Allergic Asthma and Rhinitis Caused by Household Rabbit Exposure: Identification of Serum-Specific IgE and Its Allergens

Although rabbits are common domestic pets, severe respiratory allergic reactions to rabbits in households are unusual. Ory c 1, a 17-kDa glycoprotein found in saliva and fur, has previously been identified as a major rabbit allergen. In this report, we describe the cases of three patients with rabbit allergy who presented with asthma and/or rhinitis while living in households with detectable levels of serum-specific IgE and major IgE binding components. Three patients with rabbit allergy and 18 unexposed nonatopic healthy controls were enrolled. Enzyme-linked immunosorbent assays (ELISA) for serum-specific IgE and IgG4 to rabbit epithelium and inhibition ELISA were performed followed by sodium dodecye sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and IgE immunoblotting. All three patients with rabbit allergy had high serum-specific IgE antibody levels compared with controls. The results of the inhibition ELISA showed significant inhibition with the addition of rabbit epithelium, whereas no significant inhibition was noted with the addition of cat and dog epithelia. Two IgE-binding components with molecular weights of 16 kDa and 67.5 kDa were identified by IgE immunoblotting. In conclusion, rabbit exposure may induce IgE-mediated bronchial asthma and/or rhinitis in domestic settings

Events as a tool for sales management at the instance of the Product and Brand Experience at BMW AG

nicht vorhande

Machine learning-based optimisation of Higgs coupling measurements in the H → 4l decay channel with ATLAS Run 3 data

Cross-section measurements for the different Higgs boson production and decay processes constitute a key area in the exploration of Higgs properties, with a high sensitivity to potential physics beyond the Standard Model. Due to its exceptionally clear signal, the decay of a Higgs boson into a ZZ$^*$ pair with a subsequent decay of each Z boson into two signature light leptons, H → ZZ$^∗$ → 4 l , is one of the most important channels for the Higgs property measurements. To increase the sensitivity of the Higgs cross-section measurements, the Simplied Template Cross Section (STXS) approach has been developed, where the final state is divided into exclusive phase space regions. Optimized classification of events according to kinematic production regions is vital to improve the signal sensitivity. The previous round of STXS measurements in the H → 4 l channel with the Run 2 ATLAS dataset employed a event classification using a Recurrent Neural Network (RNN) based approach. With the new Run 3 dataset at a centre-of-mass energy of 13.6 TeV, an alternative Neural Network approach based on permutation invariant Deep Sets is explored for this classification, which is motivated by permutation invariant symmetries between the H → ZZ$^∗$ → 4 l final state particles. A direct comparison between the predictive behaviour and the signal-/background separation is made between the Deep Set and RNN models in all STXS bins. Similar comparisons are made between Deep Sets trained on Run 2 and Run 3 data. The STXS classification schemes used in the RNN training during Run 2 and Run 3 are both considered for the Deep Set Neural Networks. Improved or comparable signal-to-background separations were observed for the Deep Set models compared to the RNN models in all kinematical STXS classification regions. Training on Run 3 data lead to improvements in the Deep Set performance compared to Run 2 data. The Run 2 iteration of the STXS binning scheme is in general observed to be more favourable for the Deep Set performance. The Deep Set approach can be considered as a viable alternative to the previous RNN approach

Machine learning-based optimisation of Higgs coupling measurements in the H → 4l decay channel with ATLAS Run 3 data

Cross-section measurements for the different Higgs boson production and decay processes constitute a key area in the exploration of Higgs properties, with a high sensitivity to potential physics beyond the Standard Model. Due to its exceptionally clear signal, the decay of a Higgs boson into a ZZ⋆ pair with a subsequent decay of each Z boson into two signature light leptons, H → ZZ∗ → 4l, is one of the most important channels for the Higgs property measurements. To increase the sensitivity of the Higgs cross-section measurements, the Simplified Template Cross Section (STXS) approach has been developed, where the final state is divided into exclusive phase space regions. Optimized classification of events according to kinematic production regions is vital to improve the signal sensitivity. The previous round of STXS measurements in the H → 4l channel with the Run 2 ATLAS dataset employed a event classification using a Recurrent Neural Network (RNN) based approach. With the new Run 3 dataset at a centre-of-mass energy of 13.6 TeV, an alternative Neural Network approach based on permutation invariant Deep Sets is explored for this classification, which is motivated by permutation invariant symmetries between the H → ZZ∗ → 4l final state particles. A direct comparison between the predictive behaviour and the signal-/background separation is made between the Deep Set and RNN models in all STXS bins. Similar comparisons are made between Deep Sets trained on Run 2 and Run 3 data. The STXS classification schemes used in the RNN training during Run 2 and Run 3 are both considered for the Deep Set Neural Networks. Improved or comparable signal-to-background separations were observed for the Deep Set models compared to the RNN models in all kinematical STXS classification regions. Training on Run 3 data lead to improvements in the Deep Set performance compared to Run 2 data. The Run 2 iteration of the STXS binning scheme is in general observed to be more favourable for the Deep Set performance. The Deep Set approach can be considered as a viable alternative to the previous RNN approach

Skin prick test extracts for dog allergy diagnosis show considerable variations regarding the content of major and minor dog allergens

&lt;i&gt;Background:&lt;/i&gt; Commercial skin prick test (SPT) extracts used for the diagnosis of dog allergy are prepared by extracting allergens from natural sources, e.g. dog hair and dander. Due to different starting material and extraction methods used, it is likely that extracts differ regarding their allergen contents. &lt;i&gt;Methods:&lt;/i&gt; The total protein content and composition of dog SPT extracts from 5 European manufacturers were compared by silver-stained SDS-PAGE. Specific antibody probes were generated to detect major and minor allergens in each extract by immunoblotting. Additionally, sera of patients suffering from dog allergy were used to detect dog allergens in SPT extracts. &lt;i&gt;Results:&lt;/i&gt; SPT extracts showed a 20-fold variation regarding the total protein content. The contents of the major dog allergen Can f 1 and of Can f 2 varied considerably between the extracts. In one of the extracts, neither Can f 1 nor Can f 2 could be detected by immunoblotting. The contents of the minor dog allergen Can f 3, albumin, also showed great variability. In one of the dog SPT extracts, the presence of human serum albumin (HSA) was detected with HSA-specific antibodies. &lt;i&gt;Conclusion:&lt;/i&gt; The observed variability of commercial dog SPT extracts regarding their allergen contents likely has a negative influence on the accuracy of diagnosis of dog allergy.</jats:p