Array-Based Sensing of Metastatic Cells and Tissues Using Nanoparticle–Fluorescent Protein Conjugates
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
Rapid and sensitive methods of discriminating between healthy tissue and metastases are critical for predicting disease course and designing therapeutic strategies. We report here the use of an array of gold nanoparticle-green fluorescent protein elements to rapidly detect metastatic cancer cells (in minutes), as well as to discriminate between organ-specific metastases and their corresponding normal tissues through their overall intracellular proteome signatures. Metastases established in a new preclinical non-small-cell lung cancer metastasis model in athymic mice were used to provide a challenging and realistic testbed for clinical cancer diagnosis. Full differentiation between the analyte cell/tissue was achieved with as little as 200 ng of intracellular protein (~1000 cells) for each nanoparticle, indicating high sensitivity of this sensor array. Notably, the sensor created a distinct fingerprint pattern for the normal and metastatic tumor tissues. Moreover, this array-based approach is unbiased, precluding the requirement of a priori knowledge of the disease biomarkers. Taken together, these studies demonstrate the utility of this sensor for creating fingerprints of cells and tissues in different states and present a generalizable platform for rapid screening amenable to microbiopsy samples.
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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