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American Society of Transplantation Symposium on B Cells in Transplantation: Harnessing Humoral Immunity from Rodent Models to Clinical Practice

2007· article· en· W1976891527 on OpenAlexafffund
Allan D. Kirk, William M. Baldwin, Marília Cascalho, Anita S. Chong, Megan Sykes, Lori J. West

Bibliographic record

VenueAmerican Journal of Transplantation · 2007
Typearticle
Languageen
FieldMedicine
TopicXenotransplantation and immune response
Canadian institutionsUniversity of Alberta
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthFondation pour la Recherche MédicaleHeart and Stroke Foundation of CanadaNational Heart, Lung, and Blood InstituteAmerican Society of Transplantation
KeywordsTransplantationMedicineImmunologyOrgan transplantationImmunityIntensive care medicineImmune systemInternal medicine

Abstract

fetched live from OpenAlex

There is growing awareness that B cells and alloantibodies are important mediators of both acute and chronic allograft injury. Unfortunately, few therapies are clinically available to mitigate the function of B cells or the effects of established alloantibody. As a result, many sensitized people await transplantation without a suitable donor, and several rejection syndromes are emerging that appear to involve B cells either as antibody producers or as antigen-presenting cells. In recognition of this unmet need in transplantation, the American Society of Transplantation organized a Symposium on B cells in Organ Transplantation to foster interest in this topic amongst basic researchers attending the annual meeting of the American Association of Immunologists. This manuscript will give an overview of the presentations from this symposium including the current risks of allosensitization, adaptive accommodation, approaches toward B-cell tolerance for allo- and xenoantigens and clinical application of these concepts in ABO incompatible neonatal cardiac transplantation.

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)0.005
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0170.008

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 head0.039
GPT teacher head0.361
Teacher spread0.322 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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