ReviewNon-coding RNA2025
The Emerging Applications of Artificial MicroRNA-Mediated Gene Silencing in Plant Biotechnology.
Review in Non-coding RNA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Hidden conversations beneath the soil: microRNA and hormone control of root symbioses.Archives of microbiology · 2026Review
- Artificial MicroRNA delivery and gene silencing via extracellular vesicles derived from molecularly modified tobacco.Protoplasma · 2026Article
- Exploring the CeRNA landscape in plants: advances, methods, and challenges.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Small RNAs big impact: a review on microRNA-mediated tolerance in wheat under terminal heat.Functional & integrative genomics · 2025Review
- miRNA-Mediated Regulation ofInternational journal of molecular sciences · 2025Article
- The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials.Plants (Basel, Switzerland) · 2025Review
- RNA silencing: the future potential strategy for engineering virus resistance in plants.Physiology and molecular biology of plants : an international journal of functional plant biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Improving crop yield potential is crucial to meet the increasing demands of a rapidly expanding global population in an ever-changing and challenging environment. Therefore, different technological approaches have been proposed over the last decades to accelerate plant breeding. Among them, artificial microRNAs (amiRNAs) represent an innovative tool with remarkable potential to assist plant improvement. MicroRNAs (miRNAs) are a group of endogenous, small (20-24 nucleotides), non-coding RNA molecules that play a crucial role in gene regulation. They are associated with most biological processes of a plant, including reproduction, development, cell differentiation, biotic and abiotic stress responses, metabolism, and plant architecture. In this context, amiRNAs are synthetic molecules engineered to mimic the structure and function of endogenous miRNAs, allowing for the targeted silencing of specific nucleic acids. The current review explores the diverse applications of amiRNAs in plant biology and agriculture, such as the management of infectious agents and pests, the engineering of plant metabolism, and the enhancement of plant resilience to abiotic stress. Moreover, we address future perspectives on plant amiRNA-based gene silencing strategies, highlighting the need for further research to fully comprehend the potential of this technology and to translate its scope toward the widespread adoption of amiRNA-based strategies for plant breeding.
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.