Fully bioresorbable hybrid opto-electronic neural implant system for simultaneous electrophysiological recording and optogenetic stimulation.

Bioresorbable neural implants based on emerging classes of biodegradable materials offer a promising solution to the challenges of secondary surgeries for removal of implanted devices required for existing neural implants. In this study, we introduce a fully bioresorbable flexible hybrid opto-electronic system for simultaneous electrophysiological recording and optogenetic stimulation. The flexible and soft device, composed of biodegradable materials, has a direct optical and electrical interface with the curved cerebral cortex surface while exhibiting excellent biocompatibility. Optimized to minimize light transmission losses and photoelectric artifact interference, the device was chronically implanted in the brain of transgenic mice and performed to photo-stimulate the somatosensory area while recording local field potentials. Thus, the presented hybrid neural implant system, comprising biodegradable materials, promises to provide monitoring and therapy modalities for versatile applications in biomedicine.

Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces.

A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject's mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system's reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).

Surveillance of home environment in children with atopic dermatitis: a questionnaire survey.

BACKGROUND: The increasing prevalence of atopic dermatitis (AD) suggests a role for environmental factors in triggering a genetic predisposition in sufferers. OBJECTIVE: The purpose of this study was to investigate home environmental factors related to AD severity. METHODS: We conducted a questionnaire survey about the home environmental factors in 380 children from two daycare centers and the Samsung Medical Center outpatient clinic. AD was diagnosed by Hanifin and Rajka's criteria and its severity was assessed by the Severity Scoring of Atopic Dermatitis index. Children were divided into normal control group, mild AD group and severe AD group. Home environmental factors were compared among the three groups and were statistically analyzed using analysis of variance, Chi-square test, Fisher's exact test, and multiple logistic analysis. RESULTS: Indoor remodeling activities, such as painting (p = 0.004), floor covering (p = 0.001) and wallpaper changing (p = 0.002) were associated with severity of AD. Those in the severe AD group were more likely to live in an apartment (p < 0.001). Severe AD was observed more frequently when the monthly income of household (p = 0.027) and final educational status of mother (p = 0.001) were higher. CONCLUSION: Some home environmental factors were associated with AD severity, but its causal relationship is not clear. Further research is needed to confirm these associations and to clarify whether they are causative.

The clinical effects of hospitalization in a low pollutant room on atopic dermatitis.

BACKGROUND: Environmental pollutants are thought to be one of major triggers of atopic dermatitis (AD). OBJECTIVE: We attempted to evaluate the clinical effects of environment with low indoor pollutant levels on AD management. METHODS: Fifty-one children (mean age 1.7 years) with moderate to severe AD who failed to show improvement with conventional management were recruited. Disease severity was assessed by SCORAD (Scoring of AD) indices. They were admitted in a low pollutant oom for 3-4 days (mean 3.3 days) which was designed to keep low levels of dust, house dust mites, micro-organisms, and indoor air pollutants such as total volatile organic compounds (TVOCs), particulate matter (PM), and so on. Air pollutant levels in the low pollutant room were lower than primary standards defined by the Korean Ministry of Environment. we compared disease severity on admission and after discharge, and the pollutant levels of each patient's home and low pollutant room. RESULTS: The SCORAD was significantly reduced from 42.0 ± 11 .5 to 29.8 ± 8.9 (p < 0.001) by management in a low pollutant room. PM(2.5), PM(10), formaldehyde, TVOCs, carbon dioxide, bacterial suspensions, and indoor molds were significantly higher in the patient's home than low pollutant room. Out of 29 patients who deteriorated after discharge to their home, 8 patients were admitted again, and their SCORAD was rapidly decreased from 53.1 ± 16.2 to 39.2 ± 9.8 (p = 0.036). CONCLUSION: Indoor air pollutants are likely to affect AD in susceptible individuals. Environmental control to lower indoor air pollutant levels might be necessary for better management of AD in some patients.

Natural course of cow's milk allergy in children with atopic dermatitis.

Cow's milk is one of the most common food allergens in children with atopic dermatitis (AD). This study was conducted to describe the natural course of cow's milk allergy in children with AD, and to identify factors predictive of outcome. To accomplish this, we reviewed the medical records of 115 children who were diagnosed with AD and cow's milk allergy before 24 months of age to evaluate their clinical characteristics and prognostic factors. During a follow-up period of 24 to 114 months, the median age for tolerance to cow's milk was found to be 67 months. Multivariate analysis using the Cox proportional hazard model revealed that the peak cow's milk-specific IgE level within 24 months after birth was the most important factor for prediction of the outcome of cow's milk allergy. In conclusion, half of the children younger than 24 months of age with AD and cow's milk allergy could tolerate cow's milk at 67 months of age. The peak cow's milk-specific IgE level within the first 24 months of birth is useful to predict the prognosis of cow's milk allergy in children with AD.