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Record W3161976706 · doi:10.1182/blood.2020009417

Upregulated PD-L1 delays human neutrophil apoptosis and promotes lung injury in an experimental mouse model of sepsis

2021· article· en· W3161976706 on OpenAlexaff
Jiafeng Wang, Jian Xie, Zhenzhen Zhao, Sahil Gupta, Yu Guo, Songhui Jia, Jean Parodo, John C. Marshall, Xiaoming Deng

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Natural Science Foundation of China
KeywordsSepsisTumor necrosis factor alphaProtein kinase BDownregulation and upregulationImmunologyApoptosisLungPI3K/AKT/mTOR pathwayMedicineCancer researchFas ligandBiologyProgrammed cell deathInternal medicine

Abstract

fetched live from OpenAlex

PD-L1 is a ligand for PD-1, and its expression has been shown to be upregulated in neutrophils harvested from septic patients. However, the effect of PD-L1 on neutrophil survival and sepsis-induced lung injury remains largely unknown. In this study, PD-L1 expression correlated negatively with rates of apoptosis in human neutrophils harvested from patients with sepsis. Coimmunoprecipitation assays on control neutrophils challenged with interferon-γ and LPS showed that PD-L1 complexes with the p85 subunit of phosphatidyl 3-kinase (PI3K) to activate AKT-dependent survival signaling. Conditional CRE/LoxP deletion of neutrophil PD-L1 in vivo further protected against lung injury and reduced neutrophil lung infiltration in a cecal ligation and puncture (CLP) experimental sepsis animal model. Compared with wild-type animals, PD-L1-deficient animals presented lower levels of plasma tumor necrosis factor-α and interleukin-6 (IL-6) and higher levels of IL-10 after CLP, and reduced 7-day mortality in CLP PD-L1-knockout animals. Taken together, our data suggest that increased PD-L1 expression on human neutrophils delays cellular apoptosis by triggering PI3K-dependent AKT phosphorylation to drive lung injury and increase mortality during clinical and experimental sepsis.

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)0.000
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 score0.091
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.048
GPT teacher head0.326
Teacher spread0.278 · 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

Citations209
Published2021
Admission routes1
Has abstractyes

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