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Chronic rhinosinusitis (CRS) is an inflammatory disease affecting the sinuses or nose. Persistent inflammatory responses can lead to tissue remodeling, which is a pathological characteristics of CRS. Activation of fibroblasts in the nasal mucosal stroma, differentiation and collagen deposition, and subepithelial fibrosis have been associated with CRS. We aimed to assess the inhibitory effects of doxycycline and deoxycholic acid-polyethyleneimine conjugate (DA3-Doxy) on myofibroblast differentiation and extracellular matrix (ECM) production in nasal fibroblasts stimulated with TGF-β1. To enhance efficacy, we prepared DA3-Doxy using a conjugate of low-molecular-weight polyethyleneimine (PEI) (MW 1800) and deoxycholic acid (DA) and Doxy. The synthesis of the DA3-Doxy polymer was confirmed using nuclear magnetic resonance, and the critical micelle concentration required for cationic micelle formation through self-assembly was determined. Subsequently, the Doxy loading efficiency of DA3 was assessed. The cytotoxicity of Doxy, DA3, PEI, and DA-Doxy in nasal fibroblasts was evaluated using the WST-1 assay. The anti-tissue remodeling and anti-inflammatory effects of DA3-Doxy and DA3 were examined using real-time polymerase chain reaction (Real-time PCR), immunocytochemistry, western blot, and Sircol assay. Both DA3 and DA3-Doxy exhibited cytotoxicity at 10 μg/ml in nasal fibroblasts. Doxy partially inhibited α-smooth muscle actin, collagen types I and III, and fibronectin. However, DA3-Doxy significantly inhibited α-SMA, collagen types I and III, and fibronectin at 5 μg/ml. DA3-Doxy also modulated TGF-β1-induced changes in the expression of MMP 1, 2, and 9. Nonetheless, TGF-β1-induced expression of MMP3 was further increased by DA3-Doxy. The expression of TIMP 1 and 2 was partially reduced with 5 μg/ml DA3-Doxy. Although initially developed for the delivery of genetic materials or drugs, DA3 exhibits inhibitory effects on myofibroblast differentiation and ECM production. Therefore, it holds therapeutic potential for CRS, and a synergistic effect can be expected when loaded with CRS treatment drugs.

Apatinib, a novel and selective inhibitor of vascular endothelial growth factor (VEGF) receptor 2, has been demonstrated recently to exhibit anticancer efficacy by inhibiting the VEGF signaling pathway. Given the importance of VEGF in retinal vascular leakage, the present study was designed to investigate whether apatinib-loaded polymeric nanoparticles inhibit VEGF-mediated retinal vascular hyperpermeability and block diabetes-induced retinal vascular leakage. For the delivery of water-insoluble apatinib, the drug was encapsulated in nanoparticles composed of human serum albumin (HSA)-conjugated polyethylene glycol (PEG). In vitro paracellular permeability and transendothelial electric resistance assays showed that apatinib-loaded HSA-PEG (Apa-HSA-PEG) nanoparticles significantly inhibited VEGF-induced endothelial hyperpermeability in human retinal microvascular endothelial cells. In addition, they substantially reduced the VEGF-induced junctional loss and internalization of vascular endothelial-cadherin, a major component of endothelial junction complexes. In vivo intravitreal injection of Apa-HSA-PEG nanoparticles in mice blocked VEGF-induced retinal vascular leakage. These in vitro and in vivo data indicated that Apa-HSA-PEG nanoparticles efficiently blocked VEGF-induced breakdown of the blood-retinal barrier. In vivo experiments with streptozotocin-induced diabetic mice showed that an intravitreal injection of Apa-HSA-PEG nanoparticles substantially inhibited diabetes-induced retinal vascular leakage. These results demonstrated, for the first time, that apatinib-loaded nanoparticles may be a promising therapeutic agent for the prevention and treatment of diabetes-induced retinal vascular disorders.

The brain–computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Glioblastoma multiforme (GBM), the most aggressive and malignant glioma, has a poor prognosis. Although patients with GBM are treated with surgery, chemotherapy, and radiation therapy, GBM is highly resistant to treatment, making it difficult and expensive to treat. In this study, we analyzed the Gene Expression Profiling Interactive Analysis dataset, the Cancer Genome Atlas dataset, and Gene Expression Omnibus array data. ZBTB7A (also called FBI1/POKEMON/LRF) was found to be highly expressed in low-grade glioma but significantly downregulated in patients with GBM. ZBTB7A is a transcription factor that plays an important role in many developmental stages, including cell proliferation. The activation of epithelial-mesenchymal transition (EMT) is a key process in cancer progression and metastasis. Erythrocyte membrane protein band 4.1 like 5 (EPB41L5) is an essential protein for EMT progression and metastasis in various types of cancer. We found that ZBTB7A depletion in U87 cells induced GBM progression and metastasis. Based on RNA sequencing data, ZBTB7A directly binds to the promoter of the EPB41L5 gene, reducing its expression and inhibiting GBM progression. We demonstrated that ZBTB7A dramatically inhibits GBM tumor growth through transcriptional repression of EPB41L5. Thus, both ZBTB7A and EPB41L5 may be potential biomarkers and novel therapeutic targets for GBM treatment. Overall, we discovered the role of a novel tumor suppressor that directly inhibits GBM progression (ZBTB7A) and identified EPB41L5 as a therapeutic target protein for patients with GBM. Brain tumors: Regulatory protein suppresses tumor growth The identification of a key tumor-suppressing protein could provide biomarkers and treatments for an aggressive and fast-growing form of brain and spinal cord cancer. Glioblastoma multiforme (GBM) is resistant to many forms of treatment, resulting in poor prognosis. By analyzing gene expression databases, Kyung-Chul Choi and Seong Who Kim at the University of Ulsan College of Medicine in Seoul, South Korea, and co-workers found that expression of a regulatory protein, the transcription factor ZBTB7A, is significantly reduced in patients with aggressive GBM. In experiments on mice, the researchers found that ZBTB7A inhibits expression of the EPB41L5 gene, which codes for a protein which promotes tumor growth and metastasis. Both the ZBTB7A and EPB41L5 proteins could provide useful biomarkers for GBM prognosis, and offer therapeutic targets for the disease.

