Research status of NIR-II fluorescent probe in liver cancer imaging
Bibliographic record
Abstract
Liver cancer is a type of malignant tumor with extremely high incidence and mortality rates. For a long time, accurately distinguishing the boundary of liver tumors and achieving accurate resection of liver cancer tissue has been one of the urgent needs of clinical treatment of liver cancer. Fluorescent probes have the advantages of non-invasive, high sensitivity, real-time spatial resolution, etc., and have been widely studied in guiding liver cancer surgical navigation, photothermal therapy, and photodynamic therapy. At present, indocyanine green belongs to zone I fluorescence, which is widely used in the clinic. The shortcomings are shallow penetration depth, non-targeting, easy dispersion, and so on. The near-infrared window II (NIR)-II region has many advantages, such as high spatial resolution, deep penetration, low light absorption and scattering of biological substrates, and minimal fluorescence of tissue itself. In this review, we present the latest advances in the study of fluorescent probes for NIR-II liver cancer and discuss the challenges facing the study of fluorescent probes for NIR-II liver cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".