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Record W2924589929 · doi:10.1111/nyas.14029

Sputum from chronic obstructive pulmonary disease patients inhibits T cell migration in a microfluidic device

2019· article· en· W2924589929 on OpenAlexafffund
Xiaoou Ren, Jiandong Wu, David B. Levin, Susy Santos, Ricardo Lobato de Faria, Michael Zhang, Francis Lin

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsSeven Oaks General HospitalVictoria General HospitalUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsCOPDSputumChemotaxisMedicinePathogenesisImmunologyCellLungAirwayPulmonary diseaseInternal medicinePathologyChemistryAnesthesia

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is a common lung disease characterized by narrowed airways, resulting in serious breathing difficulty. Previous studies have demonstrated that inflammatory infiltration of leukocytes in the airway is associated with the pathogenesis of COPD. In the present study, we employed a microfluidic approach to assess the effect of COPD sputum on activated human peripheral blood T cell migration and chemotaxis under well-controlled gradient conditions. Our results showed considerable basal migration of T cells derived from peripheral blood of COPD patients and healthy controls in the medium control groups. By contrast, the migration of T cells from COPD patients and healthy controls was significantly inhibited in the presence of a gradient of sputum supernatant from COPD patients. Furthermore, chemotaxis of T cells from COPD patients or healthy subjects toward an SDF-1α gradient was clearly inhibited by sputum samples from the COPD patients. The inhibition effect revealed by the microfluidic cell migration experiments provides new information about the complex involvement of T cell trafficking in COPD.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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