Perfusion Staining Methods for Visualization of Intact Microvascular Networks in Whole Mount Skeletal Muscle Preparations
Bibliographic record
Abstract
INTRODUCTION: Visualization of the intact microvascular network in skeletal muscle requires labeling the entire network in whole mount preparations where muscle fibre length can be set to near optimal but the tools to do this are not clear. METHODS: We intravascularly injected CD-1 mice with different fluorescently labelled lectins (fluorescent isolectin GS-IB4 [ISO], wheat germ agglutinin [WGA], lycopersicon esculentum [LYCO]) or FITC-labelled gel. Soleus, extensor digitorum longus, diaphragm, gluteus maximus and cremaster muscles were excised, pinned at optimal sarcomere length and viewed using fluorescence microscopy. RESULTS: WGA and LYCO were effective at labeling the entire vascular network with WGA labeling capillaries more brightly. ISO labelled the arteriolar vasculature and early segments of the capillaries but not the full length of the capillaries or the venular network. FITC-labelled gel was effective at labelling the microvascular network but not all small vessels were consistently labelled. The pattern of staining for each labelling method was similar across all muscle fibre-types tested. CONCLUSIONS: WGA was optimal for perfusion labeling and visualization of the intact microvascular network in whole mount skeletal muscle preparations and can be used in combination with ISO to distinguish the arteriolar and venous sides of the network. INTRODUCTION: Visualization of the intact microvascular network in skeletal muscle requires labeling the entire network in whole mount preparations where muscle fibre length can be set to near optimal but the tools to do this are not clear. METHODS: We intravascularly injected CD-1 mice with different fluorescently labelled lectins (fluorescent isolectin GS-IB4 [ISO], wheat germ agglutinin [WGA], lycopersicon esculentum [LYCO]) or FITC-labelled gel. Soleus, extensor digitorum longus, diaphragm, gluteus maximus and cremaster muscles were excised, pinned at optimal sarcomere length and viewed using fluorescence microscopy. RESULTS: WGA and LYCO were effective at labeling the entire vascular network with WGA labeling capillaries more brightly. ISO labelled the arteriolar vasculature and early segments of the capillaries but not the full length of the capillaries or the venular network. FITC-labelled gel was effective at labelling the microvascular network but not all small vessels were consistently labelled. The pattern of staining for each labelling method was similar across all muscle fibre-types tested. CONCLUSIONS: WGA was optimal for perfusion labeling and visualization of the intact microvascular network in whole mount skeletal muscle preparations and can be used in combination with ISO to distinguish the arteriolar and venous sides of the network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".