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

Background Epigenetic regulations frequently appear in Glioblastoma (GBM) and are highly associated with metabolic alterations. Especially, Histone deacetylases (HDACs) correlates with the regulation of tumorigenesis and cell metabolism in GBM progression, and HDAC inhibitors report to have therapeutic efficacy in GBM and other neurological diseases; however, GBM prevention and therapy by HDAC inhibition lacks a mechanism in the focus of metabolic reprogramming. Methods HDAC2 highly express in GBM and is analyzed in TCGA/GEPIA databases. Therefore, HDAC2 knockdown affects GBM cell death. Analysis of RNA sequencing and qRT-PCR reveals that miR-3189 increases and GLUT3 decreases by HDAC2 knockdown. GBM tumorigenesis also examines by using in vivo orthotopic xenograft tumor models. The metabolism change in HDAC2 knockdown GBM cells measures by glucose uptake, lactate production, and OCR/ECAR analysis, indicating that HDAC2 knockdown induces GBM cell death by inhibiting GLUT3. Results Notably, GLUT3 was suppressed by increasing miR-3189, demonstrating that miR-3189-mediated GLUT3 inhibition shows an anti-tumorigenic effect and cell death by regulating glucose metabolism in HDAC2 knockdown GBM. Conclusions Our findings will demonstrate the central role of HDAC2 in GBM tumorigenesis through the reprogramming of glucose metabolism by controlling miR-3189-inhibited GLUT3 expression, providing a potential new therapeutic strategy for GBM treatment.

It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30–0.45 gpeak with similar sensitivity in the 80–270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment.

In veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis. This study aimed to develop a deep learning-based model for classifying the status of canine chronic kidney disease (CKD) using renal ultrasonographic images and assess its diagnostic performance in comparison with that of veterinary imaging specialists, thereby verifying its clinical utility. In this study, 883 ultrasonograms were obtained from 198 dogs, including those diagnosed with CKD according to the International Renal Interest Society (IRIS) guidelines and healthy dogs. After preprocessing and labeling each image with its corresponding IRIS stage, the renal regions were extracted and classified based on the IRIS stage using the convolutional neural network-based object detection algorithm You Only Look Once. The training scenarios consisted of multi-class classification, categorization of images into IRIS stages, and four binary classifications based on specific IRIS stages. To prevent model overfitting, we balanced the dataset, implemented early stopping, used lightweight models, and applied dropout techniques. Model performance was assessed using accuracy, recall, precision, F1 score, and receiver operating characteristic curve and compared with the diagnostic accuracy of four specialists. Inter- and intra-observer variabilities among specialists were also evaluated. The developed model exhibited a low accuracy of 0.46 in multi-class classification. However, a significant performance improvement was observed in binary classifications, with the model designed to distinguish stage 3 or higher showing the highest accuracy of 0.85. In this classification, recall, precision, and F1 score values were all 0.85, and the area under the curve was 0.89. Compared with radiologists, whose accuracy ranged from 0.48 to 0.62 in this experimental scenario, the AI model exhibited superiority. Intra-observer reliability among radiologists was substantial, whereas inter-observer variability showed a moderate level of agreement. This study developed a deep-learning framework capable of reliably classifying CKD IRIS stages 3 and 4 in dogs using ultrasonograms. The developed framework demonstrated higher accuracy than veterinary imaging specialists and provided more objective and consistent interpretations. Therefore, deep-learning-based ultrasound diagnostics are potentially valuable tools for diagnosing CKD in dogs.

Tamoxifen is widely used as a medication for estrogen receptor α (ERα)-positive breast cancer, despite the ~50% incidence of tamoxifen resistance. To overcome such resistance, combining tamoxifen with other agents is considered an effective approach. Here, through in vitro studies with ER-positive MCF7 cells and ER-negative MDA-MB-231 cells, validated by the use of xenograft mice, we investigated the potential of tumor necrosis factor α (TNFα) to enhance tamoxifen sensitivity and identified NCOR1 as a key downstream regulator. TNFα specifically degraded nuclear receptor corepressor 1 (NCOR1) in MCF7 cells. Moreover, knockdown of NCOR1, similar to TNFα treatment, suppressed cancer cell growth and promoted apoptosis only in MCF7 cells and MCF7 xenograft mice through the stabilization of p53, a tumor suppressor protein. Interestingly, NCOR1 knockdown with TNFα treatment increased the occupancy of p53 at the p21 promoter, while decreasing that of ERα. Notably, NCOR1 formed a complex with p53 and ERα, which was disrupted by TNFα. Finally, combinatorial treatment with tamoxifen, TNFα and short–hairpin (sh)-NCOR1 resulted in enhanced suppression of tumor growth in MCF7 xenograft mice compared to single tamoxifen treatment. In conclusion, TNFα promoted tamoxifen sensitivity through the dissociation of the ERα-p53-NCOR1 complex, pointing at NCOR1 as a putative therapeutic target for overcoming tamoxifen resistance in ERα-positive breast cancer.