Visualization of 3D White Adipose Tissue Structure Using Whole-mount Staining
Bibliographic record
Abstract
Adipose tissue is an important metabolic organ with high plasticity and is responsive to environmental stimuli and nutrient status. As such, various techniques have been developed to study the morphology and biology of adipose tissue. However, conventional visualization methods are limited to studying the tissue in 2D sections, failing to capture the 3D architecture of the whole organ. Here we present whole-mount staining, an immunohistochemistry method that preserves intact adipose tissue morphology with minimal processing steps. Hence, the structures of adipocytes and other cellular components are maintained without distortion, achieving the most representative 3D visualization of the tissue. In addition, whole-mount staining can be combined with lineage tracing methods to determine cell fate decisions. However, this technique has some limitations to providing accurate information regarding deeper parts of adipose tissue. To overcome this limitation, whole-mount staining can be further combined with tissue clearing techniques to remove the opaqueness of tissue and allow for complete visualization of entire adipose tissue anatomy using light-sheet fluorescent microscopy. Therefore, a higher resolution and more accurate representation of adipose tissue structures can be captured with the combination of these techniques.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".