
Mesoporous silica nanoparticles (MSNs) exhibit highly beneficial characteristics for devising efficient biosensors for different analytes. Their unique properties, such as capabilities for stable covalent binding to recognition groups (e.g., antibodies or aptamers) and sensing surfaces, open a plethora of opportunities for biosensor construction. In addition, their structured porosity offers capabilities for entrapping signaling molecules (dyes or electroactive species), which could be released efficiently in response to a desired analyte for effective optical or electrochemical detection. This work offers an overview of recent research studies (in the last five years) that contain MSNs in their optical and electrochemical sensing platforms for the detection of cancer biomarkers, classified by cancer type. In addition, this study provides an overview of cancer biomarkers, as well as electrochemical and optical detection methods in general.
MSNs, cancer biomarkers, Review, Biosensing Techniques, Electrochemical Techniques, biosensors, optical detection, Silicon Dioxide, Neoplasms, electrochemical detection, Biomarkers, Tumor, Nanoparticles, Humans, Porosity, TP248.13-248.65, Biotechnology
MSNs, cancer biomarkers, Review, Biosensing Techniques, Electrochemical Techniques, biosensors, optical detection, Silicon Dioxide, Neoplasms, electrochemical detection, Biomarkers, Tumor, Nanoparticles, Humans, Porosity, TP248.13-248.65, Biotechnology
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 18 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
