ReviewBiosensors2022
Emerging Biosensors for Oral Cancer Detection and Diagnosis-A Review Unravelling Their Role in Past and Present Advancements in the Field of Early Diagnosis.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed.
- Current Trends in Diagnosis and Early Monitoring of Oral Cavity Cancer: Techniques and Biomarkers.Cancers · 2026Review
- Biosensors for oral cancer detection: a scoping review.Diagnostic pathology · 2026Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- Cell-Based Biosensors in Oral Health: Emerging Tools for Rapid Detection and Monitoring of Oral Diseases.Biosensors · 2026Review
- Biosensors in dental, oral and craniofacial applications.Npj biosensing · 2026Review
- Leveraging Dental Biotechnology for Population Oral Health: Innovations in Prevention, Diagnosis, and Treatment.International journal of molecular sciences · 2025Review
- Recent Advancements and Applications of Nanosensors in Oral Health: Revolutionizing Diagnosis and Treatment.European journal of dentistry · 2025Article
- Article
- Perspectives on the Application of Biosensors for the Early Detection of Oral Cancer.Sensors (Basel, Switzerland) · 2025Review
- Nanomaterials for Theranostic Management of Oral Cancer: Advances in Imaging, Biosensing, Targeted Delivery, and Multimodal Synergistic Therapy.International journal of nanomedicine · 2025Review
- microRNA-based electrochemical biosensor for early detection of pulmonary arterial hypertension.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Delving into Matrix Metalloproteinase-1 (MMP-1) and its Significance in Periodontal Diseases.Journal of pharmacy & bioallied sciences · 2024Review
- Review
- Review Insights on Salivary Proteomics Biomarkers in Oral Cancer Detection and Diagnosis.Molecules (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer is a serious concern to people all over the world because of its high mortality rate and metastatic spread to other areas of the body. Despite recent advancements in biomedical research, OC detection at an early stage remains a challenge and is complex and inaccurate with conventional diagnostics procedures. It is critical to study innovative approaches that can enable a faster, easier, non-invasive, and more precise diagnosis of OC in order to increase the survival rate of patients. In this paper, we conducted a review on how biosensors might be an excellent tool for detecting OC. This review covers the strategies that use different biosensors to target various types of biomarkers and focuses on biosensors that function at the molecular level viz. DNA biosensors, RNA biosensors, and protein biosensors. In addition, we reviewed non-invasive electrochemical methods, optical methods, and nano biosensors to analyze the OC biomarkers present in body fluids such as saliva and serum. As a result, this review sheds light on the development of ground-breaking biosensors for the early detection and diagnosis of OC.
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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.