ArticleSmall science2024
Emerging Multifunctional Carbon-Nanomaterial-Based Biosensors for Cancer Diagnosis.
Article in Small science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Article
- Spatially Profiling Trace Cytokine Signatures From Microscopically Derived Skin Samples to Probe Skin Disease Inflammation.Small methods · 2026Article
- Clinical biomarkers and biosensors for early detection of metabolic dysfunction-associated steatohepatitis (MASH): a translational perspective.Frontiers in bioengineering and biotechnology · 2026Review
- Nanomaterial Engineered Biosensors and Stimulus-Responsive Platform for Emergency Monitoring and Intelligent Diagnosis.Biosensors · 2025Review
- Nanomaterial-Based Biosensors for the Detection of COVID-19.Indian journal of microbiology · 2025Article
- Nanomaterials for Theranostic Management of Oral Cancer: Advances in Imaging, Biosensing, Targeted Delivery, and Multimodal Synergistic Therapy.International journal of nanomedicine · 2025Review
- Recent Progress of Molecularly Imprinted Technique for the Detection of Mycotoxins in Food.Foods (Basel, Switzerland) · 2024Review
- Photonics of Hydrothermally Treated β-Lactoglobulin Amyloids.Small science · 2024Article
- Advancements in SARS-CoV-2 detection: Navigating the molecular landscape and diagnostic technologies.Heliyon · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite significant advancements in medical technology, cancer remains the world's second-leading cause of death, largely attributed to late-stage diagnoses. While traditional cancer detection methodologies offer foundational insights, they often lack the specificity, affordability, and sensitivity for early-stage identification. In this context, the development of biosensors offers a distinct possibility for the precise and rapid identification of cancer biomarkers. Carbon nanomaterials, including graphene, carbon nitride, carbon quantum dots, and other carbon-based nanostructures, are highly promising for cancer detection. Their simplicity, high sensitivity, and cost-effectiveness contribute to their potential in this field. This review aims to elucidate the potential of emerging carbon-nanomaterial-based biosensors for early cancer diagnosis. The relevance of the various biosensor mechanisms and their performance to the physicochemical properties of carbon nanomaterials is discussed in depth, focusing on demonstrating broad methodologies for creating performance biosensors. Diverse carbon-nanomaterial-based detection techniques, such as electrochemical, fluorescence, surface plasmon resonance, electrochemiluminescence, and quartz crystal microbalance, are emphasized for early cancer detection. At last, a summary of existing challenges and future outlook in this promising field is elaborated.
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Registered trials
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.