In Vitro and In Vivo Models for the Development of Hair Growth Materials By Regulating the ß-Catenin Signaling Pathways.

Although hair loss contributes to various social and economic, research methods for material development are currently limited. In this study, we established a research model for developing materials for hair growth through the regulation of ß-catenin. We confirmed that 100 nM tegatrabetan (TG), a ß-catenin inhibitor, decreased the proliferation of human hair follicle dermal papilla cells (HFDPCs) at 72 h. In addition, TG-induced apoptosis suppressed the phosphorylation of GSK-3ß and Akt, translocation of ß-catenin from the cytosol to the nucleus, and the expression of cyclin D1. Interestingly, TG significantly increased the G2/M arrest in HFDPCs. Subcutaneous injection of TG suppressed hair growth and the number of hair follicles in C57BL/6 mice. Moreover, TG inhibited the expression of cyclin D1, ß-catenin, keratin 14, and Ki67. These results suggest that TG-induced inhibition of hair growth can be a promising model for developing new materials for enhancing ß-catenin-mediated hair growth.

Innate Immune-Enhancing Effect of Pinus densiflora Pollen Extract via NF-κB Pathway Activation.

Considering the emergence of various infectious diseases, including the coronavirus disease 2019 (COVID-19), people's attention has shifted towards immune health. Consequently, immune-enhancing functional foods have been increasingly consumed. Hence, developing new immune-enhancing functional food products is needed. Pinus densiflora pollen can be collected from the male red pine tree, which is commonly found in Korea. P. densiflora pollen extract (PDE), obtained by water extraction, contained polyphenols (216.29 ± 0.22 mg GAE/100 g) and flavonoids (35.14 ± 0.04 mg CE/100 g). PDE significantly increased the production of nitric oxide (NO) and reactive oxygen species (ROS) but, did not exhibit cytotoxicity in RAW 264.7 cells. Western blot results indicated that PDE induced the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX)-2. PDE also significantly increased the mRNA and protein levels of cytokines and the phosphorylation of IKKα/ß and p65, as well as the activation and degradation of IκBα. Additionally, western blot analysis of cytosolic and nuclear fractions and immunofluorescence assay confirmed that the translocation of p65 to the nucleus after PDE treatment. These results confirmed that PDE increases the production of cytokines, NO, and ROS by activating NF-κB. Therefore, PDE is a promising nutraceutical candidate for immune-enhancing functional foods.

A comprehensive research setup for monitoring Alzheimer's disease using EEG, fNIRS, and Gait analysis.

Alzheimer's disease (AD) has a detrimental impact on brain function, affecting various aspects such as cognition, memory, language, and motor skills. Previous research has dominantly used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to individually measure brain signals or combine the two methods to target specific brain functions. However, comprehending Alzheimer's disease requires monitoring various brain functions rather than focusing on a single function. This paper presents a comprehensive research setup for a monitoring platform for AD. The platform incorporates a 32-channel dry electrode EEG, a custom-built four-channel fNIRS, and gait monitoring using a depth camera and pressure sensor. Various tasks are employed to target multiple brain functions. The paper introduced the detailed instrumentation of the fNIRS system, which measures the prefrontal cortex, outlines the experimental design targeting various brain functioning programmed in BCI2000 for visualizing EEG signals synchronized with experimental stimulation, and describes the gait monitoring hardware and software and protocol design. The ultimate goal of this platform is to develop an easy-to-perform brain and gait monitoring method for elderly individuals and patients with Alzheimer's disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00306-7.

Sinapic acid alleviates inflammatory bowel disease (IBD) through localization of tight junction proteins by direct binding to TAK1 and improves intestinal microbiota.

Introduction: Although sinapic acid is found in various edible plants and has been shown to have anti-inflammatory properties including colitis, its underlying mechanism and effects on the composition of the gut microbiota are largely unknown. We aimed to identify an early response kinase that regulates the localization of tight junction proteins, act at the onset of the inflammatory response, and is regulated by sinapic acid. Additionally, we analyzed the effects of sinapic acid on the homeostasis of the intestinal microbiome. Methods: We examined the aberrant alterations of early response genes such as nuclear factor-kappa B (NF-κB) and activating transcription factor (ATF)-2 within 2 h of sinapic acid treatment in fully differentiated Caco-2 cells with or without lipopolysaccharide and tumor necrosis factor (TNF)-α stimulation. To confirm the effect of sinapic acid on stimulus-induced delocalization of tight junction proteins, including zonula occludens (ZO)-1, occludin, and claudin-2, all tight junction proteins were investigated by analyzing a fraction of membrane and cytosol proteins extracted from Caco-2 cells and mice intestines. Colitis was induced in C57BL/6 mice using 2% dextran sulfate sodium and sinapic acid (2 or 10 mg/kg/day) was administrated for 15 days. Furthermore, the nutraceutical and pharmaceutical activities of sinapic acid for treating inflammatory bowel disease (IBD) evaluated. Results: We confirmed that sinapic acid significantly suppressed the stimulus-induced delocalization of tight junction proteins from the intestinal cell membrane and abnormal intestinal permeability as well as the expression of inflammatory cytokines such as interleukin (IL)-1ß and TNF-α in vitro and in vivo. Sinapic acid was found to bind directly to transforming growth factor beta-activated kinase 1 (TAK1) and inhibit the stimulus-induced activation of NF-κB as well as MAPK/ATF-2 pathways, which in turn regulated the expression of mitogen-activated protein kinase (MLCK). Dietary sinapic acid also alleviated the imbalanced of gut microbiota and symptoms of IBD, evidenced by improvements in the length and morphology of the intestine in mice with colitis. Discussion: These findings indicate that sinapic acid may be an effective nutraceutical and pharmaceutical agent for IBD treatment as it targets TAK1 and inhibits subsequent NF-κB and ATF-2 signaling.

P300 Speller Performance Predictor Based on RSVP Multi-feature.

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller's performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller's performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300's speller performance. We found that several of the RSVP's event-related potential (ERP) and behavioral features were correlated with the P300 speller's offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.

CANet: A Channel Attention Network to Determine Informative Multi-channel for Image Classification from Brain Signals.

Changes in brain state that depend on various visual image stimulations have been investigated recently; however, it is difficult to decode visual image information from brain signal information. Recently, deep learning techniques have been applied to classify brain signals in various experiments, such as motor imagery and steady state visual evoked potential. However, although the deep learning model seems powerful, it is understood poorly, and thus, can be considered a black box. Accordingly, when multi-channel brain signals are trained, which channels include important information is not understood clearly. In this paper, we proposed a channel attention network (CANet) and investigated the way the deep learning network may determine which channels contain more important information that represents brainwaves' characteristics and the way it may visualize that information. Using such spatial channel information, we found that our proposed deep learning architecture outperforms basic approaches (spatial channel information is not considered) to classifying categorized images from visual evoked magnetoencephalographic (MEG) brain signals.