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

Label‐Free Impedance Analysis of Induced Pluripotent Stem Cell‐Derived Spinal Cord Progenitor Cells for Rapid Safety and Efficacy Profiling DOI
Linwei He, Jerome Tan, Shi‐Yan Ng

et al.

Advanced Materials Technologies, Journal Year: 2024, Volume and Issue: 9(20)

Published: July 5, 2024

Abstract Regenerative therapies, including the transplantation of spinal cord progenitor cells (SCPCs) derived from induced pluripotent stem (iPSCs), are promising treatment strategies for injuries. However, risk tumorigenicity residual iPSCs advocates an unmet need rapid SCPCs safety profiling. Herein, a (≈3000 min ‐1 ) electrical‐based microfluidic biophysical cytometer is reported to detect low‐abundance at single‐cell resolution. Based on multifrequency impedance measurements (0.3 12 MHz), features cell size, deformability, membrane, and nucleus dielectric properties simultaneously quantified as hydrodynamically stretched cross junction under continuous flow. A supervised uniform manifold approximation projection (UMAP) model further developed impedance‐based quantification undifferentiated with high sensitivity (≈1% spiked iPSCs) shows good correlations differentiation outcomes using two iPSC lines. Cell membrane opacity (day 1) also identified novel early intrinsic predictive biomarker that exhibits strong correlation SCPC efficiency 10). Overall, it envisioned this label‐free optic‐free platform technology can be versatile cost‐effective process analytical tool monitor or assess quality in regenerative medicine.

Language: Английский

Citations

2
Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital DOI Creative Commons
Yuan Wang,

Chenbi Li,

Zeheng Wang

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 3428 - 3436

Published: Jan. 1, 2024

In this research, we address the urgent need for accurate prediction of in-hospital survival periods patients diagnosed with pancreatic cancer (PC), a disease notorious its late-stage diagnosis and dismal rates.Utilizing machine learning (ML) technologies, focus on application Variational Autoencoders (VAE) data augmentation ensemble techniques enhancing predictive accuracy.Our dataset comprises biochemical blood test (BBT) results from stage II/III PC patients, which is limited in size, making VAE's capability particularly valuable.The study employs several ML models, including Elastic Net (EN), Decision Trees (DT), Radial Basis Function Support Vector Machine (RBF-SVM), evaluates their performance using metrics such as Mean Absolute Error (MAE) Squared (MSE).Our findings reveal that EN, DT, RBF-SVM are most effective models within VAE-augmented framework, showing substantial improvements accuracy.An approach further optimized results, reducing MAE to approximately 10 days.These advancements hold significant implications field precision medicine, enabling more targeted therapeutic interventions optimizing healthcare resource allocation.The can also serve foundational step towards personalized solutions patients.

Language: Английский

Citations

2
Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry DOI Open Access
Jian Wei, W. Gao, Xinlong Yang

et al.

Biomicrofluidics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Jan. 1, 2024

Mitosis is a crucial biological process where parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit mitosis, for instance, the anti-cancer interacting with tumor proliferation leading to mitosis arrest at specific phase or death eventually. Combining machine learning microfluidic impedance flow cytometry (IFC) offers concise way label-free high-throughput classification of drug-treated cells single-cell level. IFC-based analysis generates large amount data related electrophysiology parameters, helps establish correlations between these states. This work demonstrates application state classification, including binary differentiations G1/S apoptosis states G2/M states, as well three subpopulations comprising subgroup insensitive drug beyond drug-induced apoptosis. The amplitudes used input features model training were extracted from IFC-measured datasets deep neural network (DNN) was exploited here structure (e.g., hidden layer number neuron in each layer) optimized given type drug. For H1650 cells, we obtained an accuracy 78.51% 82.55% HeLa achieved high 96.94% both which induced by taxol treatment. Even higher approaching 100% vinblastine-treated differentiation viable non-viable We also demonstrate capability DNN high-accuracy complete sample treated vinblastine.

Language: Английский

Citations

2
Label-free high-throughput impedance-activated cell sorting DOI

Kui Zhang,

Ziyang Xia,

Yiming Wang

et al.

Lab on a Chip, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

A label-free high-throughput impedance-activated cell sorting platform can sort cells at a throughput of 1000 events per s.

Language: Английский

Citations

2
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

Citations

10