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Record W2482422259 · doi:10.3791/54205

Femur Window Chamber Model for <em>In Vivo</em> Cell Tracking in the Murine Bone Marrow

2016· article· en· W2482422259 on OpenAlexafffund
Yonghong Chen, Azusa Maeda, Jiachuan Bu, Ralph S. DaCosta

Bibliographic record

VenueJournal of Visualized Experiments · 2016
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer Centre
FundersUniversity Health Network
KeywordsBone marrowHaematopoiesisIn vivoImmune systemPathologyStem cellHematopoietic stem cellMedicineBiologyCancer researchImmunologyCell biology

Abstract

fetched live from OpenAlex

Bone marrow is a complex organ that contains various hematopoietic and non-hematopoietic cells. These cells are involved in many biological processes, including hematopoiesis, immune regulation and tumor regulation. Commonly used methods for understanding cellular actions in the bone marrow, such as histology and blood counts, provide static information rather than capturing the dynamic action of multiple cellular components in vivo. To complement the standard methods, a window chamber (WC)-based model was developed to enable serial in vivo imaging of cells and structures in the murine bone marrow. This protocol describes a surgical procedure for installing the WC in the femur, in order to facilitate long-term optical access to the femoral bone marrow. In particular, to demonstrate its experimental utility, this WC approach was used to image and track neutrophils within the vascular network of the femur, thereby providing a novel method to visualize and quantify immune cell trafficking and regulation in the bone marrow. This method can be applied to study various biological processes in the murine bone marrow, such as hematopoiesis, stem cell transplantation, and immune responses in pathological conditions, including cancer.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.375
Teacher spread0.335 · 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 designBench or experimental
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

Citations8
Published2016
Admission routes2
Has abstractyes

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