Continuous sensing of tumor-targeted molecular probes with a vertical cavity surface emitting laser-based biosensor
Bibliographic record
Abstract
Molecular optical imaging is a widespread technique for interrogating molecular events in living subjects. However, current approaches preclude long-term, continuous measurements in awake, mobile subjects, a strategy crucial in several medical conditions. Consequently, we designed a novel, lightweight miniature biosensor for in vivo continuous optical sensing. The biosensor contains an enclosed vertical-cavity surface-emitting semiconductor laser and an adjacent pair of near-infrared optically filtered detectors. We employed two sensors (dual sensing) to simultaneously interrogate normal and diseased tumor sites. Having established the sensors are precise with phantom and in vivo studies, we performed dual, continuous sensing in tumor (human glioblastoma cells) bearing mice using the targeted molecular probe cRGD-Cy5.5, which targets αVβ3 cell surface integrins in both tumor neovasculature and tumor. The sensors capture the dynamic time-activity curve of the targeted molecular probe. The average tumor to background ratio after signal calibration for cRGD-Cy5.5 injection is approximately 2.43±0.95 at 1 h and 3.64±1.38 at 2 h (N=5 mice), consistent with data obtained with a cooled charge coupled device camera. We conclude that our novel, portable, precise biosensor can be used to evaluate both kinetics and steady state levels of molecular probes in various disease applications.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".