ArticleNature communications2022
Fast custom wavelet analysis technique for single molecule detection and identification.
Article in Nature communications, 2022. 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
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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.
- Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry.Nature communications · 2024Article
- Dual optofluidic distributed feedback dye lasers for multiplexed biosensing applications.Scientific reports · 2023Article
- Adaptive time modulation technique for multiplexed on-chip particle detection across scales.Optica · 2023Article
- Machine learning at the edge for AI-enabled multiplexed pathogen detection.Scientific reports · 2023Article
- All-in-One Optofluidic Chip for Molecular Biosensing Assays.Biosensors · 2022Article
- Fast custom wavelet analysis technique for single molecule detection and identification.Nature communications · 2022Article
- Automated Optofluidic Analysis of Single Extracellular Vesicles for Organoid Culture Monitoring.IEEE journal of selected topics in quantum electronics : a publication of the IEEE Lasers and Electro-optics SocietyArticle
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.
Funding
Abstract
Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.
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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.