Additional file 1 of Highly thermal stable RNase A@PbS/ZnS quantum dots as NIR-IIb image contrast for visualizing temporal changes of microvasculature remodeling in flap
Bibliographic record
Abstract
Additional file 1: Fig. S1. Characterization of NIR-IIb–emitting RNase A@PbS/ZnS QDs. (A) Absorption spectrum of RNase A@PbS/ZnS QDs. (B) Energy-dispersive X-ray of RNase A@PbS/ZnS QDs. (C) Selected area electron diffraction pattern of RNase A@PbS/ZnS QDs. (D) TEM images of RNase A@PbS/ZnS QDs. (E) Size-distribution of RNase A@PbS/ZnS QDs. Fig. S2. The effects of different concentrations of RNase A@PbS/ZnS QDs on MSCs viability. (a). Live/dead cell staining for MSCs (green fluorescence-live cells, red fluorescence-dead cells). Scale bars represent 50 μm. (b). Quantification analysis of MSCs viability. Fig. S3. DM the diameter of epigastric artery (A) and femoral artery (B); the blue arrows indicate the femoral artery, and the yellow arrows indicate the epigastric artery. Fig. S4. In vivo pharmacokinetics and biodistribution of RNase A@PbS/ZnS QDs in normal mice. (A) Body weight of RNase A@PbS/ZnS QDs treated mice over a period time of 21 d. (B) Time course of Pb2+ concentration in the blood of RNase A@PbS/ZnS QDs treated mice over 21 h. (C) Time course of Pb2+ concentration in the feces of RNase A@PbS/ZnS QDs treated mice. (D) Biodistribution of Pb2+ in organs. Fig. S5. In vivo biodistribution of RNase A@PbS/ZnS QDs in flap perfusion animal model mice (A) Bright field and NIR-IIb fluorescence images of various organs collected from the mice at 21 days after postinjection. (B) Quantitative analysis NIR-IIb signal intensity of various organs. Fig. S6. Representative photomicrographs of hematoxylin and eosin staining on the major organs of the mice after injection of RNase A@PbS/ZnS QDs.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.880 | 0.130 |
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".