Evidence map›Paper›PMID 39957820›Full record

ReviewRSC advances2025

Emerging trends in the cystatin C sensing technologies: towards better chronic kidney disease management.

Jeethu Raveendran, Dhanya Gangadharan, Jagadeesh Bayry, P Abdul Rasheed

Abstract readReview
In one paragraph

Review in RSC advances, 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. Article
  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.

Jeethu RaveendranDepartment of Biological Sciences and Engineering, Indian Institute of Technology Palakkad Palakkad Kerala 678623 India abdulrasheed@iitpkd.ac.in.
Dhanya GangadharanDepartment of Biotechnology, Sahrdaya College of Engineering and Technology Thrissur 684002 Kerala India.
Jagadeesh BayryDepartment of Biological Sciences and Engineering, Indian Institute of Technology Palakkad Palakkad Kerala 678623 India abdulrasheed@iitpkd.ac.in.ORCID https://orcid.org/0000-0003-0498-9808
P Abdul RasheedDepartment of Biological Sciences and Engineering, Indian Institute of Technology Palakkad Palakkad Kerala 678623 India abdulrasheed@iitpkd.ac.in.ORCID https://orcid.org/0000-0002-3969-0163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cystatin C (CysC), a protein, has replaced creatinine as a biomarker of kidney function and other diseases and has led to a surge in the research on the development of efficient CysC biosensors. The current CysC sensing technologies are remarkable in terms of selectivity and reproducibility. However, the complexity, cost, and space requirements of these methods render them unsuitable for real-time monitoring or point-of-care (PoC) implementations in healthcare settings. This review discusses the most recent developments in the field of CysC biosensing and to the best of our knowledge, this is the first focused review exclusively on CysC biosensing modalities. Our goal is to provide a thorough overview of the current state of CysC biosensors, and presenting mechanisms related to biosensor recognition and transduction. The review starts with clinical significance of CysC detection followed by detailed analysis of different CysC biosensing methods with emphasis on the necessity of PoC monitoring of CysC. We have also highlighted current challenges and an outlook on future perspectives. We anticipate that this study will play a key role in the understanding the working principle of CysC sensors and will aid in the designing of new efficient sensing modalities for the detection of CysC.

Identifiers

PMID39957820
PMCPMC11826153

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.