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Record W2509033762 · doi:10.1016/j.celrep.2016.07.076

Differential Intrasplenic Migration of Dendritic Cell Subsets Tailors Adaptive Immunity

2016· article· en· W2509033762 on OpenAlexfundno aff
Samuele Calabrό, Dong Liu, Antonia E. Gallman, Manuela Sales Lima Nascimento, Zizi Yu, Tingting Zhang, Pei Chen, Biyan Zhang, Lan Xu, Uthaman Gowthaman, Jayendra Kumar Krishnaswamy, Ann M. Haberman, Adam Williams, Stephanie C. Eisenbarth

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsImmunityAcquired immune systemDendritic cellImmunologyBiologyCell biologyChemistryImmune system

Abstract

fetched live from OpenAlex

Evidence suggests that distinct splenic dendritic cell (DC) subsets activate either CD4+ or CD8+ T cells in vivo. This bias has been partially ascribed to differential antigen presentation; however, all DC subsets can activate both T cell lineages in vitro. Therefore, we tested whether the organization of DC and T cell subsets in the spleen dictated this preference. We discovered that CD4+ and CD8+ T cells segregated within splenic T cell zones prior to immunization. After intravenous immunization, the two major conventional DC populations, distinguished by 33D1 and XCR1 staining, migrated into separate regions of the T cell zone: 33D1+ DCs migrated into the CD4+ T cell area, whereas XCR1+ DCs migrated into the CD8+ T cell area. Thus, the post-immunization location of each DC subset correlated with the T cell lineage it preferentially primes. Preventing this co-localization selectively impaired either CD4+ or CD8+ T cell immunity to blood-borne antigens.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations125
Published2016
Admission routes1
Has abstractyes

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