SOXE group transcription factors regulates the expression of FoxG1 during inner ear development.

The transcription factor FOXG1 plays an important role in inner ear development; however, the cis-regulatory mechanisms controlling the inner-ear-specific expression of FOXG1 are poorly understood. In this study, we aimed to identify the element that specifically regulates FoxG1 expression in the otic vesicle, which develops into the inner ear, through comparative genome analysis between vertebrate species and chromatin immunoprecipitation. The cis-regulatory element (E2) identified showed high evolutionary conservation among vertebrates in the genomic DNA of FoxG1 spanning approximately 3 Mbp. We identified core sequences important for the activity of the otic-vesicle-specific enhancer through in vitro and in vivo reporter assays for various E2 enhancer mutants and determined the consensus sequence for SOX DNA binding. In addition, SoxE, a subfamily of the Sox family, was simultaneously expressed in the otic vesicles of developing embryos and showed a similar protein expression pattern as that of FoxG1. Furthermore, SOXE transcription factors induced specific transcriptional activity through the FoxG1 Otic enhancer (E2b). These findings suggest that the interaction between the otic enhancer of FoxG1 and SOXE transcription factor, in which the otic expression of FoxG1 is evolutionarily well-conserved, is important during early development of the inner ear, a sensory organ important for survival in nature.

Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos.

BACKGROUND: Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. METHODS: To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. RESULTS: Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. CONCLUSION: An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future.

Calibration curve-free electrochemical quantitation by micro-nano multi-scale gap devices.

Quantitation without relying on the calibration curve has long been an issue of overcoming analytical problems accompanied with the inherent limitations of the calibration curve fitting errors. Here, we report on a calibration curve-free method for electrochemical quantitation based on a multi-scale gap device (MGD). The MGD is an integrated device having a series of interdigitated electrodes (IDE) with micro-to-nano gap distances. The device shows a gap-dependent redox current of the analyte when subjected to the electrochemical cycling between the two facing electrodes of its componential IDEs. Based on the fact that the current increases as the gap distance decreases, the analyte concentration could be directly estimated: the rate of increase in the current was directly proportional to the analyte concentration. The calibration curve was not necessary for the quantitation. The accuracy of this MGD approach was better than that of an IDE collection of the same gap distance, which was deteriorated at the larger gap distances particularly. The MGD-based quantitation of dopamine, potassium ferricyanide, and aminophenol was demonstrated in a relatively broad range of concentrations (100 nM-5 mM).

Pre-hospital modified shock index for prediction of massive transfusion and mortality in trauma patients.

BACKGROUND: Modified shock index (MSI) is a useful predictor in trauma patients. However, the value of prehospital MSI (preMSI) in trauma patients is unknown. The aim of this study was to investigate the accuracy of preMSI in predicting massive transfusion (MT) and hospital mortality among trauma patients. METHODS: This was a retrospective, observational, single-center study. Patients presenting consecutively to the trauma center between January 2016 and December 2017, were included. The predictive ability of both prehospital shock index (preSI) and preMSI for MT and hospital mortality was assessed by calculating the areas under the receiver operating characteristic curves (AUROCs). RESULTS: A total of 1007 patients were included. Seventy-eight (7.7%) patients received MT, and 30 (3.0%) patients died within 24 h of admission to the trauma center. The AUROCs for predicting MT with preSI and preMSI were 0.773 (95% confidence interval [CI], 0.746-0.798) and 0.765 (95% CI, 0.738-0.791), respectively. The AUROCs for predicting 24-hour mortality with preSI and preMSI were 0.584 (95% CI, 0.553-0.615) and 0.581 (95% CI, 0.550-0.612), respectively. CONCLUSIONS: PreSI and preMSI showed moderate accuracy in predicting MT. PreMSI did not have higher predictive power than preSI. Additionally, in predicting hospital mortality, preMSI was not superior to preSI.

Luteolin inhibited the gene expression, production and secretion of MUC5AC mucin via regulation of nuclear factor kappa B signaling pathway in human airway epithelial cells.

