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Record W4241359853 · doi:10.3410/f.732450290.793552019

Faculty Opinions recommendation of BCG Educates Hematopoietic Stem Cells to Generate Protective Innate Immunity against Tuberculosis.

2018· dataset· en· W4241359853 on OpenAlexaboutno aff
Serge Mostowy, Margarida C. Gomes

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2018
Typedataset
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsnot available
Fundersnot available
KeywordsInnate immune systemImmunologyHaematopoiesisBiologyMyelopoiesisReprogrammingAdoptive cell transferStem cellImmunityProgenitor cellAcquired immune systemImmune systemBone marrowCell biologyT cellCellGenetics

Abstract

fetched live from OpenAlex

The dogma that adaptive immunity is the only arm of the immune response with memory capacity has been recently challenged by several studies demonstrating evidence for memory-like innate immune training. However, the underlying mechanisms and location for generating such innate memory responses in vivo remain unknown. Here, we show that access of Bacillus Calmette-Guérin (BCG) to the bone marrow (BM) changes the transcriptional landscape of hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs), leading to local cell expansion and enhanced myelopoiesis at the expense of lymphopoiesis. Importantly, BCG-educated HSCs generate epigenetically modified macrophages that provide significantly better protection against virulent M. tuberculosis infection than naïve macrophages. By using parabiotic and chimeric mice, as well as adoptive transfer approaches, we demonstrate that training of the monocyte/macrophage lineage via BCG-induced HSC reprogramming is sustainable in vivo. Our results indicate that targeting the HSC compartment provides a novel approach for vaccine development.Copyright © 2017 Elsevier Inc. All rights reserved. PMID: 29328912 Funding information This work was supported by: CIHR, Canada Grant ID: 232519 CIHR, Canada Grant ID: 301538 CIHR, Canada Grant ID: FDN-143273

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

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