Recent advances in non-optical microfluidic platforms for bioparticle detection DOI Creative Commons

Bayinqiaoge,

Yuxin Zhang, Tim Cole

et al.

Biosensors and Bioelectronics, Journal Year: 2022, Volume and Issue: 222, P. 114944 - 114944

Published: Nov. 30, 2022

The effective analysis of the basic structure and functional information bioparticles are great significance for early diagnosis diseases. synergism between microfluidics particle manipulation/detection technologies offers enhanced system integration capability test accuracy detection various bioparticles. Most microfluidic platforms based on optical strategies such as fluorescence, absorbance, image recognition. Although have proven their capabilities in practical clinical bioparticles, shortcomings expensive components whole bulky devices limited practicality development point-of-care testing (POCT) systems to be used remote underdeveloped areas. Therefore, there is an urgent need develop cost-effective non-optical bioparticle that can act alternatives counterparts. In this review, we first briefly summarise passive active methods manipulation microfluidics. Then, survey latest progress electrical, magnetic, acoustic techniques detection. Finally, a perspective offered, clarifying challenges faced by current developing POCT applications.

Language: Английский

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications DOI Open Access
Jianhua Zhou, Jianpei Dong, Hongwei Hou

et al.

Lab on a Chip, Journal Year: 2024, Volume and Issue: 24(5), P. 1307 - 1326

Published: Jan. 1, 2024

This review outlines the current advances of high-throughput microfluidic systems accelerated by AI. Furthermore, challenges and opportunities in this field are critically discussed as well.

Language: Английский

Citations

20
Impedance‐Based Multimodal Electrical‐Mechanical Intrinsic Flow Cytometry DOI
Yongxiang Feng,

Junwen Zhu,

Huichao Chai

et al.

Small, Journal Year: 2023, Volume and Issue: 19(45)

Published: July 12, 2023

Abstract Reflecting various physiological states and phenotypes of single cells, intrinsic biophysical characteristics (e.g., mechanical electrical properties) are reliable important, label‐free biomarkers for characterizing cells. However, single‐modal or properties alone not specific enough to characterize cells accurately, it has been long challenging couple the conventionally image‐based characterization impedance‐based characterization. In this work, spatial‐temporal impedance sensing signal leveraged, an multimodal electrical‐mechanical flow cytometry framework on‐the‐fly high‐dimensional measurement is proposed, that is, Young's modulus E , fluidity β radius r cytoplasm conductivity σ i membrane capacitance C sm With characterization, can better reveal difference in cell types, demonstrated by experimental results with three types cancer (HepG2, MCF‐7, MDA‐MB‐468) 93.4% classification accuracy pharmacological perturbations cytoskeleton (fixed Cytochalasin B treated cells) 95.1% accuracy. It envisioned provides a new perspective accurate single‐cell

Language: Английский

Citations

32
Microsystem Advances through Integration with Artificial Intelligence DOI Creative Commons
Hsieh‐Fu Tsai, Soumyajit Podder, Pin‐Yuan Chen

et al.

Micromachines, Journal Year: 2023, Volume and Issue: 14(4), P. 826 - 826

Published: April 8, 2023

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale volume, typically on the of micro- or nanoliters. Under larger surface-to-volume ratio, advantages low reagent consumption, faster reaction kinetics, more compact systems are evident in microfluidics. However, miniaturization microfluidic chips introduces challenges stricter tolerances designing controlling them for interdisciplinary applications. Recent advances artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, optimization bioanalysis data analytics. In microfluidics, Navier-Stokes equations, which partial differential equations describing viscous fluid motion complete form known not general analytical solution, can be simplified fair performance through numerical approximation due inertia laminar flow. Approximation using neural networks trained by rules physical knowledge new possibility predict physicochemical nature. The combination automation produce large amounts data, where features patterns difficult discern human extracted machine learning. Therefore, integration with AI potential revolutionize workflow enabling precision control analysis. Deployment smart may tremendously beneficial various applications future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), personalized medicine. this review, we summarize key integrated discuss outlook possibilities combining

Language: Английский

Citations

24
Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis DOI Creative Commons
Mahtab Kokabi, Muhammad Nabeel Tahir, Darshan Singh

et al.

