Optical imaging of tumor microenvironment.
Bibliographic record
Abstract
Tumor microenvironment plays important roles in tumor development and metastasis. Features of the tumor microenvironment that are significantly different from normal tissues include acidity, hypoxia, overexpressed proteases and so on. Therefore, these features can serve as not only biomarkers for tumor diagnosis but also theraputic targets for tumor treatment. Imaging modalities such as optical, positron emission tomography (PET) and magnetic resonance imaging (MRI) have been intensively applied to investigate tumor microenvironment. Various imaging probes targeting pH, hypoxia and proteases in tumor microenvironment were thus well developed. In this review, we will focus on recent examples on fluorescent probes for optical imaging of tumor microenvironment. Construction of these fluorescent probes were based on characteristic feature of pH, hypoxia and proteases in tumor microenvironment. Strategies for development of these fluorescent probes and applications of these probes in optical imaging of tumor cells or tissues will be discussed in this review paper.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".