Nano–Bio Interactions of Extracellular Vesicles with Gold Nanoislands for Early Cancer Diagnosis
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 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.
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