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Πέμπτη 17 Μαρτίου 2022

Effects of salicylate derivatives on localization of p.H723R allele product of SLC26A4

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Publication date: Available online 16 March 2022

Source: Auris Nasus Larynx

Author(s): Michio Murakoshi, Yuhi Koike, Shin Koyama, Shinichi Usami, Kazusaku Kamiya, Katsuhisa Ikeda, Yoichi Haga, Kohei Tsumoto, Hiroyuki Nakamura, Noriyasu Hirasawa, Kenji Ishihara, Hiroshi Wada

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MCM-41-supported double metal cyanide nanocomposite catalyst for ring-opening polymerisation of propylene oxide

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Abstract

The fabrication of low-cost, recyclable, reusable, and heterogeneous catalysts is of considerable significance for green chemistry. In this study, a nanocomposite catalyst, MCM-41-supported Zn–Co double metal cyanide (MCM-41@DMC), was prepared using the synchronous dropping method. Thereafter, the composition, crystal structure, complexing state, morphology, and thermal stability of the catalyst were characterised using scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, Brunauer–Emmett–Teller analysis, and thermogravimetry. The effects of different organic ligands on the catalytic activity of samples were evaluated, and the results showed that ethyl acetoacetate exhibited the best catalytic activity owing to its ketone coordination. The use of the MCM-41 support was beneficial for improving the catalytic activity because it reduced the crystallinity and substantially increased the ex ternal specific surface area of the catalyst. The experimental results pertaining to the use of the MCM-41@DMC catalyst in the fabrication of polypropylene glycol showed that the conversion of propylene oxide and molecular weight reached 90.6% and 2900, respectively. This study provides a new strategy for the green synthesis of poly(propylene glycol) products.

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Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG

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Abstract

Objectives

Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it.

Methods

A novel framework for forecasting the occurrence of apnea from single-lead electrocardiogram (ECG) based on deep recurrent neural networks is proposed. ECG R-peak amplitudes and R-R intervals are extracted and aligned using power spectral analysis, and recurrent deep learning models are developed to extract the most predictive ECG features and forecast the occurrence of apnea.

Results

The performance of the proposed approach was validated in forecasting apnea events up to five minutes in future on a dataset of 70 sleep recordings. A forecasting accuracy of up to 94.95% was achieved which was higher than the performance of conventional multilayer perceptron (p < 0.05) and other state-of-the-art techniques.

Conclusions

The proposed deep learning approach was successful in forecasting the occurrence of sleep apnea from single-lead ECG. It can therefore be adopted in wearable sleep monitors for the management of sleep apnea. Our developed algorithms are publicly available on GitHub.

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