Leukocyte telomere length and mycophenolate therapy in chronic hypersensitivity pneumonitis
Notice bibliographique
Résumé
Recent prospective clinical trials have shown antifibrotic therapies slow lung function decline in patients with idiopathic pulmonary fibrosis (IPF) [1, 2] and progressive fibrosing interstitial lung disease (ILD). Similar findings were demonstrated in scleroderma-associated ILD [3] despite use of the immunosuppressive therapy mycophenolate mofetil (MMF). Prospective data for the treatment of other forms of ILD, such as chronic hypersensitivity pneumonitis (CHP) are lacking. Our groups previously reported that the treatment of CHP with MMF was associated with a decreased incidence of adverse events, a reduction in prednisone dose, and improved lung function when compared to prednisone alone [4, 5], but prospective studies are needed to confirm these findings. Short leukocyte telomere length (TL) is associated with increased mortality in patients with ILD, including CHP and IPF [6–8]. A recent investigation also showed TL may influence the response to immunosuppressive therapy. In that study, patients with IPF and short TL had a higher risk of death, lung transplantation, and forced vital capacity (FVC) decline, when exposed to immunosuppressive therapy, including MMF [9]. In this investigation we sought to determine whether similar findings occurred in patients with CHP. We hypothesised that patients with CHP and short TL would experience a higher prevalence of death and disease progression when compared to those with longer TL. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. Arinze reports grants from MSD, outside the submitted work;. Conflict of interest: Dr. Adegunsoye reports personal fees from Genentech, personal fees from Boehringer Ingelheim, outside the submitted work;. Conflict of interest: Dr. Morisset has nothing to disclose. Conflict of interest: Dr. Newton has nothing to disclose. Conflict of interest: Dr. Oldham reports personal fees from Boehringer Ingelheim, personal fees from Genentech, outside the submitted work;. Conflict of interest: Dr. Vittinghoff has nothing to disclose. Conflict of interest: Dr. Linderholm has nothing to disclose. Conflict of interest: Dr. Strek reports grants from Boehringer Ingelheim, grants from Galapagos, grants from Novartis, outside the submitted work;. Conflict of interest: Dr. Noth reports grants and personal fees from Boehringer Ingelheim, personal fees from Intermune, personal fees from Anthera, personal fees from GSK, personal fees from Immuneworks, outside the submitted work;. Conflict of interest: Dr. Garcia has nothing to disclose. Conflict of interest: Dr. Wolters reports grants and personal fees from Boehringer Ingelheim, personal fees from Blade therapeutics, personal fees from Roche, grants from Genentech, outside the submitted work;. Conflict of interest: Dr. Ley reports grants from Nina Ireland Program for Lung Health, during the conduct of the study; personal fees from Genentech, outside the submitted work;.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».