Luteolin, a flavonoidal compound derived from Lonicera japonica Thunb. and Chrysanthemum indicum L., has been reported to show anti-inflammatory, anti-oxidative and anti-carcinogenic effects. In this study, we investigated whether luteolin significantly affects the secretion, production and gene expression of airway mucin. Confluent NCI-H292 cells were pretreated with luteolin for 30 min and then stimulated with EGF (epidermal growth factor) or PMA (phorbol 12-myristate 13-acetate) for 24 h or the indicated periods. The MUC5AC mucin gene expression was measured by RT-PCR. Production and secretion of MUC5AC mucin protein were measured by ELISA. To elucidate the action mechanism of luteolin, effect of luteolin on PMA-induced NF-κB signaling pathway was investigated by western blot analysis. The results were as follows: (1) Luteolin inhibited the secretion of MUC5AC mucin protein induced by EGF or PMA; (2) Luteolin inhibited the production of MUC5AC mucin protein and the expression of MUC5AC mucin gene induced by EGF or PMA; (3) Luteolin inhibited PMA-induced phosphorylation and degradation of inhibitory kappa Bα (IκBα); (4) Luteolin inhibited PMA-induced phosphorylation and nuclear translocation of nuclear factor kappa B (NF-κB) p65. This result suggests that luteolin can regulate the secretion, production and gene expression of mucin by acting on airway epithelial cells via regulation of NF-kB signaling pathway.

Inhibition of TNF-α-induced MUC5AC mucin gene expression and production by wogonin through the inactivation of NF-κB signaling in airway epithelial cells.

In this study, we investigated whether wogonin significantly affects MUC5AC mucin gene expression and production in human airway epithelial cells. Confluent NCI-H292 cells were pretreated with wogonin for 30 min and then stimulated with tumor necrosis factor-α (TNF-α) for 24 h or the indicated periods. The MUC5AC mucin gene expression and mucin protein production were measured by RT-PCR and ELISA, respectively. We found that incubation of NCI-H292 cells with wogonin significantly inhibited mucin production and down-regulated MUC5AC gene expression induced by TNF-α in a dose-dependent fashion. To elucidate the action mechanism of wogonin, effect of wogonin on TNF-α-induced NF-κB signaling pathway was investigated by western blot analysis. Wogonin inhibited NF-κB activation induced by TNF-α. Inhibition of IKK by wogonin led to the suppression of IκB phosphorylation and degradation, p65 nuclear translocation and NF-κB-regulated gene expression. This, in turn, led to the down-regulation of MUC5AC protein production in NCI-H292 cells. Wogonin also inhibited the gene products involved in cell survival (Bcl-2) and proliferation (cyclooxygenase-2). These results suggest that wogonin inhibits the NF-κB signaling pathway, which may explain its role in the inhibition of MUC5AC mucin gene expression and production.

Total variation denoising-based method of identifying the states of single molecules in break junction data.

Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.

Frequency Domain Deep Learning With Non-Invasive Features for Intraoperative Hypotension Prediction.

BACKGROUND: Intraoperative hypotension can lead to postoperative organ dysfunction. Previous studies primarily used invasive arterial pressure as the key biosignal for the detection of hypotension. However, these studies had limitations in incorporating different biosignal modalities and utilizing the periodic nature of biosignals. To address these limitations, we utilized frequency-domain information, which provides key insights that time-domain analysis cannot provide, as revealed by recent advances in deep learning. With the frequency-domain information, we propose a deep-learning approach that integrates multiple biosignal modalities. METHODS: We used the discrete Fourier transform technique, to extract frequency information from biosignal data, which we then combined with the original time-domain data as input for our deep learning model. To improve the interpretability of our results, we incorporated recent interpretable modules for deep-learning models into our analysis. RESULTS: We constructed 75,994 segments from the data of 3,226 patients to predict hypotension during surgery. Our proposed frequency-domain deep-learning model outperformed conventional approaches that rely solely on time-domain information. Notably, our model achieved a greater increase in AUROC performance than the time-domain deep learning models when trained on non-invasive biosignal data only (AUROC 0.898 [95% CI: 0.885-0.91] vs. 0.853 [95% CI: 0.839-0.867]). Further analysis revealed that the 1.5-3.0 Hz frequency band played an important role in predicting hypotension events. CONCLUSION: Utilizing the frequency domain not only demonstrated high performance on invasive data but also showed significant performance improvement when applied to non-invasive data alone. Our proposed framework offers clinicians a novel perspective for predicting intraoperative hypotension.

