The development of a virtual 3D model of the renal corpuscle from serial histological sections for <scp>E</scp>‐learning environments
Bibliographic record
Abstract
Histology is a core subject in the anatomical sciences where learners are challenged to interpret two-dimensional (2D) information (gained from histological sections) to extrapolate and understand the three-dimensional (3D) morphology of cells, tissues, and organs. In gross anatomical education 3D models and learning tools have been associated with improved learning outcomes, but similar tools have not been created for histology education to visualize complex cellular structure-function relationships. This study outlines steps in creating a virtual 3D model of the renal corpuscle from serial, semi-thin, histological sections obtained from epoxy resin-embedded kidney tissue. The virtual renal corpuscle model was generated by digital segmentation to identify: Bowman's capsule, nuclei of epithelial cells in the parietal capsule, afferent arteriole, efferent arteriole, proximal convoluted tubule, distal convoluted tubule, glomerular capillaries, podocyte nuclei, nuclei of extraglomerular mesangial cells, nuclei of epithelial cells of the macula densa in the distal convoluted tubule. In addition to the imported images of the original sections the software generates, and allows for visualization of, images of virtual sections generated in any desired orientation, thus serving as a "virtual microtome". These sections can be viewed separately or with the 3D model in transparency. This approach allows for the development of interactive e-learning tools designed to enhance histology education of microscopic structures with complex cellular interrelationships. Future studies will focus on testing the efficacy of interactive virtual 3D models for histology education.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".