Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

MicroRNAs01:22

MicroRNAs

23.9K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
23.9K
MicroRNAs01:22

MicroRNAs

3.8K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
3.8K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Aggregates Classification01:29

Aggregates Classification

970
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
970
Classification of Systems-II01:31

Classification of Systems-II

458
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
458
Classification of Systems-I01:26

Classification of Systems-I

552
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
552

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Novel aptamers targeting heparan sulfate for delivery of RNA therapeutics in Alzheimer's disease.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Identification of a Novel Disulfidptosis-Related Five-Gene Signature for Prognostic Prediction and Immune Characterization in Esophageal Cancer.

Biology·2026
Same author

SBM-Attention U-Net: A Hybrid Transformer Network for Liver Tumor Segmentation in Medical Images.

Sensors (Basel, Switzerland)·2026
Same author

Versatile Chitosan-Based Hydrogel Dressings for Multi-Scenario Wound Management.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Nanomaterial Engineered Biosensors and Stimulus-Responsive Platform for Emergency Monitoring and Intelligent Diagnosis.

Biosensors·2025
Same author

Hybrid nanomaterials-based biomedical phototheranostic platforms.

Progress in biomedical engineering (Bristol, England)·2025
Same journal

Heterojunction-Enhanced Interfacial Evanescent-Tunable Fiber Optic Probe for Amplification-free CRISPR/Cas12a-Based Rapid and Ultrasensitive Detection of MPXV.

Analytical chemistry·2026
Same journal

Tunable Charge Transfer in Europium Metal-Organic Frameworks for Ratiometric Sensing of a Sarin Simulant.

Analytical chemistry·2026
Same journal

A β-Cyclodextrin/Ag<sub>2</sub>O@MWCNT-Based Stochastic Platform for the Simultaneous Molecular Enantiorecognition and Enantioanalysis of Twelve Amino Acids in Biological Matrices.

Analytical chemistry·2026
Same journal

The ACS at 150: The History of Analytical Chemistry Publications and a Century of Progress.

Analytical chemistry·2026
Same journal

Machine Learning-Enabled Image Analysis of Complex Chemical Mixtures: Synthetic Urine Droplets as a Test System.

Analytical chemistry·2026
Same journal

H<sub>2</sub>O<sub>2</sub>/Viscosity Tandem-Locked Fluorescent Probes Based on an In Situ Fluorophore Synthesis Strategy for Colitis Imaging and Diagnosis.

Analytical chemistry·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.9K

Integrated Classifier Based on Coacervates for Weighted Digital miRNA Classification.

Wenyu Sun1, Liu Liu1, Xiaohui Liu1

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China.

Analytical Chemistry
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an integrated classifier based on coacervates (ICC) for microRNA (miRNA) classification. This novel DNA computing approach offers precise miRNA analysis for biomedical diagnostics.

More Related Videos

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

Published on: October 7, 2025

345
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Related Experiment Videos

Last Updated: Jan 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.9K
MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

Published on: October 7, 2025

345
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Area of Science:

  • Biotechnology
  • Molecular Computing
  • Biomedical Diagnostics

Background:

  • DNA molecular computing is a rapidly advancing field with applications in DNA storage and clinical diagnosis.
  • MicroRNA (miRNA) classification is crucial for various biomedical applications, including disease diagnosis.

Purpose of the Study:

  • To construct an integrated classifier based on coacervates (ICC) for accurate miRNA classification.
  • To leverage coacervates as a framework and microreactor for DNA-based computation.

Main Methods:

  • Utilized coacervates as a computational core and microreactor for miRNA analysis.
  • Developed a DNA classifier integrated within the coacervate structure.
  • Enabled local processing within coacervates to report on miRNA inputs.

Main Results:

  • The coacervate-based classifier demonstrated ordered classification of different miRNAs.
  • The system successfully implemented result weighting and performed arithmetic operations (multiplication, addition).
  • Coacervates acted as microreactors, confining miRNA inputs and preventing interference.

Conclusions:

  • The proposed integrated classifier based on coacervates (ICC) provides a novel platform for miRNA classification.
  • This technology shows significant potential for applications in biomedical diagnostics.
  • The coacervate framework enables precise and interference-free molecular computation for biological analysis.