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Age-associated inflammation enhances macrophage-pathogen interactions in vitro. (INC7P.400)

2014· article· en· W1517390721 on OpenAlexaff
Joanne Turner

Bibliographic record

VenueThe Journal of Immunology · 2014
Typearticle
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsInflammationImmune systemProinflammatory cytokineImmunologyMacrophageInnate immune systemBiologyLungPhospholipidosisIn vitroMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Increasing age is associated with elevated levels of circulating pro-inflammatory cytokines, termed inflammaging. This inflammatory state can modulate a variety of immune functions and contribute to the increased susceptibility of the elderly to numerous infection diseases. Using Mycobacterium tuberculosis (M.tb) as a model pathogen we evaluated the influence of inflammation on macrophage function in vitro using the aged mouse model. We detected elevated basal levels of inflammatory cytokines in the lungs of naïve old mice, and pulmonary macrophages were shown to be in a pre-activated state(elevated mRNA for IRF-1, IRGM1 and CIITA). Furthermore, lung macrophages from old mice had altered trafficking marked by enhanced phago-lysosome (P-L) fusion upon infection with M.tb in vitro, which was refractory to enhancement by IFN-gamma activation. When old mice were placed on a diet of the non-steroidal anti-inflammatory drug (NSAID) ibuprofen for 2 weeks prior to isolation of lung macrophages, this pre-activated phenotype was reversed in old mice. These data demonstrate that the hyper-inflammatory state of the lung in old mice can influence the early interactions of immune cells with M.tb and that this phenotype can be reversed by NSAIDs. These data suggest that inflammaging in the lung could have a beneficial impact on innate immune function during infection, but the long term consequences of this phenotype in the context of M.tb infection are currently unknown.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes1
Has abstractyes

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