Evidence map›Paper›PMID 39597132›Full record

ReviewMicromachines2024

CMOS Point-of-Care Diagnostics Technologies: Recent Advances and Future Prospects.

Tania Moeinfard, Ebrahim Ghafar-Zadeh, Sebastian Magierowski

Abstract readReview
In one paragraph

Review in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. SUBTRACTIVE NANOFLUIDICS IN 65-NM CMOS COPPER BEOL ACHIEVE 100-NM WIDTH.Proceedings. IEEE International Conference on Micro Electro Mechanical Systems · 2026
    Article
  6. 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

3 authors.

Tania MoeinfardDepartment of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.ORCID 0009-0003-3253-2845
Ebrahim Ghafar-ZadehDepartment of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.ORCID 0000-0002-7090-2304
Sebastian MagierowskiDepartment of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review provides a comprehensive overview of point-of-care (PoC) devices across several key diagnostic applications, including blood analysis, infectious disease detection, neural interfaces, and commercialized integrated circuits (ICs). In the blood analysis section, the focus is on biomarkers such as glucose, dopamine, and aptamers, and their respective detection techniques. The infectious disease section explores PoC technologies for detecting pathogens, RNA, and DNA, highlighting innovations in molecular diagnostics. The neural interface section reviews advancements in neural recording and stimulation for therapeutic applications. Finally, a survey of commercialized ICs from companies such as Abbott and Medtronic is presented, showcasing existing PoC devices already in widespread clinical use. This review emphasizes the role of complementary metal-oxide-semiconductor (CMOS) technology in enabling compact, efficient diagnostic systems and offers insights into the current and future landscape of PoC devices.

Indexed as

bioimpedance spectroscopybiomedical integrated circuits (ICs)CMOS technologyglucose monitoringmedical diagnosticsneural recordingpoint-of-care (PoC)portable devicesreal-time monitoringSARS-CoV-2 diagnostics

Identifiers

PMID39597132
PMCPMC11596111

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.