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Record W4309688493 · doi:10.3390/antiox11112302

Myeloperoxidase: Regulation of Neutrophil Function and Target for Therapy

2022· review· en· W4309688493 on OpenAlexafffund
Salma A. Rizo-Téllez, Meriem Sekheri, János G. Filep

Bibliographic record

VenueAntioxidants · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersCanadian Institutes of Health Research
KeywordsMyeloperoxidaseAzurophilic granuleNeutrophil extracellular trapsInnate immune systemCell biologyPhagocytosisBiologyImmune systemImmunityImmunologyInflammation

Abstract

fetched live from OpenAlex

Neutrophils, the most abundant white blood cells in humans, are critical for host defense against invading pathogens. Equipped with an array of antimicrobial molecules, neutrophils can eradicate bacteria and clear debris. Among the microbicide proteins is the heme protein myeloperoxidase (MPO), stored in the azurophilic granules, and catalyzes the formation of the chlorinating oxidant HOCl and other oxidants (HOSCN and HOBr). MPO is generally associated with killing trapped bacteria and inflicting collateral tissue damage to the host. However, the characterization of non-enzymatic functions of MPO suggests additional roles for this protein. Indeed, evolving evidence indicates that MPO can directly modulate the function and fate of neutrophils, thereby shaping immunity. These actions include MPO orchestration of neutrophil trafficking, activation, phagocytosis, lifespan, formation of extracellular traps, and MPO-triggered autoimmunity. This review scrutinizes the multifaceted roles of MPO in immunity, focusing on neutrophil-mediated host defense, tissue damage, repair, and autoimmunity. We also discuss novel therapeutic approaches to target MPO activity, expression, or MPO signaling for the treatment of inflammatory and autoimmune diseases.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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)0.0020.000

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 head0.063
GPT teacher head0.299
Teacher spread0.236 · 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.

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

Citations122
Published2022
Admission routes2
Has abstractyes

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