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Record W4315700404 · doi:10.1111/all.15643

The Molecular Allergology User's Guide version 2.0 is freely available!

2023· editorial· en· W4315700404 on OpenAlexfundno aff
Karin Hoffmann‐Sommergruber, Alexandra F. Santos, Heimo Breiteneder

Bibliographic record

VenueAllergy · 2023
Typeeditorial
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsnot available
FundersNorman Cousins Center for PsychoneuroimmunologyMedical Research CouncilAsthma and Lung UKMedical Research Council CanadaNational Institute on Handicapped Research
KeywordsMedicineClinical immunologyAllergyMedical educationTask forceFamily medicineLibrary scienceComputer scienceImmunologyPolitical science

Abstract

fetched live from OpenAlex

The need to improve the care of allergic patients and to provide up-to-date education of healthcare professionals and researchers about Molecular Allergology led to the idea of compiling existing knowledge on allergen molecules based on initial concepts proposed by Adriano Mari. In 2015, during a 3 day workshop in Berlin, Paolo Matricardi, Stephanie Dramburg, and Markus Ollert gathered an expert team of more than 50 enthusiastic authors from over 15 countries to start this task. As a result, the first edition of the Molecular Allergology User's Guide (MAUG 1.0) was launched during the Annual Congress of the European Academy of Allergy and Clinical Immunology (EAACI) in Vienna, Austria, in 2016. This guide was designed for both clinicians and scientists to familiarize themselves with the seemingly overwhelming list of different allergen molecules available for testing and provided diagnostic algorithms for health professionals. The print version of the book published by the EAACI soon became a classic for anyone interested in Molecular Allergology as well as a collector's item (Figure 1). In addition, a concise version of the guide's text was published as a supplement of the journal Pediatric Allergy and Immunology.1 The MAUG 1.0 guide had sufficient worldwide impact on the discipline of Molecular Allergology as it not only provided well-structured information on physico-chemical and biological features of allergenic proteins, their sources and nomenclature but also information on their use as diagnostic reagents in clinical practice. This way, the guide aimed to bridge the gap between scientific research and clinical practice.2, 3 The production of this kind of handbook was very much supported by the EAACI as most allergic diseases were covered, as well as the potential roles of the most important allergens in a diagnostic work-up. Due to the rapidly evolving field of Molecular Allergology, even then, an update was envisioned to map the progress that would certainly be achieved in the years to come. Since 2016, science has indeed evolved further and new allergen sources and allergenic proteins have been identified.4 New diagnostic methods have subsequently been developed and old ones had to be adapted. With such methods, detailed studies on allergen-specific immune responses leading to an allergic reaction became feasible and new clinical studies were conducted.2 Furthermore, new findings on allergen protein families and their relevance for immune responses challenged previously developed diagnostic algorithms, for both diagnosis and allergen-specific immunotherapy.2-4 Therefore, the need for an updated version became apparent, and a dedicated task force for “The Molecular Allergology User's Guide 2.0” (MAUG 2.0) was proposed and approved by the EAACI Executive Committee in 2021. This undertaking was accomplished through a close interaction between clinicians and scientists with the support of the EAACI leadership. Under the guidance of the editorial team (Figure 2), formed by Karin Hoffmann-Sommergruber, Christiane Hilger, Stephanie Dramburg, Alexandra Santos, and Leticia de las Vecillas, more than 100 authors contributed with their expertise, enthusiasm and data to produce the update version of MAUG: the MAUG 2.0. The team of authors included not only the ones that were part of the first edition but also many additional authors for the current edition. Among all contributors, the support from Paolo Matricardi who was the driving force behind the first edition and also actively engaged in the second edition, needs to be acknowledged. The new MAUG 2.0 provides state-of-the-art information on allergen molecules, their clinical relevance and application in diagnostic algorithms for clinical practice in a patient-tailored manner. MAUG 2.0 also summarizes the current understanding of the role of co-factors during an allergic response and introduces the current knowledge of the underlying molecular mechanisms of this immune reaction. Like its predecessor MAUG 1.0, this handbook is designed for both, clinicians and scientists to make sense of the ever expanding list of different allergen molecules available for testing and by providing updated and new diagnostic algorithms for health professionals. Furthermore, this hand book gives an overview on the basic mechanisms of test formats and the biology of allergen molecules and the application of allergen tests for exposure. It includes 45 chapters, 93 Tables, 200 figures, and more than 1000 references. MAUG 2.0 was launched during the Annual Congress of the EAACI in Prague, Czech Republic, in 2022 (Figure 2) and can be freely downloaded from the EAACI website. (https://hub.eaaci.org/resources_guidelines/molecular-allergology-users-guide-2-0/). As happened with the first version, MAUG 2.0 will be published as a supplement of the journal Pediatric Allergy and Immunology and can be cited as such.[doi:10.1111/pai.13854]. MAUG 2.0 will undoubtedly continue to be the reference book for Molecular Allergology and will encourage further advances in this exciting discipline in the future. Dr. Hoffmann-Sommergruber reports an unrestricted grant from ThermoFisher, Hycor, MADX, InBio, and EroImmun to EAACI for typesetting and layout design for MAUG 2.0 and personal research support from the Government of Lower Austria (DARC Project- subproject 7). Dr. Santos reports grants and personal fees from Medical Research Council; grants from Asthma UK, Immune Tolerance Network/National Institute of Allergy and Infectious Diseases (NIAID,NIH), Food Allergy Research and Education (FARE), BBSRC and Rosetrees Trust; personal fees from Thermo Scientific, Nutricia, Infomed, Novartis, Allergy Therapeutics, Buhlmann, as well as research support from Buhlmann and Thermo Fisher Scientific through a collaboration agreement with King's College London. Dr. Breiteneder declares no conflict of interest.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.008

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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