Evidence map›Paper›PMID 40454326›Full record

ArticleBiomicrofluidics2025

Cyber-physical security of biochips: A perspective.

Navajit Singh Baban, Sukanta Bhattacharjee, Yong-Ak Song, Krishnendu Chakrabarty, Ramesh Karri

Abstract read
In one paragraph

Article in Biomicrofluidics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Navajit Singh BabanDivision of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0001-5804-0070
Sukanta BhattacharjeeDepartment of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India.ORCID https://orcid.org/0000-0001-8660-4574
Yong-Ak SongDivision of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0001-8066-2933
Krishnendu ChakrabartySchool of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.ORCID https://orcid.org/0000-0003-4475-6435
Ramesh KarriDepartment of Electrical and Computer Engineering, New York University, New York, New York 85287, USA.ORCID https://orcid.org/0000-0001-7989-5617

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microfluidic biochips (MBs) are transforming diagnostics, healthcare, and biomedical research. However, their rapid deployment has exposed them to diverse security threats, including structural tampering, material degradation, sample-level interference, and intellectual property (IP) theft, such as counterfeiting, overbuilding, and piracy. This perspective highlights emerging attack vectors and countermeasures aimed at mitigating these risks. Structural attacks, such as stealthy design code modifications, can result in faulty diagnostics. To address this, deep learning -based anomaly detection leverages microstructural changes, including optical changes such as shadows or reflections, to identify and resolve faults. Material-level countermeasures, including mechano-responsive dyes and spectrometric watermarking, safeguard against subtle chemical alterations during fabrication. Sample-level protections, such as molecular barcoding, ensure bio-sample integrity by embedding unique DNA sequences for authentication. At the IP level, techniques like watermarking, physically unclonable functions, fingerprinting, and obfuscation schemes provide robust defenses against reverse engineering and counterfeiting. Together, these approaches offer a multi-layered security framework to protect MBs, ensuring their reliability, safety, and trustworthiness in critical applications.

Identifiers

PMID40454326
PMCPMC12124908

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.