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Flow cytometric analysis of defensins in blood and marrow neutrophils

2000· article· en· W2001492984 on OpenAlexafffund
M. Emilia Klut, Beth A. Whalen, James C. Hogg

Bibliographic record

VenueEuropean Journal Of Haematology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsBone marrowFlow cytometryMyeloidGranulocyteBiologyMolecular biologyImmunologyMyeloperoxidaseAlkaline phosphataseChemistryInflammationBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Polymorphonuclear neutrophils (PMN) are vital in host defense against microbial infections. This study provides a flow cytometric method for the quantitative analysis of microbicidal peptides (defensins) in cells of PMN lineage. Rabbit neutrophil peptides, NP-2 and NP-5, were measured in all PMN and in subpopulations of PMN expressing 1-selectin. PMN lineage counts were made on Wright's-stained blood smears and marrow cytospins. Immunoreactivity for NP-2, and NP-5 was detected by using the alkaline phosphatase anti-alkaline phosphatase technique. The results show that marrow PMN express higher levels of NP-2 and NP-5 than blood PMN, p < 0.001 and that these levels are associated with elevated numbers of myeloid precursors. In both blood and marrow, NP-2 occurs in two PMN subpopulations and the mean fluorescence intensity of NP-2 is consistently higher than that of NP-5. Increased levels of defensins are observed in circulating PMN depicting the most 1-selectin p < 0.05. Immunocytochemical results indicate that PMN defensins reside in cytoplasmic granules and are not constitutively expressed on the cell surface. Furthermore, defensins are not detected in monocytes, eosinophils, lymphocytes and erythrocytes. The flow cytometric method described here provides a novel means of quantitating host natural defenses, allows the characterization of PMN subpopulations and has clinical applications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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 designObservational
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

Citations8
Published2000
Admission routes2
Has abstractyes

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