In Vivo Ischemia Detection by Luminescent Nanothermometers
Bibliographic record
Abstract
There is an urgent need to develop new diagnosis tools for real in vivo detection of first stages of ischemia for the early treatment of cardiovascular diseases and accidents. However, traditional approaches show low sensitivity and a limited penetration into tissues, so they are only applicable for the detection of surface lesions. Here, it is shown how the superior thermal sensing capabilities of near infrared-emitting quantum dots (NIR-QDs) can be efficiently used for in vivo detection of subcutaneous ischemic tissues. In particular, NIR-QDs make possible ischemia detection by high penetration transient thermometry studies in a murine ischemic hindlimb model. NIR-QDs nanothermometers are able to identify ischemic tissues by means of their faster thermal dynamics. In addition, they have shown to be capable of monitoring both the revascularization and damage recovery processes of ischemic tissues. This work demonstrates the applicability of fluorescence nanothermometry for ischemia detection and treatment, as well as a tool for early diagnosis of cardiovascular disease.
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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".