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Record W4221026495 · doi:10.1128/iai.00522-21

Surveying the Epigenetic Landscape of Tuberculosis in Alveolar Macrophages

2022· review· en· W4221026495 on OpenAlex
Katrina Madden, Yi Chu Liang, Nusrah Rajabalee, Gonzalo G. Alvarez, Jim Sun

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInfection and Immunity · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsOttawa HospitalInstitute of Infection and ImmunityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Sanitarium AssociationCanadian Institutes of Health ResearchGovernment of OntarioUniversity of Ottawa
KeywordsBiologyTuberculosisEpigeneticsImmunologyPathologyGeneticsMedicine

Abstract

fetched live from OpenAlex

, small-animal, or blood-derived cell models, which do not accurately reflect the pulmonary nature of the disease. In humans, the first and major target cells of Mycobacterium tuberculosis are alveolar macrophages (AM). As such, their response to infection and treatment is clinically relevant and ultimately drives the outcome of disease. In this review, we compare the fundamental differences between AM and circulating monocyte-derived macrophages in the context of TB and summarize the recent advances in elucidating the epigenomes of these cells, including changes to the transcriptome, DNA methylome, and chromatin architecture. We will also discuss trained immunity in AM as a new and emerging field in TB research and provide some perspectives for the translational potential of targeting host epigenetics as an alternative TB therapy.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.317
Teacher spread0.275 · 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