Biosensors, Journal Year: 2023, Volume and Issue: 13(9), P. 884 - 884

Published: Sept. 13, 2023

Cancer is a fatal disease and significant cause of millions deaths. Traditional methods for cancer detection often have limitations in identifying the its early stages, they can be expensive time-consuming. Since typically lacks symptoms only detected at advanced it crucial to use affordable technologies that provide quick results point care diagnosis. Biosensors target specific biomarkers associated with different types offer an alternative diagnostic approach care. Recent advancements manufacturing design enabled miniaturization cost reduction point-of-care devices, making them practical diagnosing various diseases. Furthermore, machine learning (ML) algorithms been employed analyze sensor data extract valuable information through statistical techniques. In this review paper, we details on how contribute ongoing development processing techniques biosensors, which are continually emerging. We also used along comparison performance ML sensing modalities terms classification accuracy.

Language: Английский

Citations

24
AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery DOI
Junchi Liu, Hanze Du, Lei Huang

et al.

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(30), P. 38832 - 38851

Published: July 17, 2024

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient processing, etc. Microfluidics coupled with AI is poised revolutionize landscape discovery. By integrating advanced platforms algorithms, researchers can rapidly screen large libraries compounds, identify novel candidates, and elucidate complex pathways unprecedented speed efficiency. This review provides an overview advances challenges AI-based microfluidics their applications We discuss synergistic combination high-throughput AI-driven analysis phenotype characterization, drug-target interactions, modeling. In addition, we highlight potential AI-powered achieve automated system. Overall, represents promising approach shaping future by enabling rapid, cost-effective, accurate identification therapeutically relevant compounds.

Language: Английский

Citations

11
Tutorial on impedance and dielectric spectroscopy for single-cell characterisation on microfluidic platforms: theory, practice, and recent advances DOI Creative Commons
Fatemeh Dadkhah Tehrani, Michael O’Toole, David J. Collins

et al.

Lab on a Chip, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Integration of low-frequency electrical impedance and broadband electromagnetic sensing with microfluidic devices enables high-throughput analysis cell size, membrane properties, intracellular characteristics.

Language: Английский

Citations

1
Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning DOI Creative Commons
Mahtab Kokabi,

Jianye Sui,

Neeru Gandotra

et al.

Biosensors, Journal Year: 2023, Volume and Issue: 13(3), P. 316 - 316

Published: Feb. 24, 2023

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis molecular diagnostics. Given the need for testing DNA amounts and its purity many samples, including samples very small input DNA, there utility of novel machine learning approaches accurate high-throughput quantification. Here, we demonstrated ability neural network predict coupled paramagnetic beads. To this end, custom-made microfluidic chip applied detect molecules bound beads by measuring impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements frequency imaginary real parts intensity within channel as deep models concentration. Specifically, 10 different architectures are examined. The results proposed regression model indicate that R_Squared 97% slope 0.68 achievable. Consequently, can be suitable, fast, method measure concentration sample. presented study demonstrate use information embedded raw data amount

Language: Английский

Citations

15
Machine learning empowered multi-stress level electromechanical phenotyping for high-dimensional single cell analysis DOI
Minhui Liang, Qiang Tang, Jianwei Zhong

et al.

Biosensors and Bioelectronics, Journal Year: 2023, Volume and Issue: 225, P. 115086 - 115086

Published: Jan. 19, 2023

Language: Английский

Citations

13
Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment DOI Creative Commons

Armita Salahi,

Carlos Honrado, John H. Moore

et al.

Biosensors and Bioelectronics, Journal Year: 2023, Volume and Issue: 231, P. 115262 - 115262

Published: March 30, 2023

Language: Английский

Citations

13
Evaluating the Accuracy of Impedance Flow Cytometry with Cell-Sized Liposomes DOI
Huichao Chai, Yongxiang Feng,

Junwen Zhu

et al.

ACS Sensors, Journal Year: 2023, Volume and Issue: 8(7), P. 2681 - 2690

Published: June 22, 2023

Electrical properties of single cells are important label-free biomarkers disease and immunity. At present, impedance flow cytometry (IFC) provides means for high throughput characterization single-cell electrical properties. However, the accuracy spherical single-shell model widely used in IFC has not been well evaluated due to lack reliable reproducible particles with true-value parameters as benchmarks. Herein, a method is proposed evaluate cell-sized unilamellar liposomes synthesized through double emulsion droplet microfluidics. The influence three key dimension (i.e., measurement channel width w, height h, electrode gap g) were experiment. It was found that relative error intrinsic measured by less than 10% when size sensing zone close particles. further reveals h greatest on accuracy, maximum can reach ∼30%. Error caused g slightly larger w. This solid guideline design system. envisioned this advance improvement accurate cells.

Language: Английский

Citations

12