Molecular imaging of tumour‐associated pathological biomarkers with smart nanoprobe: From “Seeing” to “Measuring”
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
Although the extraordinary progress has been made in molecular biology, the prevention of cancer remains arduous. Most solid tumours exhibit both spatial and temporal heterogeneity, which is difficult to be mimicked in vitro. Additionally, the complex biochemical and immune features of tumour microenvironment significantly affect the tumour development. Molecular imaging aims at the exploitation of tumour-associated molecules as specific targets of customized molecular probe, thereby generating image contrast of tumour markers, and offering opportunities to non-invasively evaluate the pathological characteristics of tumours in vivo. Particularly, there are no "standard markers" as control in clinical imaging diagnosis of individuals, so the tumour pathological characteristics-responsive nanoprobe-based quantitative molecular imaging, which is able to visualize and determine the accurate content values of heterogeneous distribution of pathological molecules in solid tumours, can provide criteria for cancer diagnosis. In this context, a variety of "smart" quantitative molecular imaging nanoprobes have been designed, in order to provide feasible approaches to quantitatively visualize the tumour-associated pathological molecules in vivo. This review summarizes the recent achievements in the designs of these nanoprobes, and highlights the state-of-the-art technologies in quantitative imaging of tumour-associated pathological molecules.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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