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Enregistrement W4411550165 · doi:10.3389/fmed.2025.1642976

Editorial: Road trip from mild to severe asthmatic inflammation: the traffic lights of biomarkers in asthma management, volume II

2025· editorial· en· W4411550165 sur OpenAlexaff
Κonstantinos Porpodis, Paschalis Steiropoulos, Spyridon Gougousis, Harissios Vliagoftis, Kalliopi Domvri

Notice bibliographique

RevueFrontiers in Medicine · 2025
Typeeditorial
Langueen
DomaineMedicine
ThématiqueAsthma and respiratory diseases
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésAsthmaMedicineTraffic volumeInflammationAsthma managementVolume (thermodynamics)Internal medicineTransport engineeringEngineering

Résumé

récupéré en direct d'OpenAlex

There is still a clinical need to delineate complex endotypes of asthma and to identify novel biomarkers with high predictive and prognostic value to achieve an optimal personalized approach. This Research Topic, describes current topics in asthma biomarker research, providing a better understanding of the utility of the currently available biomarkers and the current biomarker research regarding asthma remission, and suggesting new approaches to use biomarkers in everyday clinical practice for optimal management of patients with asthma. Our issue assembles six high-quality articles describing the benefits of using biomarkers for asthma management.Within this context, Jingcheng Dong et al. explored the association between baseline Th2 biomarker levels and clinical manifestations in pediatric asthma and identified predictors of clinical remission. The study included 172 children and the authors evaluated a number of clinical parameters, incuding FeNO, blood eosinophils, and serum biomarkers (TSLP, IL-4/5/13, TARC, Periostin, IgE). The authors concluded that serum TSLP is independently associated with clinical remission in Th2-high pediatric asthma and integration with lung function and IgE may form a composite biomarker panel for remission evaluation. This stratification tool may guide asthma risk stratification and personalized disease management, but longitudinal studies are warranted to validate its prognostic utility.In a slightly different approach, given the important role of cytokines in asthma pathophysiology, Yansen Zheng et al. investigated the causal effects between cytokines and asthma, using the inverse variance weighted Mendelian randomization (MR) method. The MR analysis showed that levels of IL-5 and IL-9 were increased in asthma, indicating the downstream effects of IL-5 and IL-9 on asthma. Besides, they concluded that there was no evidence that cytokines increased or decreased the risk of asthma. Using similar methodology, Roan Eltigani Zaied et al. investigated the distinct and shared genetic risk factors contributing to the development of unspecified asthma (no age-specific), childhood onset asthma (COA) and adult-onset asthma (AOA). They employed a two-sample MR analysis to elucidate the causal association between genes within lung and whole-blood-specific gene regulatory networks (GRNs) and the development of unspecified asthma, COA, and AOA using the Wald ratio method. They identified genes (including ORMDL3, PEBP1P3) whose altered expression in lung or blood is putatively causally associated with unspecified asthma and two age-specific asthma presentations, proposing that the causal genes identified in this analysis hold promise as potential drug targets, emphasizing the need to consider the asthma subtype in the development of asthma drugs.Feng Xu et al. explored the relationship between the systemic immuneinflammation index (SII) and mortality in patients with asthma. The study included 6,156 participants from the National Health and Nutrition Examination Survey (NHANES) for US adults from 2001 to 2018. Subgroup analyses revealed SII's association with all-cause mortality across various demographics, including age, sex, race, education levels, smoking status, and marital status suggesting that SII may potentially serve as a predictive tool for evaluating asthma mortality rates. Similarly, Tulei Tian et al. analyzed data from 40,664 participants from NHANES to assess the relationship between SII and asthma and asthma-related events. They found that SII is positively correlated with the persistence of asthma, yet has limited predictive power for asthma recurrence, highlighting SII's potential as a tool for assessing asthma risk and formulating targeted management strategies.Both studies, by analyzing participants from NHANES revealed the potential use of SII in asthma management.In a broader population, Celeste M Porsbjerg et al. aimed to elucidate the association between individual biomarker levels or levels of biomarker combinations before initiation of a biologic with changes in asthma outcomes after therapy with a biologic in real-life. This was a registry-based, cohort study using data from 23 countries, which participate in the International Severe Asthma Registry (May 2017-February 2023); results from 3,751 patients that initiated biologics were included. They concluded that since higher baseline blood eosinophil count, FeNO and their combination can predict biologic-associated lung function improvement, earlier intervention in patients with impaired lung function or at risk of accelerated lung function decline with biologics may be beneficial.We believe that this Research Topic adds to the current literature and advances our understanding of the role of biomarkers in asthma management given the big cohorts analyzed. It is with great pleasure that we are presenting the articles included in this research Topic to the asthma research community.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,024
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0040,004
Bibliométrie0,0040,001
Études des sciences et des technologies0,0020,003
Communication savante0,0070,005
Science ouverte0,0050,001
Intégrité de la recherche0,0130,018
Charge utile insuffisante (le modèle a refusé de juger)0,0150,010

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,254
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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