Hypoxia-specific ultrasensitive detection of tumours and cancer cells in vivo
Bibliographic record
Abstract
Highly sensitive and specific non-invasive molecular imaging methods are particularly desirable for the early detection of cancers. Here we report a near-infrared optical imaging probe highly specific to the hypoxic tumour microenvironment to detect tumour and cancer cells with the sensitivity to a few thousands cancer cells. This oxygen-sensitive, near-infrared emitting and water-soluble phosphorescent macromolecular probe can not only report the hypoxic tumour environment of various cancer models, including metastatic tumours in vivo, but can also detect a small amount of cancer cells before the formation of the tumour based on the increased oxygen consumption during cancer cell proliferation. Thus, the reported hypoxia-sensitive probe may offer an imaging tool for characterizing the tumour microenvironment in vivo, detecting cancer cells at a very early stage of tumour development and lymph node metastasis. As hypoxia is a hallmark of tumour microenvironment, hypoxia-sensing probes are used for tumour imaging. Here, the authors report a hypoxia probe with increased sensitivity, water solubility and functional pH range, allowing in vivodetection of early metastases as small as a few thousand cells.
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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 | 0.000 | 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) | 0.001 | 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".