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Abstract The application of Li metal anodes in rechargeable batteries is impeded by safety issues arising from the severe volume changes and formation of dendritic Li deposits. Three‐dimensional hollow carbon is receiving increasing attention as a host material capable of accommodating Li metal inside its cavity; however, uncontrollable and nonuniform deposition of Li remains a challenge. In this study, we synthesize metal–organic framework‐derived carbon microcapsules with heteroatom clusters (Zn and Ag) on the capsule walls and it is demonstrated that Ag‐assisted nucleation of Li metal alters the outward‐to‐inward growth in the microcapsule host. Zn‐incorporated microcapsules are prepared via chemical etching of zeolitic imidazole framework‐8 polyhedra and are subsequently decorated with Ag by a galvanic displacement reaction between Ag+ and metallic Zn. Galvanically introduced Ag significantly reduces the energy barrier and increases the reaction rate for Li nucleation in the microcapsule host upon Li plating. Through combined electrochemical, microstructural, and computational studies, we verify the beneficial role of Ag‐assisted Li nucleation in facilitating inward growth inside the cavity of the microcapsule host and, in turn, enhancing electrochemical performance. This study provides new insights into the design of reversible host materials for practical Li metal batteries.

This study aimed to investigate the effects of ultrasound-guided high-dose Shinbaro 2 pharmacopuncture on the pain, dysfunction, and quality of life in patients with low back pain and radiating pain due to an acute herniated intervertebral disc (HIVD). A 39-year-old male patient with low back pain and radiating pain caused by an acute HIVD was treated with Korean and Western integrative medicine, with a focus on ultrasound-guided high-dose Shinbaro 2 pharmacopuncture at Kambin’s triangle. The treatment lasted 16 weeks, including a 12-day hospitalization. The low back pain and radiating pain were evaluated using the numeric rating scale (NRS). The lumbar function and quality of life were assessed using the Oswestry disability index (ODI) and the EuroQol five-dimension index (EQ5D). Satisfaction was gauged using the patient global impression of change (PGIC). After treatment, the NRS score decreased from 10 to 1, whereas the ODI and EQ5D scores improved from 84.44 to 28.89 and from 0.303 to 0.871, respectively. The PGIC was rated as 1, indicating considerable improvement. Notably, the changes observed during hospitalization were significant. This report suggests that ultrasound-guided high-dose Shinbaro 2 pharmacopuncture at Kambin’s triangle significantly improves the pain, dysfunction, and quality of life in patients with an acute HIVD, demonstrating its potential usefulness among Korean medicine practitioners.

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://github.com/mswzeus/PLUS.

Instantaneous drowsiness (i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject's drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme gradient boosting as a machine learning classifier. In addition, we introduce a novel phenotype labeling of instantaneous drowsiness by combining both task dependent and independent measures of alertness (psychomotor vigilance task and electrooculography technique, respectively). The results show that our techniques outperform others used in previous studies. We also identified which spectral components (θ, α, and γ) and channels (Fp1, Fp2, T3, T4, O1, O2, and electrocardiogram) play important roles in our drowsiness detection framework. To verify the applicability for a mobile environment, we implemented our framework on a wireless EEG, as well as on a wired EEG. We hereby present our successful results.

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.

Sphingosine-1-phosphate-1 (S1P1) receptor agonists are well-known drugs for treating multiple sclerosis (MS) caused by autoreactive lymphocytes that attack the myelin sheath. Therefore, an effective therapeutic strategy is to reduce the lymphocytes in the blood by inducing S1P1 receptor internalization. We synthesized serinolamide A, a natural product of the sea, and performed S1P1 receptor internalization assay to evaluate functionally antagonistic S1P1 receptor agonist activity. In order to synthesize derivatives with better efficacy than serinolamide A and B, new derivatives were synthesized by introducing the phenyl ring moiety of fingolimod. Among them, compounds 19 and 21 had superior S1P1 agonistic effects to serinolamide. We also confirmed that compound 19 effectively inhibited lymphocyte outflow in peripheral lymphocyte count (PLC) assay.

