ArticleMicrosystems & nanoengineering2019
Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning.
Article in Microsystems & nanoengineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05090319 (Use of a Micro-hematology Analyzer for Discriminating Between Viral and Bacterial Infections in Hospitalized Adults), which is not on this map. Cited by 28 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.
Use of a Micro-hematology Analyzer for Discriminating Between Viral and Bacterial Infections in Hospitalized Adults: An Observational Study
Who cites it
28 citing papers in PubMed, 71 citations in OpenAlex.
- Event-Preserving Neural Network Denoising for Impedance Flow Cytometry.Nanomaterials (Basel, Switzerland) · 2026Article
- Article
- Therapeutic Drug Monitoring and Point-of-Care Technologies: Opportunities and Current Challenges.Therapeutic drug monitoring · 2026Review
- Allele-specific electrical genotyping for diagnosis of transthyretin amyloidosis.Communications engineering · 2025Article
- Integrating optical and electrical sensing with machine learning for advanced particle characterization.Biomedical microdevices · 2024Article
- Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry.Biomicrofluidics · 2024Article
- 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
- Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment.Biosensors & bioelectronics · 2023Article
- Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.Sensors (Basel, Switzerland) · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Machine learning at the edge for AI-enabled multiplexed pathogen detection.Scientific reports · 2023Article
- Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning.Biosensors · 2023Article
- Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- A Review on Microfluidics-Based Impedance Biosensors.Biosensors · 2023Review
- Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry.Lab on a chip · 2022Article
- Review
- Deep learning for non-parameterized MEMS structural design.Microsystems & nanoengineering · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Frequency-Time Domain (FTD) Impedance Data Analysis to Improve Accuracy of Microparticle Enumeration in a Microfluidic Electronic Counter.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 μL of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.
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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.