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Graphical Abstract:

A contorted hexabenzocorone (HBC) mesogen based epoxy was synthesized and characterized for high thermal stability and thermal conductivity. Since HBC has a nano‐graphene like structure, which would have strong pi–pi interactions, it was expected that HBC epoxy would have superior heat resistance and thermal conductivity. Based on this hypothesis, HBC containing four epoxy groups at edges was synthesized and confirmed by NMR and mass spectrum. HBC epoxy was cured with diaminophenolsulfone and their thermal stability and thermal conductivity were investigated. As expected, the decomposition of 5 wt% occurred at 323.44°C, and thermal conductivity was 0.32 W/mK where the general thermal conductivity of epoxy resins is around 0.2 W/mK. Such enhancement of thermal properties would be explained by the formation of pi–pi stacking of HBC mesogens. Epoxy groups were introduced to contorted hexabenzocorone (HBC) structure for high heat resistance and thermal conductivity. The HBC epoxy was cured by diaminophenylsulfone at 170°C for 2 h. The Td(5 wt%) and thermal conductivity of the cured HBC epoxy were 323.44°C and 0.32 W/mK, respectively. Such high thermal stability and conductivity are originated from pi–pi stacking of HBC mesogens.

Background: Although an association between COVID-19 vaccination and Bell’s palsy (BP) has been reported, a clear causal relationship has not been elucidated. We investigated the risk and clinical characteristics of BP after COVID-19 vaccination. Methods: This retrospective chart review evaluated the association between COVID-19 vaccination and BP by comparing the number of patients diagnosed with BP during the pre-COVID-19 vaccination period (March 2018–February 2021) and the COVID-19 mass vaccination period (March 2021–February 2022). We then compared vaccine-related (time between vaccination and BP onset < 42 days) and -unrelated (time interval ≥ 42 days or non-vaccination) clinical characteristics in newly diagnosed patients with BP. Results: BP occurred more during the COVID-19 vaccination period than in the previous three pre-vaccination years. Thirteen patients developed BP within 42 days of vaccination. All patients, except one, developed BP after mRNA-based vaccination, with most cases (9/13, 69.2%) occurring after the second or third dose. Thirteen patients with vaccine-related BP were younger (age 43.92 ± 13.14 vs. 54.32 ± 16.01 years; p= 0.033) and more frequently experienced taste changes (58.8% vs. 10.9%; p= 0.002) than 52 patients with vaccine-unrelated BP. Patients with vaccine-related BP had a greater likelihood of good and faster (p= 0.042) facial nerve function recovery than those with vaccine-unrelated BP (100% vs. 78%). Conclusion: COVID-19 vaccines, especially mRNA-based vaccines, may be associated with BP cases with distinctive clinical characteristics, which occur more frequently in young individuals, are frequently accompanied by taste changes, and have fast and good recovery.

Graphical Abstract: The pure BNNT thread-based woven textiles, created using the lyotropic molecular self-assembly method and soft domain selective degradation process, offer an innovative solution to protect both astronauts and critical electronics from the hazards of space radiation at extreme temperature.

Bulbar dysfunction in amyotrophic lateral sclerosis (ALS) significantly affects daily life, leading to weight loss and reduced survival. Methods for evaluating bulbar dysfunction, including videofluoroscopic swallowing studies and the bulbar component of the ALS Functional Rating Scale-Revised (ALSFRS-R), have been employed; however, Korean-specific tools are lacking. The Center for Neurologic Study Bulbar Function Scale (CNS-BFS) comprehensively evaluates bulbar symptoms. This study aimed to develop and validate the Korean version of the CNS-BFS (K-CNS-BFS) to assess bulbar dysfunction in Korean patients with ALS. Twenty-seven patients with ALS were recruited from a tertiary hospital in South Korea based on revised El Escorial criteria. Demographic, clinical, and measurement data were collected. The K-CNS-BFS was evaluated for reliability and validity. Reliability assessment revealed strong internal consistency (Cronbach alpha) for the K-CNS-BFS subscales and total score. Test–retest reliability showed significant correlation. Content validity index was excellent, and convergent validity demonstrated significant correlations between the K-CNS-BFS and relevant measures. Discriminant validity was observed between the K-CNS-BFS and motor/respiratory subscores of the ALSFRS-R. Construct validity demonstrated significant correlations between the K-CNS-BFS subscales and total score. This is the first study to investigate the reliability and validity of the Korean version of the CNS-BFS, which showed consistent and reliable scores that correlated with tests for bulbar or general dysfunction. The K-CNS-BFS effectively measured bulbar dysfunction similar to the original CNS-BFS. The K-CNS-BFS is a reliable and valid tool for assessing bulbar dysfunction in patients with ALS in South Korea.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

Nowadays, deep neural networks (DNNs) for image processing are becoming more complex; thus, reducing computational cost is increasingly important. This study highlights the construction of a DNN for real‐time image processing, training various image processing operators efficiently through multitask learning. For real‐time image processing, the proposed algorithm takes a joint upsampling approach through bilateral guided upsampling. For multitask learning, the overall network is based on an encoder‐decoder architecture, which consists of encoding, processing, and decoding components, in which the encoding and decoding components are shared by all the image processing operators. In the processing component, a semantic guidance map, which contains processing information for each image processing operator, is estimated using simple linear shifts of the shared deep features. Through these components, the proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing. This paper proposes a real‐time image enhancement algorithm, based on the encoder‐decoder architecture for multitask learning. The proposed network shares the encoding and decoding components for all image processing operators, and the processing component estimates different semantic guidance maps through a simple linear transformation of the deep feature. The proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing.

