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Record W2052286319 · doi:10.1371/journal.pone.0027508

Comparing the MRI Appearance of the Lymph Nodes and Spleen in Wild-Type and Immuno-Deficient Mouse Strains

2011· article· en· W2052286319 on OpenAlexafffund
Vasiliki Economopoulos, Jennifer Noad, Shruti Krishnamoorthy, Brian K. Rutt, Paula J. Foster

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsWestern University
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Cancer Society Research Institute
KeywordsLymphSpleenLymph nodeMagnetic resonance imagingPathologyLymphatic systemMedicineBiologyImmunologyRadiology

Abstract

fetched live from OpenAlex

The goal of this study was to investigate the normal MRI appearance of lymphoid organs in immuno-competent and immuno-deficient mice commonly used in research. Four mice from each of four different mouse strains (nude, NOG, C57BL/6, CB-17 SCID (SCID)) were imaged weekly for one month. Images were acquired with a 3D balanced steady state free precession (bSSFP) sequence. The volume of the lymph nodes and spleens were measured from MR images. In images of nude and SCID mice, lymph nodes sometimes contained a hyperintense region visible on MRI images. Volumes of the nodes were highly variable in nude mice. Nodes in SCID mice were smaller than in nude or C57Bl/6 mice (p<0.0001). Lymph node volumes changed slightly over time in all strains. The spleens of C57Bl/6 and nude mice were similar in size and appearance. Spleens of SCID and NOG mice were significantly smaller (p<0.0001) and abnormal in appearance. The MRI appearance of the normal lymph nodes and spleen varies considerably in the various mouse strains examined in this study. This is important to recognize in order to avoid the misinterpretation of MRI findings as abnormal when these strains are used in MRI imaging studies.

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)0.000
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 score0.036
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.064
GPT teacher head0.255
Teacher spread0.190 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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