Nano–Bio Interactions of Extracellular Vesicles with Gold Nanoislands for Early Cancer Diagnosis
Bibliographic record
Abstract
Extracellular vesicles or exosomes are membrane encapsulated biological nanometric particles secreted virtually by all types of cells throughout the animal kingdom. They carry a cargo of active molecules to proximal and distal cells of the body as mechanism of physiological communication, to maintain natural homeostasis as well as pathological responses. Exosomes carry a tremendous potential for liquid biopsy and therapeutic applications. Thus, there is a global demand for simple and robust exosome isolation methods amenable to point-of-care diagnosis and quality control of therapeutic exosome manufacturing. This can be achieved by molecular profiling of the exosomes for use with specific sets of molecular-markers for diagnosis and quality control. Liquid biopsy is undoubtedly the most promising diagnosis process to advance "personalized medicine." Currently, liquid biopsy is based on circulating cancer cells, cell free-DNA, or exosomes. Exosomes potentially provide promise for early-stage diagnostic possibility; in order to facilitate superior diagnosis and isolation of exosomes, a novel platform is developed to detect and capture them, based on localized surface plasmon resonance (LSPR) of gold nanoislands, through strong affinity between exosomes and peptide called Venceremin or Vn96. Physical modeling, based on the characteristics of the gold nanoislands and the bioentities involved in the sensing, is also developed to determine the detection capability of the platform, which is optimized experimentally at each stage. Preliminary results and modeling present a relationship between the plasmonic shift and the concentration of exosomes and, essentially, indicate possibilities for label-free early diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".