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Record W1980505425 · doi:10.1186/1710-1492-6-s3-p7

Flow cytometry to identify leukocyte sub-populations in blood and induced sputum in asthmatic and healthy volunteers exposed to diesel exhaust

2010· article· en· W1980505425 on OpenAlexaffvenue
Mandy Pui, John C. Lay, Neil E. Alexis, Christopher Carlsten

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSputumFlow cytometryDiesel exhaustImmunologyMedicineAsthmaDiesel fuelChemistryPathologyTuberculosis

Abstract

fetched live from OpenAlex

To identify five leukocyte types (in blood and induced sputum) and bronchial epithelial cells (in sputum only) using multi-colour flow cytometry in healthy and mildly asthmatic volunteers exposed to diesel exhaust. Mild asthmatics and normal controls were recruited as study subjects. This crossover study was double-blinded, randomized and counter-balanced to the order of three conditions: diesel exhaust with anti-oxidant, diesel exhaust with placebo, or filtered air with placebo. The subjects were exposed to either filtered air or diesel exhaust (300 ug PM 2.5 /m ) in a state-of-the-art diesel exhaust exposure facility. An anti-oxidant, N-acetylcysteine (600 mg), or a placebo was taken orally for five days preceding, and on the day of the exposure. Each subject was exposed to each of the three conditions. Peripheral blood samples were taken pre-exposure, and also at 2, 6, and 30 hours after the beginning of exposure. Sputum induction was performed by inhalation of hypertonic saline according to ATS guidelines pre-exposure, and also at 6, and 30 hours after the beginning of exposure. FACSCanto II (BD Biosciences) was used for flow cytometry. A 5-colour, 12-marker (CD3/CD9/CD14/CD16/CD19/CD20/CD45/CD56/CD83/CD206/CD326/HLA-DR) combination was used to identify dendritic cells, macrophages, monocytes, neutrophils, eosinophils, and bronchial epithelial cells. Direct immunolabelling was performed on whole peripheral blood. After incubation, red blood cells were lysed. Remaining cells were washed and resuspended in PBS with 0.5% paraformaldehyde. Sputum plugs were homogenized with 0.1% DTT, filtered, and then centrifuged to remove supernatant. Sputum cells were resuspended in PBS at 1 million per mL. Direct immunolabelling was performed. After incubation, cells were washed and resuspended in PBS with 0.5% paraformaldehyde. Spectral compensation for flow cytometry was performed using an automatic calibration technique (BD CompBeads). Cellular debris was eliminated on the SSC/FSC scattergram. A gating strategy was designed to identify the leukocyte sub-populations and bronchial epithelial cells. Surface markers were chosen based on differential cell-specific expression according to existing literature.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.391
Teacher spread0.319 · 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 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

Citations6
Published2010
Admission routes2
Has abstractyes

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