Nanoscopic technologies toward molecular profiling of single extracellular vesicles for cancer liquid biopsy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracellular vesicles (EVs) have emerged as promising cancer biomarkers due to their encapsulation of molecular signals reflective of originating tumor cells. Conventional analytical methods often fall short in comprehensive EV molecular profiling, necessitating innovative approaches for enhanced sensitivity and selectivity. This review focuses on the utilization of nanoplasmonic structures for optical signal detection of EVs, exploring advancements, challenges, and future prospects toward single EV molecular profiling. Nanoplasmonic structures offer enhanced optical readout capabilities, leveraging light iridescence, and plasmonic amplification suitable for the size range and complexity of the EVs. We delve into the research and implications of on-chip methods, shedding light on EVs' role in health and disease. Despite notable progress, opportunities still exist to further develop nanoplasmonic arrays, customizing them for bioanalytes of interest, crucial for both label-free and labeled techniques to attain the objectives of their EV profiling. One such example is the use of specific antibodies for surface functionalization in nanoplasmonic arrays. Other approaches involve tailoring the design of platforms to the physical properties of target EVs, thereby enhancing characterization capabilities. The subsequent sections will cover a curated selection of relevant studies. We later discuss EV analysis through plasmonic nanoarrays in clinical sample scenarios. While patterning methods, such as colloidal self-assembly and e-beam lithography, enable integration with microfluidic systems, facilitating future investigations, few technologies have entered clinical trials. This roadblock highlights the need for further development of cost-effective, detailed molecular profiling methods. Moreover, we discuss avenues like single EV profiling and machine learning to address challenges related to heterogeneity of EVs as liquid biopsy biomarkers. Finally, we discuss future opportunities in developing nanoplasmonic-assisted EV profiling and studied their driving advancements in diagnostic and therapeutic realms, such as customizable nanoplasmonic structures coupled with artificial intelligence analysis modules, as a path forward for precise EV molecular profiling, which may enable personalized therapeutic interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it