ArticleMolecular diagnosis & therapy2021
Treatable Mechanisms in Asthma.
Article in Molecular diagnosis & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 27 citations in OpenAlex.
- Targeted biologic therapies and advanced drug delivery approaches in asthma management: a clinical perspective.Inflammopharmacology · 2025Review
- Endotype-driven decisions in choosing a biologic for airway diseases.Frontiers in allergy · 2025Review
- Results from a UK consensus about the optimal prescribing of medium strength triple therapy in uncontrolled adult asthma patients in the NHS.Journal of family medicine and primary care · 2024Article
- Relationship Between Asthma Control Status and Health-Related Quality of Life in Japan: A Cross-Sectional Mixed-Methods Study.Advances in therapy · 2023Article
- Might It Be Appropriate to Anticipate the Use of Long-Acting Muscarinic Antagonists in Asthma?Drugs · 2023Article
- Molecular Accounting and Profiling of Human Respiratory Microbial Communities: Toward Precision Medicine by Targeting the Respiratory Microbiome for Disease Diagnosis and Treatment.International journal of molecular sciences · 2023Review
- Investigational Treatments in Phase I and II Clinical Trials: A Systematic Review in Asthma.Biomedicines · 2022Review
- Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma.Journal of personalized medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asthma is a heterogeneous condition, but firm identification of heterogeneity-focused treatments is still lacking. Dividing patients into subgroups of asthma pheno-/endotypes based on combined clinical and cellular biological characteristics and linking them to targeted treatments could be a potentially useful approach to personalize therapy for better outcomes. Nonetheless, there are still many problems related to the identification and validation of asthma phenotypes and endotypes. Alternatively, a precision-medicine strategy for the management of patients with airways disease that is free from the traditional diagnostic labels and based on identifying "treatable traits" in each patient might be preferable. However, it would represent a quite unsophisticated approach because the definition of a treatable trait is too imprecise. In fact, there is still no understanding of the mechanisms underlying treatable traits that allow directing any targeted therapies against any particular treatable trait. Fortunately, in-depth identification of underlying molecular pathways to guide targeted treatment in individual patients is in progress thanks to the improvement in big data management obtained from '-omic' sciences that is greatly increasing knowledge concerning asthma.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.