Rapid diagnosis and quarantine of influenza virus mutant-infected people is critical to contain the fatal viral infection spread because effective antiviral drugs are normally not available. Conventional methods, however, cannot be used for the diagnosis because these methods need predefined labels, likely also unavailable for just emerging viruses. Here, we propose label-free identification of cells infected with different influenza viruses based on surface-enhanced Raman spectroscopy (SERS) and principal component analysis (PCA). Viral envelope proteins that are displayed on the surface of cells after infection of influenza viruses were targeted for this identification. Cells that expressed the envelope proteins of A/WSN/33 H1N1 or A/California/04/2009 H1N1 influenza viruses produced distinct SERS signals. Cells that displayed combinations of the envelope proteins from these two viral variants, an indication of emergence of a new virus, also generated characteristic SERS patterns. However, the cell’s own surface proteins often hindered the identification of virally infected cells by producing SERS peaks similar to viral ones. PCA of the obtained SERS patterns could effectively capture the virus-specific signal components from the jumbled SERS peaks. Our study demonstrates a potential of combination of SERS and PCA to identify newly emerging influenza viruses through sensing the cells infected with the viruses.

In this paper, we review the recent research progress on Si pillar assisted hierarchical three dimensional (3D) carbon nanotube structures focusing mainly on the rational modification of the 3D network of single-walled carbon nanotube (3DNC) structures and its potential applications. Compared with conventional carbon nanotube (CNT) based microstructures which have been studied by other researchers, the 3DNC attract more attention because of its unique hierarchical structure which is comprised of interconnected CNTs between Si pillars. Functionalization of CNTs without destroying the hierarchical 3D structure of 3DNC is always a challenge. Physical and chemical vapor depositions, electrochemical depositions, polymer coating, and capillary force induced self-assembly have been applied for the surface modification of 3DNC. Those modified 3DNC structures have been applied to various research areas, like signal enhancement, microfluidic chips, energy storage, catalysis, and sensors, because of their unique hierarchical 3D structures. We also introduce some synthetic works on the capillary force induced wall-shaped CNT structures on pillar substrates.This paper reviews the recent research progress on the Si pillar assisted hierarchical three dimensional carbon nanotube structures focusing mainly on the rational modification of 3D network of single-walled carbon nanotube (3DNC) structure and its potential applications. We also introduce some synthetic studies of capillary force induced wall-shaped CNT structures on pillar substrates.

In the present study, cellulose whiskers were incorporated into nanofibers of polyethylene oxide (PEO) by the electrospinning process to enhance the mechanical properties of the electrospun PEO fibers. Cellulose whiskers consisting of highly crystalline rod‐like particles with a high aspect ratio and specific area were obtained by the acid hydrolysis of bacterial cellulose microfibrils. From the transmission electron microscopy (TEM) images of the bacterial cellulose whiskers, their length was measured to be 420 ± 190 nm and their width 11 ± 4 nm. The height of the whiskers was measured to be 10 ± 2 nm by atomic force microscopy. The successful formation of electrospun fibers with a diameter of less than 1 µm and well‐embedded microfibrils was confirmed by scanning electron microscopy and TEM.

Summary:A simple and mass‐producible method was developed to densely assemble multiwalled carbon nanotubes (MWNTs) onto electrospun nylon 6 nanofibrous membranes. The process consists of dispersing the acid‐treated MWNTs in surfactant solutions or organic solvents, and dipping the nanofibrous membranes in the resulting dispersion for only 60 seconds, followed by the extraction of the surfactants in pure water and drying. The conductivity of the MWNT‐adsorbed nanofibrous membranes ranges from 2.2 × 10−2to 1.5 × 10−1S · cm−1, as determined by the four probe method, which implies that the MWNTs are adsorbed uniformly and densely along the nanofibrous membranes. Furthermore, the results suggest that there is a strong interaction between the acid‐treated MWNTs and nylon 6. We also investigate the amount of MWNTs present in the membranes using thermogravimetric analysis.

AbstractPTASV was newly synthesized through the Wittig reaction and characteristics of various types of LEDs using this polymer were investigated. A single layer device (ITO/PTASV/Al) showed green emission at about 540 nm with the threshold voltage of 20 V. When a thin LiF layer was inserted to the PTASV/Al interface, the external quantum efficiency was increased about 10 times that of the single layer device with lower threshold voltage. The external quantum efficiency of the device with heterojunction structure (ITO/PTASV/Alq3/LiF/Al) was 0.01% at current density of 8 mA/cm2, in air and at room temperature.