Evidence map›Paper›PMID 40735651›Full record

ReviewBiotechnology reports (Amsterdam, Netherlands)2025

Enhancing cell characterization with microfluidics and AI: a comprehensive review of mechanical, electrical, and hybrid techniques.

Marwan Taha, Anna-Maria Pappa, Hani Saleh, Anas Alazzam

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biotechnology reports (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marwan TahaDepartment of Mechanical and Nuclear Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.
Anna-Maria PappaDepartment of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
Hani SalehComputer & Communication Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
Anas AlazzamDepartment of Mechanical and Nuclear Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper examines recent advancements in cell characterization using microfluidic devices, emphasizing mechanical, electrical, and hybrid methodologies. These technologies have substantially improved throughput, precision, and the range of cell types they can analyze. Key microfluidic technologies for cell characterization are reviewed, including label-free electrical and mechanical methods designed for high-throughput, real-time analysis. Microfluidic advancements in cell characterization are critically assessed, along with challenges such as operational complexity and the need for more adaptable, user-friendly platforms. The integration of AI and machine learning in microfluidic systems is also discussed, highlighting their crucial role in automating data analysis and enhancing classification accuracy, with implications for personalized medicine and advanced cellular assays.

Indexed as

Artificial intelligence (AI)Cell characterizationHigh-throughput analysisLabel-free techniquesMachine learningMicrofluidicsPersonalized medicine

Identifiers

PMID40735651
PMCPMC12305219

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.