Evidence map›Paper›PMID 40747138›Full record

ReviewBiomicrofluidics2024

Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers.

Sanjida Yeasmin, Li-Jing Cheng

Abstract readReview
In one paragraph

Review in Biomicrofluidics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

2 authors.

Sanjida YeasminSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, USA.ORCID https://orcid.org/0000-0002-0177-6674
Li-Jing ChengSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, USA.ORCID https://orcid.org/0000-0002-1386-9368

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecularly imprinted polymers (MIPs), functioning as artificial bioreceptors, hold significant promise for biomarker detection in healthcare, disease diagnosis, and addressing drug abuse. In contrast to natural bioreceptors, MIP-based sensors offer numerous advantages, such as high stability, cost-effectiveness, high selectivity, sensitivity, and notably straightforward preparation with customizable binding sites for diverse targets. Conventional MIP sensors often necessitate external redox reagents in analytes to transduce binding events into electrochemical signals for indirect detection, presenting challenges for practical applications in wearables or point-of-care (POC) testing. Redox-active MIP sensors have emerged as a viable alternative, enabling direct and label-free electrochemical detection, with two types developed. The first type utilizes electrocatalytic materials to expedite electron transfer and facilitate a redox reaction between the captured electroactive target and the electrode. The second type incorporates an embedded redox reactive component that allows selective binding of a target to modulate its electron transfer, leading to a change in the electrical signal. This review covers emerging trends and challenges in redox-active MIP sensors for direct electrochemical detection of biomarkers, focusing on sensing mechanisms, synthesis methods, and applications. Additionally, recent progress in wearable and POC redox-active MIP sensors is highlighted. A comprehensive outlook of challenges is further provided, aiming to advance direct biomarker detection for diverse healthcare applications.

Identifiers

PMID40747138
PMCPMC12313330

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.