The latest medical research on Pediatric Allergy & Immunology

Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not.

We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models.

For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level.

We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

Based on shared decision-making (SDM) principles, a decision aid was previously developed to help patients, their caregivers, and physicians decide which peanut allergy management approach best suits them. This study refined the decision aid's content to better reflect patients' and caregivers' lived experience.

Current standard of care for peanut allergy is avoidance, although peanut oral immunotherapy has been approved by the Food and Drug Administration for use in patients 4-17 years old. An advisory board of allergy therapy experts (n = 3) and patient advocates (n = 3) informed modifications to the decision aid. The revised tool underwent cognitive debriefing interviews (CDIs) among adolescents (12-17 years old) with peanut allergy and caregivers of patients 4-17 years old with peanut allergy to evaluate its relevance, understandability, and usefulness. The 20 CDI participants understood the information presented in the SDM tool and reported it was important and relevant. Some revisions were made based on participant feedback. Results support content validity of the Peanut Allergy Treatment SDM Tool.

Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring.

A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023.

27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound.

Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0% to 100.0%, with a median of 90.3%; specificity ranged from 67.0% to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified.

This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.

Historically, systemic treatments for atopic dermatitis (AD) primarily consisted of immunosuppressive agents such as corticosteroids and Disease Modifying Antirheumatic Drugs (DMARDS), which provided symptomatic relief but often had long-term adverse effects. Newer treatments have shown significant efficacy with less side effects in clinical trials. This review discusses and compares conventional and newer systemic treatments for AD.

Newer medications for AD including dupilumab, tralokinumab, lebrikizumab, and oral JAK inhibitors have been shown to be safe and efficacious. High dose cyclosporine and dupilumab were more effective than methotrexate and azathioprine in improving clinical signs of AD. High-dose upadacitinib was shown in another meta-analysis to be most effective in the measured outcomes but had the highest frequency of adverse events. Targeted biologic treatments are increasingly favored over traditional immunosuppressive treatments of AD. Treatment can be individualized based on potency, adverse side effects, mechanism of action, and administration preference. Ongoing research continues to expand treatment options for AD.