Neuropathic pain is a chronic and sometimes intractable condition caused by lesions or diseases of the somatosensory nervous system. Many drugs are available but unfortunately do not provide satisfactory effects in patients, producing limited analgesia and undesirable side effects. Thus, there is an urgent need to develop new pharmaceutical agents to treat neuropathic pain. To date, highly specific agents that modulate a single target, such as receptors or ion channels, never progress to the clinic, which may reflect the diverse etiologies of neuropathic pain seen in the human patient population. Therefore, the development of multifunctional compounds exhibiting two or more pharmacological activities is an attractive strategy for addressing unmet medical needs for the treatment of neuropathic pain. To develop novel multifunctional compounds, key pharmacophores of currently used clinical pain drugs, including pregabalin, fluoxetine and serotonin analogs, were hybridized to the side chain of tianeptine, which has been used as an antidepressant. The biological activities of the hybrid analogs were evaluated at the human transporters of neurotransmitters, including serotonin (hSERT), norepinephrine (hNET) and dopamine (hDAT), as well as mu (μ) and kappa (κ) opioid receptors. The most advanced hybrid of these multifunctional compounds, 17, exhibited multiple transporter inhibitory activities for the uptake of neurotransmitters with IC50 values of 70 nM, 154 nM and 2.01 μM at hSERT, hNET and hDAT, respectively. Additionally, compound 17 showed partial agonism (EC50 = 384 nM) at the μ-opioid receptor with no influence at the κ-opioid receptor. In in vivo pain animal experiments, the multifunctional compound 17 showed significantly reduced allodynia in a spinal nerve ligation (SNL) model by intrathecal administration, indicating that multitargeted strategies in single therapy could considerably benefit patients with multifactorial diseases, such as pain.

Aging and age-related diseases in humans are mainly caused by oxidative stress through over-production of reactive oxygen species (ROS). In our previous study, multi-micronutrient supplement containing antioxidants and phytochemicals (MP) resulted in higher antioxidative activity by increasing plasma folate with resistance to DNA damage and LDL oxidation to subjects. In this study, plasma samples were analysed for metabolomics study using 1H NMR. The metabolic profiles before MP supplementation and after 8-week MP supplementation were clearly separated based on multivariant analyses of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). Twenty-seven metabolites from variable importance plots were screened before MP supplementation and after 8-week MP supplementation. The 27 metabolites had significantly increased levels of betaine but significantly decreased levels of choline, serine, and threonine after MP supplementation. These results suggested that MP supplementation could activate folate cycle to increase resistance to DNA damages.

In this study, a semi-empirical model is presented that correlates to previously obtained experimental overpotential data for a high temperature polymer electrolyte membrane fuel cell (HT-PEMFC). The goal is to reinforce the understanding of the performance of the cell from a modeling perspective. The HT-PEMFC membrane electrode assemblies (MEAs) were constructed utilizing an 85 wt. % phosphoric acid doped Advent TPS® membranes for the electrolyte and gas diffusion electrodes (GDEs) manufactured by Reactive Spray Deposition Technology (RSDT). MEAs with varying ratios of PTFE binder to carbon support material (I/C ratio) were manufactured and their performance at various operating temperatures was recorded. The semi-empirical model derivation was based on the coated film catalyst layer approach and was calibrated to the experimental data by a least squares method. The behavior of important physical parameters as a function of I/C ratio and operating temperature were explored.

The purpose of this study was to evaluate the role of high-resolution magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy in patients with ductal carcinoma in situ (DCIS) diagnosed on preoperative biopsy. We investigated whether the metabolic profiling of tissue samples using HR-MAS 1H NMR spectroscopy could be used to distinguish between DCIS lesions with or without an invasive component. Our institutional review board approved this combined retrospective and prospective study. Tissue samples were collected from 30 patients with pure DCIS and from 30 with DCIS accompanying invasive carcinoma. All patients were diagnosed with DCIS by preoperative core-needle biopsy and underwent surgical resection. The metabolic profiling of tissue samples was performed by HR-MAS 1H NMR spectroscopy. All observable metabolite signals were identified and quantified in all tissue samples. Metabolite intensity normalized by total spectral intensities was compared according to the tumor type using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). By univariate analysis, the metabolite concentrations of choline-containing compounds obtained with HR-MAS 1H NMR spectroscopy did not differ significantly between the pure DCIS and DCIS accompanying invasive carcinoma groups. However, the GPC/PC ratio was higher in the pure DCIS group than in the DCIS accompanying invasive carcinoma group (p = 0.004, Bonferroni-corrected p = 0.064), as well as the concentration of myo-inositol and succinate. By multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles could clearly discriminate between pure DCIS and DCIS accompanying invasive carcinoma. Our preliminary results suggest that HR-MAS MR metabolomics on breast tissue may be able to distinguish between DCIS lesions with or without an invasive component.