ReviewMethods in molecular biology (Clifton, N.J.)2022
Experimental MicroRNA Detection Methods.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- miRquad: first-in-class dPCR multiplex TaqMan™ Advanced clinical research assay for microRNA detection in head and neck cancer.Journal of experimental & clinical cancer research : CR · 2025Article
- Emerging biomarkers for early cancer detection and diagnosis: challenges, innovations, and clinical perspectives.European journal of medical research · 2025Review
- MicroRNA Gets a Mighty Award.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Extracellular Vesicle microRNA: A Promising Biomarker and Therapeutic Target for Respiratory Diseases.International journal of molecular sciences · 2024Review
- Circulating microRNAs as Potential Biomarkers in Pancreatic Cancer-Advances and Challenges.International journal of molecular sciences · 2023Review
- The Role of microRNAs in Epigenetic Regulation of Signaling Pathways in Neurological Pathologies.International journal of molecular sciences · 2023Review
- Carbon-Based Fluorescent Nano-Biosensors for the Detection of Cell-Free Circulating MicroRNAs.Biosensors · 2023Review
- Micro-RNAs in Human Placenta: Tiny Molecules, Immense Power.Molecules (Basel, Switzerland) · 2022Review
- Noncoding RNAs in apoptosis: identification and function.Turkish journal of biology = Turk biyoloji dergisi · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are considerably small yet highly important riboregulators involved in nearly all cellular processes. Due to their critical roles in posttranscriptional regulation of gene expression, they have the potential to be used as biomarkers in addition to their use as drug targets. Although computational approaches speed up the initial genomewide identification of putative miRNAs, experimental approaches are essential for further validation and functional analyses of differentially expressed miRNAs. Therefore, sensitive, specific, and cost-effective microRNA detection methods are imperative for both individual and multiplex analysis of miRNA expression in different tissues and during different developmental stages. There are a number of well-established miRNA detection methods that can be exploited depending on the comprehensiveness of the study (individual miRNA versus multiplex analysis), the availability of the sample and the location and intracellular concentration of miRNAs. This review aims to highlight not only traditional but also novel strategies that are widely used in experimental identification and quantification of microRNAs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.