Evidence map›Paper›PMID 39248694›Full record

ReviewACS sensors2024

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers.

Vishal Chaudhary, Bakr Ahmed Taha, Lucky, Sarvesh Rustagi, Ajit Khosla, Pagona Papakonstantinou, Nikhil Bhalla

Abstract readReview
In one paragraph

Review in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. Review
  15. Review
  16. Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025
    Review
  17. Review
  18. 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

7 authors.

Vishal ChaudharyPhysics Department, Bhagini Nivedita College, University of Delhi, 110043 Delhi, India.
Bakr Ahmed TahaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600 Bangi, Malaysia.
LuckyDr. B. R. Ambedkar Center for Biomedical Research, University of Delhi, 110007 Delhi, India.
Sarvesh RustagiSchool of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand 248007, India.
Ajit KhoslaSchool of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710126, China.ORCID 0000-0002-2803-8532
Pagona PapakonstantinouNanotechnology and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, 2-24 York Street, Belfast, Northern Ireland BT15 1AP, United Kingdom.ORCID 0000-0003-0019-3247
Nikhil BhallaNanotechnology and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, 2-24 York Street, Belfast, Northern Ireland BT15 1AP, United Kingdom.ORCID 0000-0002-4720-3679

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Indexed as

Biomarkers, TumorBiosensing TechniquesBreath TestsEarly Detection of CancerLung NeoplasmsElectronic NoseHumansLab-On-A-Chip DevicesVolatile Organic CompoundsBiomarkers, TumorVolatile Organic CompoundsBiomarkersBiosensorsBreathomicsEarly detectionLab-on-chipLung cancerNanotechnologyNose-on-chip

Identifiers

PMID39248694
PMCPMC11443536

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.