (AUC was 0.756). Importantly, combining detection of hsa-miR-592 and hsa-miR-9-3p achieved the highest AUC of 0.87, with a sensitivity and specificity of 82.7% and 78.9%, respectively. Target gene BDNF decreased significantly in METH addicts. Although METH addicts showed significant depressive symptoms, there was no correlation between the expression level of miR-592 and miR-9-3p and the degree of depression. Our findings suggested that hsa-miR-592, hsa-miR-9-3p, hsa-miR-206, and hsa-let-7b-3p may play a potential role in the pathology of METH addiction, and a combination of hsa-miR-592 and hsa-miR-9-3p could serve as potential peripheral biomarker and therapeutic target for METH addiction.

Indexed as

Amphetamine-Related DisordersBiomarkersMethamphetamineMicroRNAsAdultFemaleGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMaleROC CurveBiomarkersMethamphetamineMicroRNAsMIRN92 microRNA, humanMIRN9 microRNA, humanBDNFhsa-miR-592hsa-miR-9-3pMETHROC

Identifiers

PMID39201637
PMCPMC11354292

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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.