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Record W4365148966 · doi:10.3389/fimmu.2023.1170438

Thymus antibody-secreting cells: once forgotten but not lost

2023· article· en· W4365148966 on OpenAlexafffund
KimAnh T. Pioli, Peter D. Pioli

Bibliographic record

VenueFrontiers in Immunology · 2023
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research FoundationNational Institute on AgingNational Institutes of HealthUniversity of Saskatchewan
KeywordsImmune systemAntibodyImmunologySecretionBiologyBone marrowPopulationMedicineEndocrinology

Abstract

fetched live from OpenAlex

Antibody-secreting cells are essential contributors to the humoral response. This is due to multiple factors which include: 1) the ability to secrete thousands of antibodies per second, 2) the ability to regulate the immune response and 3) the potential to be long-lived. Not surprisingly, these cells can be found in numerous sites within the body which include organs that directly interface with potential pathogens (e.g., gut) and others that provide long-term survival niches (e.g., bone marrow). Even though antibody-secreting cells were first identified in the thymus of both humans and rodents in the 1960s, if not earlier, only recently has this population begun to be extensively investigated. In this article, we provide an update regarding the current breath of knowledge pertaining to thymus antibody-secreting cells and discuss the potential roles of these cells and their impact on health.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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 designNot applicable
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

Citations3
Published2023
Admission routes2
Has abstractyes

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