Review—Quantum Biosensors: Principles and Applications in Medical Diagnostics
Abstract
Originating at the intersection of physics and biosensing, quantum biosensors (QB) are transforming medical diagnostics and personalized medicine by exploiting quantum phenomena to amplify sensitivity, specificity, and detection speed compared to traditional biosensors. Their foundation lies in the fusion of biological entities like DNA, proteins, or enzymes with quantum sensors, which elicits discernible alterations in light emissions when interacting with sample molecules. Their prowess in identifying disease-linked biomarkers presents an avenue for early diagnoses of conditions like Alzheimer’s and cancer. Beyond this, they enable real-time monitoring of treatment responses by capturing the dynamism of biomarkers, but QB still faces challenges, such as issues of stability, reproducibility, and intricate quantum interactions. Moreover, seamless integration into prevailing diagnostic frameworks necessitates careful consideration. Looking ahead, the evolution of QB navigates uncharted territories. Innovations in fabrication techniques, interdisciplinary collaborations, and standardization protocols emerge as pivotal areas of exploration. This comprehensive discourse encapsulates QB’s principles, diverse iterations, and burgeoning medical utilities. It delves into inherent challenges and limitations, shedding light on the potential trajectories of future research. As QB continues to evolve, its potential to redefine medical diagnostics becomes increasingly tangible. The saga…
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.