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Record W2086642580 · doi:10.1182/blood-2006-12-062695

Platelet-bound lipopolysaccharide enhances Fc receptor–mediated phagocytosis of IgG-opsonized platelets

2007· article· en· W2086642580 on OpenAlexafffund
John W. Semple, Rukhsana Aslam, Michael Kim, Edwin R. Speck, John Freedman

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsPediatric Oncology GroupCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
FundersCanadian Blood Services
KeywordsPhagocytosisPlateletAntibody opsonizationOpsoninLipopolysaccharideFc receptorImmunologyAutoantibodyAntibodyChemistryMicrobiologyBiology

Abstract

fetched live from OpenAlex

Platelets express Toll-like receptor 4 (TLR4), and this has been shown to be responsible for the thrombocytopenia induced by lipopolysaccharide (LPS) administration in vivo. We studied the role of LPS in mediating platelet phagocytosis by THP-1 cells in vitro by flow cytometry. Opsonization of platelets with an IgG monoclonal (W6/32) antibody or with IgG autoantibody-positive sera from patients with autoimmune thrombocytopenia (AITP) significantly enhanced platelet phagocytosis (P < .001). In contrast, platelet phagocytosis did not occur if platelets were bound with only LPS. If, however, the LPS-bound platelets were also opsonized with either W6/32 or autoantibody-positive sera with titers greater than 4, there was a significant and synergistic increase in Fc-dependent platelet phagocytosis (P < .001, P = .003, P = .048, and P = .047). These results suggest that, in the presence of antiplatelet antibodies, bacterial products can significantly alter platelet phagocytosis, and this may have relevance to how Gram-negative infections enhance platelet destruction in some patients with AITP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations142
Published2007
Admission routes2
Has abstractyes

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