3D Visualization of the Glomerulus within Kidney Tissue made Transparent through Passive Optical Clearing
Bibliographic record
Abstract
The understanding of structure/function relationships in complex cellular systems is enhanced by 3D visualization of their organization at microscopic resolution. Primarily for e-learning purposes, we recently used Amira 5.1 software to develop a digital 3D model of the renal corpuscle derived from serial histologically stained semi-thin sections. Since this technique is labour-intensive and time-consuming, in this study we applied the optical clearing method CLARITY to render kidney tissue optically transparent for 3D visualization of specific structures within the renal corpuscle by immunocytochemistry and confocal microscopy. Mouse kidneys were infused with a hydrogel solution to fix and cross-link protein. Cortical kidney tissue was then cut into 1mm thick sections and passively cleared for 40 days in clearing solution. The resultant optically transparent tissue was labelled for 4 days with primary antibodies against podocyte specific antigens such as nephrin, a transmembrane protein of the slit diaphragm, followed by incubation for 4 days with fluorescent secondary antibodies. 3D visualization by confocal microscopy provides detailed morphological information of the filtration barrier in the kidney glomerulus. High-resolution 3D imaging of complex cellular structures using passive optical clearing methods thus may prove useful for histology education and in histopathological inquiries.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".