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Record W2895824610 · doi:10.1038/s41598-018-34105-3

Locating human splenic capillary sheaths in virtual reality

2018· article· en· W2895824610 on OpenAlexfundno aff
Birte Steiniger, Verena Wilhelmi, Melinda Berthold, Michael Guthe, Oleg Lobachev

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsCapillary actionCD34AnatomyBiologyStromal cellPathologyCell biologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.052
GPT teacher head0.347
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2018
Admission routes1
Has abstractyes

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