Locating human splenic capillary sheaths in virtual reality
Bibliographic record
Abstract
Abstract Stromal capillary sheath cells in human spleens strongly express CD271, the low affinity nerve growth factor receptor p75. Serial sections of a representative adult human spleen were double-stained for CD271 versus smooth muscle alpha actin (SMA) plus CD34 to visualise capillary sheaths, the arterial tree and endothelial cells by transmitted light. Preliminary three-dimensional (3D) reconstructions of single regions were inspected in virtual reality (VR). This method showed that a large number of CD271 + sheaths occur in a post-arteriolar position often surrounding capillaries located close to divisions of arterioles. The length and diameter of capillary sheaths are rather heterogeneous. Long sheaths were observed to accompany one or two generations of capillary branches. We hypothesise that human splenic capillary sheaths may attract recirculating B-lymphocytes from the open circulation of the red pulp to start their migration into white pulp follicles along branches of the arterial tree. In addition, they may provide sites of interaction among sheath macrophages and B-lymphocytes. Our innovative approach allows stringent quality control by inserting the original immunostained serial sections into the 3D model for viewing and annotation in VR. Longer series of sections will allow to unequivocally localise most of the capillary sheaths in a given volume.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".