International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Implications of peak selection in the interpretation of unsupervised mass spectrometry imaging data analyses

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: -distributed stochastic neighbor embedding and -means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data

Early life exposure to dietary and inhalant allergens

Increasing evidence points to early life, including fetal life, as being a critical time period during which the infant's allergic fate may be determined. However, initial antigen priming in utero necessitates fetal exposure to maternally derived allergen. This article reviews our current knowledge regarding materno-fetal allergen transfer in vivo and reflects on mechanisms by which this exposure might exert immunomodulatory influences on the developing immune system

Using monazite and zircon petrochronology to constrain the <i>P–T–t</i> evolution of the middle crust in the Bhutan Himalaya

The growth and dissolution behaviour of accessory phases (and especially those of geochronological interest) in metamorphosed pelites depends on, among others, the bulk composition, the prograde metamorphic evolution and the cooling path. Monazite and zircon are arguably the most commonly used geochronometers for dating felsic metamorphic rocks, yet crystal growth mechanisms as a function of rock composition, pressure and temperature are still incompletely understood. Ages of different growth zones in zircon and monazite in a garnet-bearing anatectic metapelite from the Greater Himalayan Sequence in NW Bhutan were investigated via a combination of thermodynamic modelling, microtextural data and interpretation of trace-element chemical ‘fingerprint’ indicators in order to link them to the metamorphic stage at which they crystallized. Differences in the trace-element composition (HREE, Y, EuN/Eu*N) of different phases were used to track the growth/dissolution of major (e.g. plagioclase, garnet) and accessory phases (e.g. monazite, zircon, xenotime, allanite). Taken together, these data constrain multiple pressure–temperature–time (P–T–t) points from low temperature (700 °C) conditions. The results suggest that the metapelite experienced a cryptic early metamorphic stage at c. 38 Ma at T, medium-pressure (~600 °C, 0.55 GPa) evolution at 35–29 Ma during which the garnet grew, and subsequent partial melting at >690 °C and >18 Ma. Our data confirm that both geochronometers can crystallize independently at different times along the same P–T path and that neither monazite nor zircon necessarily provides timing constraints on ‘peak’ metamorphism. Therefore, collecting monazite and zircon ages as well as major and trace-element data from major and accessory phases in the same sample is essential for reconstructing the most coherent metamorphic P–T–t evolution and thus for robustly constraining the rates and timescales of metamorphic cycles