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Effects of low doses of inhaled fluticasone propionate on inflammation and remodelling in persistent‐mild asthma

2005· article· en· W2051646880 on OpenAlexaff
A M Vignola, Loredana Riccobono, Mirella Profita, Antonio Foresi, R. Di Giorgi, Daniela Guerrera, Mark Gjomarkaj, P. Di Blasi, Pier Luigi Paggiaro

Bibliographic record

VenueAllergy · 2005
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsSputumMedicineFluticasone propionateEosinophilFluticasoneEosinophil cationic proteinAsthmaInflammationEosinophiliaImmunologyInternal medicineGastroenterologyPathologyTuberculosis

Abstract

fetched live from OpenAlex

In asthma a dysregulation of eosinophil apoptosis and an imbalance of metalloproteinase-9 (MMP-9) and tissue inhibitor metalloproteinase-1 (TIMP-1) play an important role in airway inflammation and remodelling. We evaluated the effects of a low dose of inhaled fluticasone proprionate (FP) (100 microg bid by Diskus) for 4 weeks in 24 steroid naive patients with mild persistent asthma, symptomatic and with a sputum eosinophilia >or=3% on clinical outcomes and inflammatory markers such as the induced sputum eosinophils, the induced sputum apoptotic eosinophils, the levels of MMP-9 and TIMP-1 and their molar ratio in the induced sputum supernatants. After FP treatment forced expiratory volume (FEV1) and FEV1/forced vital capacity values, PEF (L/min), sputum apoptotic eosinophils, and MMP-9/TIMP-1 molar ratio in sputum supernatants of asthmatic subjects were significantly increased in comparison with baseline, while sputum eosinophils significantly decreased. Change (Delta) in FEV1 after treatment with FP negatively correlated with the Delta in sputum eosinophils, while the Delta in MMP-9 values positively correlated with Delta in TIMP-1 values. This study shows that the clinical improvement achieved by the use of low doses of FP in asthmatics is related, at least in part, to the resolution of eosinophilic inflammation and the downregulation of remodelling markers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.235
Teacher spread0.226 · 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 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

Citations22
Published2005
Admission routes1
Has abstractyes

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