Enhancing vascular imaging using NIR-II fluorescent nanoprobes
Bibliographic record
Abstract
Fluorescence imaging has emerged as a valuable tool for clinical angiographic and cardiovascular imaging, allowing for visualization and quantification of biological processes. Among the range of fluorescence imaging windows, near-infrared (NIR) imaging has shown great promise as a non-invasive modality for angiographic and cardiovascular imaging. To overcome limitations associated with indocyanine green dye (ICG), we developed a biocompatible DNA-based platform for conjugation with ICG dyes and targeting moieties. The primary objective of this pilot study is to evaluate the efficacy of the DNA-ICG platform for contrast-enhanced NIR-II (>1250 nm) fluorescence imaging in a mouse model. Throughout the experiment, various organs were observed, including the heart, liver, spleen, caecum, and intestines. Notably, vascular structures in the tail, spinal column, and head remained visible for hours after the administration of the contrast agent. The DNA-ICG platform holds promise as an effective imaging tool for angiographic and cardiovascular studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".