Protein corona-enabled serological tests for early stage cancer detection
Bibliographic record
Abstract
Early stage cancer detection is a major issue in current medicine. In recent years, nanotechnology is providing new alternatives for early diagnosis. Upon exposure to human plasma, several nanoparticle types (e.g. gold nanoparticles) are surrounded by a protein layer referred to as protein corona (PC). The PC changes the original identity of the nanoparticle conferring a new biological character. It is now accepted that slight variations in the composition of a protein source significantly varies the PC composition. Thus, nanomaterials incubated with plasma proteins of individuals with different physiological conditions generate PCs with different compositions. This gives rise to the new concept of personalized PC. Therefore, since protein patterns of subjects affected by certain pathologies differ from those of healthy ones, diagnostic technologies based on the evaluation of personalized PC could represent a fascinating opportunity for early disease detection. Herein, we review the concept of personalized PC along with recent advances on the topic, giving an overview of some innovative analytical approaches for early stage cancer detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".