Evidence map›Paper›PMID 42120887›Full record

ArticleMicrosystems & nanoengineering2026

Single-cell impedance sensing on integrated circuit chip for fast tumor diagnosis.

Wenhao Hui, Lang Chen, Stephanie Andaluz, Yunlong Zhong, Xinru Huang, Ren Shen, Zhen Zhu, Hanbin Ma, Pui-In Mak, Rui Martins and 5 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

15 authors.

Wenhao Hui *State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China.
Lang Chen *School of Integrated Circuits, Peking University, Beijing, China.
Stephanie AndaluzState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China.
Yunlong ZhongDepartment of Hepatobiliary Surgery, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xinru HuangLiver Transplantation Center, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Ren ShenState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China.ORCID http://orcid.org/0000-0002-3503-6412
Zhen ZhuSoutheast University, School of Integrated Circuits, Key Laboratory of MEMS of Ministry of Education, Wuxi, China.ORCID http://orcid.org/0000-0001-8984-6798
Hanbin MaDepartment of Laboratory Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.ORCID http://orcid.org/0000-0002-7629-2287
Pui-In MakState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China.
Rui MartinsState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China.
Ping WangDepartment of Hepatobiliary Surgery, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. wangping1219@126.com.
Shuhong YiLiver Transplantation Center, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. yishuhong@163.com.ORCID http://orcid.org/0000-0003-1837-7837
Wei WangSchool of Integrated Circuits, Peking University, Beijing, China. w.wang@pku.edu.cn.ORCID http://orcid.org/0000-0002-5257-7675
Ka-Meng LeiState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China. kamenglei@um.edu.mo.
Yanwei JiaState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, SAR, China. yanweijia@um.edu.mo.ORCID http://orcid.org/0000-0001-5795-7450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and prompt tumor diagnosis plays a critical role in surgical decision-making to minimize damage to patients with benign tumors and maximize the cure rate of cancer patients. Although imaging, molecular testing, and histopathology assist in preoperative and postoperative tumor assessments, a fast, automated, and easy-to-operate method for intraoperative tumor diagnosis is lacking. Here, we introduce an innovative integrated circuit-based single-cell electric cell-substrate impedance sensing (IC-ECIS) platform capable of rapid intraoperative differentiation between cancerous and non-cancerous tumors. Fabricated on a semiconductor chip, this system captures ultra-weak single-cell impedance signals with exceptional sensitivity for single-cell discrimination for cancer risk evaluation. The implementation of a polymer-embedded silicon fan-out (P-eSiFO) packaging approach has reduced post-fabrication costs to under $1 per chip and allowed the microelectrodes batch processing on multi-project wafer chips. The diagnostic results can be obtained in 20 min without compromising cell viability. Clinical tumor samples from the thyroid, liver, gallbladder, bile duct, breast, and pancreas were tested on-chip to evaluate cancer risk. The on-chip diagnostic findings aligned with traditional histopathological analysis and flow cytometry results. This technology provides a fast, stable, automated, and low-sample-consumption diagnostic solution for tumors, paving the way for large-scale implementation of semiconductor-based diagnostic tools in precision oncology and personalized medicine.

Identifiers

PMID42120887
PMCPMC13168571

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.