Endotoxemia increases the clearance of mPEGylated 5000-MW quantum dots as revealed by multiphoton microvascular imaging
Bibliographic record
Abstract
Imaging the microcirculation is becoming increasingly important in assessing life-threatening disease states. To address this issue in a highly light absorbing and light scattering tissue, we use laser scanning multiphoton microscopy and fluorescent 655-nm 5000-MW methoxy-PEGylated quantum dots to image the functional microcirculation deep in mouse hind limb skeletal muscle. Using this approach, we are able to minimize in vivo background tissue autofluorescence and visualize complete 3-D microvascular units, including feeding arterioles, capillary networks, and collecting venules to depths of 150 to 200 microm. In CD1 mice treated with lipopolysaccharide to model an endotoxemic response to bacterial infection, we find that these quantum dots accumulate at microvascular bifurcations and extravasate from the microcirculation in addition to accumulating in organs (liver, spleen, lung, and kidney). The quantum dots are cleared from the circulation with a first-order elimination rate constant seven times greater than under normal conditions, 1.6+/-0.06 compared to 0.23+/-0.05 h(-1), P<0.05, thereby reducing the imaging time window. In vitro experiments using TNFalpha treated isolated leukocytes suggest that circulating monocytes (phagocytes) increased their nonspecific uptake of quantum dots when activated. In combination with multiphoton microscopy, quantum dots provide excellent in vivo imaging contrast of deep microvascular structures.
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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".