Intravital Imaging of Myeloid Cells: Inflammatory Migration and Resident Patrolling
Bibliographic record
Abstract
The first documented experiments using intravital microscopy were performed in the 19th century, in which very thin translucent tissues were used so that light could penetrate through the tissue and leukocyte trafficking could be observed (1). Neither human tissues nor solid organs in animal models could be used at the time. As such, tissues like the rodent mesentery, cremaster muscle, and ear and the bat wing were the preparations of choice for the next century. This type of imaging unveiled the very dynamic interaction of immune cells with vessel walls. The experimentalists tried to keep the conditions as close to the natural environment as was feasible. The bat wing and ear vasculatures required no surgery, making them likely the least perturbed approach. The mesentery and cremaster, which required only minor surgery, likely did induce a nonphysiologic baseline of leukocyte-vessel wall interactions. However, this came with the benefit of being able to examine cellular functions and behaviors under shear forces associated with blood flow as well as the surrounding architecture of capillaries and venules that was impossible to replicate in vitro. Indeed, as diligent as experimentalists were, in vitro settings could not completely replicate the behavior of immune cells as they interacted with each other, red blood cells and platelets in capillaries and postcapillary venules surrounded by pericytes and with macrophages, mast cells, and the myriad of other resident immune and parenchymal cells that constitute a living organ. Moreover, interorgan and neural communications were also not possible in vitro. However, it is always critical to remember that rodents, bats, and fish are not humans, and so all interpretations must be made with this in mind. It is also worth mentioning that many of the in vivo discoveries were made hand in hand with key in vitro experiments that allowed simplification of the complex model to elucidate cellular and molecular events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".