“Faster diagnosis, better outcomes: Biosensors pave the way for a brighter future for cancer patients”. As one of the top causes of death worldwide, cancer must be addressed with the help of innovative treatments and state-of-the-art diagnostic techniques. Due to stress, poor lifestyle choices, and environmental factors, cancer incidence is worryingly on the rise in India, especially among the younger generation. In India, 1 in 5 persons may receive a cancer diagnosis by 2025, potentially impacting 1.57 million people, even though 30–50% of cancers are preventable. Even though standard screening techniques are frequently too costly and impracticable for everyday use, early detection is vital. Alternatives that show promise include emerging biosensor technologies, which give quick, accurate, and customized diagnostic results. Due to its capacity to quickly and automatically identify biological changes, ultra-sensitive biosensing systems utilizing single chips have revolutionized cancer detection. Since they are more effective than conventional techniques, point-of-care (PoC) biosensors—such as innovative nano-sensing devices for exosomal micro-RNA analysis—are becoming increasingly popular. Developing sophisticated diagnostic instruments like bio-computers and resonant mirrors is made easier by these biosensors, which combine analytes, receptors, and electrical sensors to detect cancer biomarkers in biological samples. The accuracy and usability of detection are further improved by advancements in wearable technologies, microfluidics, and electrochemical and graphene-based sensors. BrCyS-Q and NanoLiposomes provide improved photodynamic treatment and targeted medication delivery, respectively. Improved patient outcomes and early intervention are anticipated using the i-Genbox, a colorimetric sensor based on LAMP technology, and DNA-SWCNT-based sensors that further improve biomarker identification for gynecologic tumors.
Citation: Dinesh Bhatia, Tania Acharjee, Monika Bhatia. Advancements in nanosensors for cancer detection[J]. AIMS Biophysics, 2024, 11(4): 527-583. doi: 10.3934/biophy.2024028
“Faster diagnosis, better outcomes: Biosensors pave the way for a brighter future for cancer patients”. As one of the top causes of death worldwide, cancer must be addressed with the help of innovative treatments and state-of-the-art diagnostic techniques. Due to stress, poor lifestyle choices, and environmental factors, cancer incidence is worryingly on the rise in India, especially among the younger generation. In India, 1 in 5 persons may receive a cancer diagnosis by 2025, potentially impacting 1.57 million people, even though 30–50% of cancers are preventable. Even though standard screening techniques are frequently too costly and impracticable for everyday use, early detection is vital. Alternatives that show promise include emerging biosensor technologies, which give quick, accurate, and customized diagnostic results. Due to its capacity to quickly and automatically identify biological changes, ultra-sensitive biosensing systems utilizing single chips have revolutionized cancer detection. Since they are more effective than conventional techniques, point-of-care (PoC) biosensors—such as innovative nano-sensing devices for exosomal micro-RNA analysis—are becoming increasingly popular. Developing sophisticated diagnostic instruments like bio-computers and resonant mirrors is made easier by these biosensors, which combine analytes, receptors, and electrical sensors to detect cancer biomarkers in biological samples. The accuracy and usability of detection are further improved by advancements in wearable technologies, microfluidics, and electrochemical and graphene-based sensors. BrCyS-Q and NanoLiposomes provide improved photodynamic treatment and targeted medication delivery, respectively. Improved patient outcomes and early intervention are anticipated using the i-Genbox, a colorimetric sensor based on LAMP technology, and DNA-SWCNT-based sensors that further improve biomarker identification for gynecologic tumors.
Artificial Bee Colony-Based Backpropagation Neural Network
Artificial Intelligence
Albumin
Acute Lymphoblastic Leukemia Image Database
Artificial Neural Network
American Society of Hematology
Gold Nanoparticles
Beta Human Chorionic Gonadotropin
Before the Common Era
Cancer Antigen 125
Carbohydrate Antigen 19-9
Cluster of Differentiation 63 (a protein commonly found on exosomes)
Carcinoembryonic Antigen
Cell-Free DNA
Convolutional Neural Network
Circulating Tumor Cells
Circulating Tumor Cells
Colorectal Cancer
Deoxyribonucleic Acid
DNA-Single-Walled Carbon Nanotube
Digital Breast Tomosynthesis
Digital Mammography
Discrete Orthogonal Stockwell Transform
Enzyme-Linked Immunosorbent Assay
Enhanced Permeability and Retention
Food and Drug Administration
Fluorescence-Activated Cell Sorting
Fluorescence Immunophenotyping and Interphase Cytogenetics as a Tool for Investigation of Neoplasms
Fluorescence in Situ Hybridization
Histological Image Analysis
Human Epididymis Protein 4
Human Papillomavirus
High-Performance Liquid Chromatography
Internet of Medical Things
Infrared
K-Nearest Neighbors
Lung Cancer
Linear Discriminant Analysis
Lung-Metastasis Initiating Stem Cells
Loop-Mediated Isothermal Amplification
Magnetic-Activated Cell Sorting
Metal Insulator Metal
Metastasis-Initiating Stem Cells
MicroRNA
MicroRNAs
Multiple Myeloma
Multidrug Resistance
Non-Communicable Diseases
Next-Generation Sequencing
Natural Killer Cells
Near-Infrared
Nanoparticles
Positron Emission Tomography
Principal Component Analysis
Prostate-Specific Antigen
Phosphoserine
Photodynamic Therapy
Ribonucleic Acid
Reduced Graphene Oxide/Gold Nanoparticles
Residual Convolutional Neural Network with 34 layers
Random Forest
Reactive Oxygen Species
Surface-Enhanced Raman Spectroscopy
Support Vector Machine
Single-Walled Carbon Nanotube
Tumor-Derived Exosomes
Tumor-Derived Exosomes
Units per Milliliter
Volatile Organic Compounds
White Blood Cells
X-radiation (a form of electromagnetic radiation)
Chitinase-3-like Protein 1
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Figures(9) / Tables(3)
Dinesh Bhatia, Tania Acharjee, Monika Bhatia. Advancements in nanosensors for cancer detection[J]. AIMS Biophysics, 2024, 11(4): 527-583. doi: 10.3934/biophy.2024028
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