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Record W2791220190 · doi:10.3390/ijms19020582

Lung Macrophage Phenotypes and Functional Responses: Role in the Pathogenesis of COPD

2018· review· en· W2791220190 on OpenAlexaff
Kei Yamasaki, Stephan F. van Eeden

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCOPDLungImmunologyPhenotypeImmune systemInnate immune systemMacrophageBiologyPhagocytosisAcquired immune systemPathogenesisMedicineIn vitroGene

Abstract

fetched live from OpenAlex

Lung macrophages (LMs) are essential immune effector cells that are pivotal in both innate and adaptive immune responses to inhaled foreign matter. They either reside within the airways and lung tissues (from early life) or are derived from blood monocytes. Similar to macrophages in other organs and tissues, LMs have natural plasticity and can change phenotype and function depending largely on the microenvironment they reside in. Phenotype changes in lung tissue macrophages have been implicated in chronic inflammatory responses and disease progression of various chronic lung diseases, including Chronic Obstructive Pulmonary Disease (COPD). LMs have a wide variety of functional properties that include phagocytosis (inorganic particulate matter and organic particles, such as viruses/bacteria/fungi), the processing of phagocytosed material, and the production of signaling mediators. Functioning as janitors of the airways, they also play a key role in removing dead and dying cells, as well as cell debris (efferocytic functions). We herein review changes in LM phenotypes during chronic lung disease, focusing on COPD, as well as changes in their functional properties as a result of such shifts. Targeting molecular pathways involved in LM phenotypic shifts could potentially allow for future targeted therapeutic interventions in several diseases, such as COPD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.317
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations141
Published2018
Admission routes1
Has abstractyes

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