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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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Τετάρτη 16 Μαρτίου 2022

Gastric duplication cyst: a challenging EUS differential diagnosis between subepithelial gastric lesion and exophytic pancreatic cystic neoplasm—a case report and a literature review

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Abstract

Gastric duplication cysts are rare congenital malformation with a potential neoplastic progression and they may represent a challenge in differential diagnosis with exophytic pancreatic cyst neoplasm. We describe a case of a 38-year old man, complaining of recurrent epigastric pain due to a large abdominal mass, referred to our Hospital for EUS evaluation. Differential diagnosis was between gastric duplication cyst and exophytic pancreatic cyst because of FNA pointed out amylase 1280 UI/L and CEA 593.33 ng/mL. Despite antibiotic prophylaxis, an overinfection of the lesion occurred after the FNA, likely due to the technical failure to drain the cyst completely. Afterwards, the patient was referred to surgery and the pathologist confirmed the diagnosis of gastric duplication cyst. In this setting, EUS procedure has gained a leading play, complementary to traditional imaging tests, although its role has been not yet standardized in the reported literature. Here , we describe and discuss our demanding case, and we propose an algorithm to simplify and standardize the diagnostic workup.

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Real-time eye state recognition using dual convolutional neural network ensemble

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Abstract

Automatic recognition of the eye states is essential for diverse computer vision applications related to drowsiness detection, facial emotion recognition (FER), human–computer interaction (HCI), etc. Existing solutions for eye state detection are either parameter intensive or suffer from a low recognition rate. This paper presents the design and implementation of a vision-based system for real-time eye state recognition on a resource-constrained embedded platform to tackle these issues. The designed system uses an ensemble of two lightweight convolutional neural networks (CNN), each trained to extract relevant information from the eye patches. We adopted transfer-learning-based fine-tuning to overcome the over-fitting issues when training the CNNs on small sample eye state datasets. Once trained, these CNNs are integrated and jointly fine-tuned to achieve enhanced performance. Experimental results manifest the effectiveness of the proposed eye state recognizer that is robust and computationally efficient. On the ZJU dataset, the proposed DCNNE model delivered the state-of-the-art recognition accuracy of 97.99% and surpassed the prior best recognition accuracy of 97.20% by 0.79%. The designed model also achieved competitive results on the CEW and MRL datasets. Finally, the designed CNNs are optimized and ported on two different embedded platforms for real-world applications with real-time performance. The complete system runs at 62 frames per second (FPS) on an Nvidia Xavier device and 11 FPS on a low-cost Intel NCS2 embedded platform using a frame size of 640 \(\times\) 480 pixels resolution.

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Microglia phenotypes are associated with subregional patterns of concomitant tau, amyloid-β and α-synuclein pathologies in the hippocampus of patients with Alzheimer’s disease and dementia with Lewy bodies

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Abstract

The cellular alterations of the hippocampus lead to memory decline, a shared symptom between Alzheimer's disease (AD) and dementia with Lewy Bodies (DLB) patients. However, the subregional deterioration pattern of the hippocampus differs between AD and DLB with the CA1 subfield being more severely affected in AD. The activation of microglia, the brain immune cells, could play a role in its selective volume loss. How subregional microglia populations vary within AD or DLB and across these conditions remains poorly understood. Furthermore, how the nature of the hippocampal local pathological imprint is associated with microglia responses needs to be elucidated. To this purpose, we employed an automated pipeline for analysis of 3D confocal microscopy images to assess CA1, CA3 and DG/CA4 subfields microglia responses in post-mortem hippocampal samples from late-onset AD (n = 10), DLB (n = 8) and age-matched control (CTL) (n =� ��11) individuals. In parallel, we performed volumetric analyses of hyperphosphorylated tau (pTau), amyloid-β (Aβ) and phosphorylated α-synuclein (pSyn) loads. For each of the 32,447 extracted microglia, 16 morphological features were measured to classify them into seven distinct morphological clusters. Our results show similar alterations of microglial morphological features and clusters in AD and DLB, but with more prominent changes in AD. We identified two distinct microglia clusters enriched in disease conditions and particularly increased in CA1 and DG/CA4 of AD and CA3 of DLB. Our study confirms frequent concomitance of pTau, Aβ and pSyn loads across AD and DLB but reveals a specific subregional pattern for each type of pathology, along with a generally increased severity in AD. Furthermore, pTau and pSyn loads were highly correlated across subregions and conditions. We uncovered tight associations between microglial changes and the subfield pathological imprint. Our findi ngs suggest that combinations and severity of subregional pTau, Aβ and pSyn pathologies transform local microglia phenotypic composition in the hippocampus. The high burdens of pTau and pSyn associated with increased microglial alterations could be a factor in CA1 vulnerability in AD.

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Laser‐Assisted Sialolithotripsy: A Correlation of Objective and Subjective Outcomes

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Objective

To analyze the long-term symptomatic results of laser-assisted sialolithotripsy (LAS) in cases of obstructive sialolithiasis and correlate with objective criteria using diagnostic sialendoscopy (DS) as a method of examination.

Methods

This is a retrospective study comprising 50 consecutive patients who underwent holmium-YAG LAS and completed follow-up of at least 6 months. Symptom scoring and endoscopic scoring were done at 6 weeks and 6 months intervals for further study purposes.

Results

At the end of 6 weeks post-LAS, 70% patients were asymptomatic (A-sym) and only 30% had residual symptoms (Sym). However, obstructed duct (OB-duct) was observed on endoscopic scoring in 88% due to stenosis, residual stones, or both stenosis and residual stones. The obstructed ducts were treated in outpatient clinic and followed up over time, leading to 98% of patients being in A-sym group at the end of study period of 6 months. At the end of study, 82% of patients had clear duct (CL-duct).

Conclusion

Holmium LAS is a viable option for the management of intermediate-sized stones. LAS if used judiciously, and in properly selected cases, has high rate of stone fragmentation and symptom resolution. A vigilant postoperative protocol taking into account residual mealtime symptoms and altered salivary characteristics combined with early DS can help identify and treat patients with residual stone fragments and ductal stenosis.

Level of Evidence

3 Laryngoscope, 2022

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