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Record W2995065520 · doi:10.1038/s42003-019-0662-5

SP-D attenuates LPS-induced formation of human neutrophil extracellular traps (NETs), protecting pulmonary surfactant inactivation by NETs

2019· article· en· W2995065520 on OpenAlexafffund
Raquel Arroyo, Meraj A. Khan, Mercedes Echaide, Jesús Pérez‐Gil, Nades Palaniyar

Bibliographic record

VenueCommunications Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCIHR Skin Research Training CentreEuropean Regional Development FundHospital for Sick ChildrenCystic Fibrosis CanadaNatural Sciences and Engineering Research Council of CanadaMitacsGovernment of Canada
KeywordsNeutrophil extracellular trapsPulmonary surfactantExtracellularChemistryCell biologyBiophysicsImmunologyBiologyBiochemistryInflammation

Abstract

fetched live from OpenAlex

Abstract An exacerbated amount of neutrophil extracellular traps (NETs) can cause dysfunction of systems during inflammation. However, host proteins and factors that suppress NET formation (NETosis) are not clearly identified. Here we show that an innate immune collectin, pulmonary surfactant protein-D (SP-D), attenuates lipopolysaccharide (LPS)-mediated NETosis in human neutrophils by binding to LPS. SP-D deficiency in mice ( Sftpd −/− ) leads to excess NET formation in the lungs during LPS-mediated inflammation. In the absence of SP-D, NETs inhibit the surface-active properties of lung surfactant, essential to prevent the collapse of alveoli, the air breathing structures of the lungs. SP-D reverses NET-mediated inhibition of surfactant and restores the biophysical properties of surfactant. To the best of our knowledge, this study establishes for the first time that (i) SP-D suppresses LPS-mediated NETosis, (ii) NETs inhibit pulmonary surfactant function in the absence of SP-D, and (iii) SP-D can restore NET-mediated inhibition of the surfactant system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.561

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 science0.0010.000
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 head0.097
GPT teacher head0.390
Teacher spread0.293 · 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

Citations54
Published2019
Admission routes2
Has abstractyes

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