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Record W2154144050 · doi:10.1128/jvi.01022-06

Peripheral Blood B-Cell Death Compensates for Excessive Proliferation in Lymphoid Tissues and Maintains Homeostasis in Bovine Leukemia Virus-InfectedSheep

2006· article· en· W2154144050 on OpenAlexaff
Christophe Debacq, Nicolas Gillet, Becca Asquith, María Teresa Sánchez-Alcaraz, Arnaud Florins, Mathieu Boxus, Isabelle Schwartz‐Cornil, M. Bonneau, G Jean, Pierre Kerkhofs, J. Hay, André Théwis, Richard Kettmann, Luc Willems

Bibliographic record

VenueJournal of Virology · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersFonds De La Recherche Scientifique - FNRSFondation contre le CancerFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureInstitut National Du CancerEuropean CommissionLeverhulme Trust
KeywordsBiologyLymphocyteLymphatic systemHomeostasisBovine leukemia virusPopulationImmunologyCell biologyLymphoid leukemiaLeukemiaVirus

Abstract

fetched live from OpenAlex

The size of a lymphocyte population is primarily determined by a dynamic equilibrium between cell proliferation and death. Hence, lymphocyte recirculation between the peripheral blood and lymphoid tissues is a key determinant in the maintenance of cell homeostasis. Insights into these mechanisms can be gathered from large-animal models, where lymphatic cannulation from individual lymph nodes is possible. In this study, we assessed in vivo lymphocyte trafficking in bovine leukemia virus (BLV)-infected sheep. With a carboxyfluorescein diacetate succinimidyl ester labeling technique, we demonstrate that the dynamics of lymphocyte recirculation is unaltered but that accelerated proliferation in the lymphoid tissues is compensated for by increased death in the peripheral blood cell population. Lymphocyte homeostasis is thus maintained by biphasic kinetics in two distinct tissues, emphasizing a very dynamic process during BLV infection.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.229
Teacher spread0.221 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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