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Record W1592766204 · doi:10.1186/s12863-015-0224-x

Evaluation of host genetics on outcome of tuberculosis infection due to differences in killer immunoglobulin-like receptor gene frequencies and haplotypes

2015· article· en· W1592766204 on OpenAlexaff
Kali Braun, Joyce Wolfe, Sandra Kiazyk, Meenu K. Sharma

Bibliographic record

VenueBMC Genetics · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsPublic Health Agency of CanadaUniversity of Manitoba
Fundersnot available
KeywordsHaplotypeBiologyTuberculosisGeneMycobacterium tuberculosisImmunologyImmune systemVirulenceGeneticsDiseasePathogenesisAntibodyGenotypeMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Outcome of Mycobacterium tuberculosis (Mtb) infection is affected by virulence of the infecting strain of Mtb, host environment, co-morbidities, and the genetic composition of the host, specifically the presence or absence of genes involved in immune responses/regulation. It is hypothesized that specific killer immunoglobulin-like receptor (KIR) genes may be associated with Mtb infection and clinical outcome. This cross-sectional study examined the KIR gene frequencies, profiles, and haplotypes of individuals with active tuberculosis, latent tuberculosis infection, compared to TB and HIV negative healthy controls. RESULTS: Analysis of KIR gene frequencies revealed differences among disease status groups, suggesting that enrichment or depletion of specific KIR genes may direct the disease outcome. Mtb infected individuals were more likely to have a centromeric-AA haplotype compared to controls. CONCLUSION: The differences in KIR gene frequencies and haplotypes may result in differential cytokine expression, contributing to different disease outcomes, and suggest a genetic influence on Mtb susceptibility and pathogenesis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.079
GPT teacher head0.294
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2015
Admission routes1
Has abstractyes

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