ArticlePloS one2014
A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 68 citations in OpenAlex.
- Microfluidic Assays for CD4 T Lymphocyte Counting: A Review.Biosensors · 2025Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors.Bioengineering (Basel, Switzerland) · 2024Review
- Nanotherapeutic Heterogeneity: Sources, Effects, and Solutions.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Microfluidics on lensless, semiconductor optical image sensors: challenges and opportunities for democratization of biosensing at the micro-and nano-scale.Nanophotonics (Berlin, Germany) · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- High-Precision Lensless Microscope on a Chip Based on In-Line Holographic Imaging.Sensors (Basel, Switzerland) · 2021Article
- Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.PloS one · 2021Article
- Machine learning-enabled multiplexed microfluidic sensors.Biomicrofluidics · 2020Review
- Is microfluidics the "assembly line" for CRISPR-Cas9 gene-editing?Biomicrofluidics · 2020Article
- Capillary-Driven Flow Microfluidics Combined with Smartphone Detection: An Emerging Tool for Point-of-Care Diagnostics.Diagnostics (Basel, Switzerland) · 2020Review
- Design and characterization of a package-less hybrid PDMS-CMOS-FR4 contact-imaging system for microfluidic integration.Journal of micro/nanolithography, MEMS, and MOEMS : JM3 · 2018Article
- Microfluidic flow cytometry: The role of microfabrication methodologies, performance and functional specification.Technology · 2018Article
- A Numerical Model of Blood Flow Velocity Measurement Based on Finger Ring.Journal of healthcare engineering · 2018Article
- An on-chip instrument for white blood cells classification based on a lens-less shadow imaging technique.PloS one · 2017Article
- Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.Sensors (Basel, Switzerland) · 2016Article
- Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution.Light, science & applications · 2015Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.
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.