Evidence map›Paper›PMID 38222505›Full record

ReviewACS omega2024

Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase.

Daisy Mehta, Divyani Gupta, Alankar Kafle, Sukhjot Kaur, Tharamani C Nagaiah

Open access · goldAbstract readReview
In one paragraph

Review in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.8field-weighted citation impact, top 4% of its field
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

9 citing papers in PubMed, 31 citations in OpenAlex.

  1. Review
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  6. Petal-like g-CFrontiers in bioengineering and biotechnology · 2026
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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 at 1 institution in 1 country.

Daisy MehtaDepartment of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Divyani GuptaDepartment of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Alankar KafleDepartment of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Sukhjot KaurDepartment of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Tharamani C NagaiahDepartment of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.ORCID https://orcid.org/0000-0003-3545-6668
Indian Institute of Technology Ropar · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early and rapid detection of neuron-specific enolase (NSE) is highly significant, as it is putative biomarker for small-cell lung cancer as well as COVID-19. Electrochemical techniques have attracted substantial attention for the early detection of cancer biomarkers due to the important properties of simplicity, high sensitivity, specificity, low cost, and point-of-care detection. This work reviews the clinically relevant labeled and label-free electrochemical immunosensors developed so far for the analysis of NSE. The prevailing role of nanostructured materials as electrode matrices is thoroughly discussed. Subsequently, the key performances of various immunoassays are critically evaluated in terms of limit of detection, linear ranges, and incubation time for clinical translation. Electrochemical techniques coupled with screen-printed electrodes developing market level commercialization of NSE sensors is also discussed. Finally, the review concludes with the current challenges associated with available methods and provides a future outlook toward commercialization opportunities for easy detection of NSE.

Identifiers

PMID38222505
PMCPMC10785636
OpenAlexW4390193850

What OpenQuestion 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 OpenQuestion graph.