ArticleBiosensors2023
Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 21 citations in OpenAlex.
- Magnetic induction spectroscopy (MIS) as potential non-contact label-free technique for DNA amplification follow-up.Journal of electrical bioimpedance · 2026Article
- A label-free nanowell-based impedance sensor for ten-minute SARS-CoV-2 detection.Sensors & diagnostics · 2025Article
- Allele-specific electrical genotyping for diagnosis of transthyretin amyloidosis.Communications engineering · 2025Article
- Microfluidic Assays for CD4 T Lymphocyte Counting: A Review.Biosensors · 2025Review
- Integrating optical and electrical sensing with machine learning for advanced particle characterization.Biomedical microdevices · 2024Article
- Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer.Cancer cell international · 2024Article
- Microscopic Imaging to Visualize the Distribution of Dietary Nucleic Acids in Food Products of Various Origins.Foods (Basel, Switzerland) · 2023Article
- Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.Biosensors · 2023Review
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
- Advances in Simple, Rapid, and Contamination-Free Instantaneous Nucleic Acid Devices for Pathogen Detection.Biosensors · 2023Review
- A Short Review on Miniaturized Biosensors for the Detection of Nucleic Acid Biomarkers.Biosensors · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.
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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.