Oxidation mechanism of nickel oxide/carbon nanotube composite.

The oxidation mechanism and thermal stability of nickel oxide (NiO)/carbon nanotube (CNT) composites were investigated by examining composites with different NiO contents by thermogravimetric analysis and transmission electron microscopy (TEM). NiO acts as a catalyst in the oxidation of CNT in the composite. CNTs can be oxidized, even in a vacuum, by reducing NiO to nickel at temperatures lower than the normal oxidation temperature of CNTs. This phase transition was confirmed directly by in situ heating TEM observations. In air, reduction by CNT occurs simultaneously with reoxidation by gaseous O2 molecules, and NiO maintains its phase. The thermal stability decreased with increasing NiO content because of defects in the CNT generated by the NiO loading.

Enhanced morphological and thermal stabilities of nickel germanide with an ultrathin tantalum layer studied by ex situ and in situ transmission electron microscopy.

The formation and morphological evolution of germanides formed in a ternary Ni/Ta-interlayer/Ge system were examined by ex situ and in situ annealing experiments. The Ni germanide film formed in the Ni/Ta-interlayer/Ge system maintained continuity up to 550°C, whereas agglomeration of the Ni germanide occurred in the Ni/Ge system without Ta-interlayer. Through microstructural and chemical analysis of the Ni/Ta-interlayer/Ge system during and after in situ annealing in a transmission electron microscope, it was confirmed that the Ta atoms remained uniformly on the top of the newly formed Ni germanide layer during the diffusion reaction. Consequently, the agglomeration of the Ni germanide film was retarded and the thermal stability was improved by the Ta incorporation.

Machine learning and analytical methods for single-molecule conductance measurements.

Single-molecule measurements of single-molecule conductance between metal nanogap electrodes have been actively investigated for molecular electronics, biomolecular analysis, and the search for novel physical properties at the nanoscale level. While it is a disadvantage that single-molecule conductance measurements exhibit easily fluctuating and unreliable conductance, they offer the advantage of rapid, repeated acquisition of experimental data through the repeated breaking and forming of junctions. Owing to these characteristics, recently developed informatics and machine learning approaches have been applied to single-molecule measurements. Machine learning-based analysis has enabled detailed analysis of individual traces in single-molecule measurements and improved its performance as a method of molecular detection and identification at the single-molecule level. The novel analytical methods have improved the ability to investigate for new chemical and physical properties. In this review, we focus on the analytical methods for single-molecule measurements and provide insights into the methods used for single-molecule data interrogation. We present experimental and traditional analytical methods for single-molecule measurements, provide examples of each type of machine learning method, and introduce the applicability of machine learning to single-molecule measurements.

Direct biomolecule discrimination in mixed samples using nanogap-based single-molecule electrical measurement.

In single-molecule measurements, metal nanogap electrodes directly measure the current of a single molecule. This technique has been actively investigated as a new detection method for a variety of samples. Machine learning has been applied to analyze signals derived from single molecules to improve the identification accuracy. However, conventional identification methods have drawbacks, such as the requirement of data to be measured for each target molecule and the electronic structure variation of the nanogap electrode. In this study, we report a technique for identifying molecules based on single-molecule measurement data measured only in mixed sample solutions. Compared with conventional methods that require training classifiers on measurement data from individual samples, our proposed method successfully predicts the mixing ratio from the measurement data in mixed solutions. This demonstrates the possibility of identifying single molecules using only data from mixed solutions, without prior training. This method is anticipated to be particularly useful for the analysis of biological samples in which chemical separation methods are not applicable, thereby increasing the potential for single-molecule measurements to be widely adopted as an analytical technique.

Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs.

Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen's weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.

Regulation of the Gene Expression of Airway MUC5AC Mucin through NF-κB Signaling Pathway by Artesunate, an Antimalarial Agent.

In this study, artesunate, an antimalarial agent, was investigated for its potential effect on the gene expression of airway MUC5AC mucin. The human pulmonary epithelial NCI-H292 cells were pretreated with artesunate for 30 min and then stimulated with phorbol 12-myristate 13-acetate (PMA), for the following 24 h. The effect of artesunate on PMA-induced nuclear factor kappa B (NF-kB) signaling pathway was also examined. Artesunate inhibited the glycoprotein production and mRNA expression of MUC5AC mucins, induced by PMA through the inhibition of degradation of inhibitory kappa Bα (IkBα) and NF-kB p65 nuclear translocation. These results suggest artesunate suppresses the gene expression of mucin through regulation of NF-kB signaling pathway, in human pulmonary epithelial cells.

Meclofenamate Suppresses MUC5AC Mucin Gene Expression by Regulating the NF-kB Signaling Pathway in Human Pulmonary Mucoepidermoid NCI-H292 Cells.

The current study aimed to reveal the potential effect of meclofenamate, a nonsteroidal anti-inflammatory drug, on the gene expression of airway MUC5AC mucin. Human pulmonary mucoepidermoid NCI-H292 cells were pretreated with meclofenamate for 30 min and stimulated with phorbol 12-myristate 13-acetate (PMA) for 24 h. Thereafter, the effect of meclofenamate on the PMA-induced nuclear factor kappa B (NF-kB) signaling pathway was assessed. Meclofenamate inhibited glycoprotein production and mRNA expression of MUC5AC mucins induced by PMA by inhibiting the degradation of inhibitory kappa Bα (IkBα) and NF-kB p65 nuclear translocation. These results suggest meclofenamate suppresses mucin gene expression by regulating NF-kB signaling pathway in human pulmonary epithelial cells.

Single-Molecule Classification of Aspartic Acid and Leucine by Molecular Recognition through Hydrogen Bonding and Time-Series Analysis.

Amino acid detection/identification methods are important for understanding biological systems. In this study, we developed single-molecule measurements for investigating quantum tunneling enhancement by chemical modification and carried out machine learning-based time series analysis for developing accurate amino acid discrimination. We performed single-molecule measurement of L-aspartic acid (Asp) and L-leucine (Leu) with a mercaptoacetic acid (MAA) chemical modified nano-gap. The measured current was investigated by a machine learning-based time series analysis method for accurate amino acid discrimination. Compared to measurements using a bare nano-gap, it is found that MAA modification improves the difference in the conductance-time profiles between Asp and Leu through the hydrogen bonding facilitated tunneling phenomena. It is also found that this method enables determination of relative concentration. even in the mixture of Asp and Leu. It improves selective analysis for amino acids and therefore would be applicable in medicine, diagnosis, and single-molecule peptide sequencing.

Comparative Analysis of the Differences in Dentofacial Morphology According to the Tongue and Lip Pressure.

The aim of this study was to evaluate the effects of the tongue and lip pressure on dentofacial morphology. The subjects comprised 194 patients with malocclusion. Anterior and posterior tongue elevation and lip pressures were evaluated using the Iowa Oral Performance Instrument (IOPI) device. The lateral cephalograms of each subject were traced and digitized to perform the analysis. Statistical analysis was used to investigate the relationship between perioral muscle force and the cephalometric variables. Anterior and posterior tongue pressure was both higher in males than in females. No sex difference in lip pressure was observed. The group with a low posterior tongue pressure showed a short ramus height, short posterior facial height, and clockwise-rotated mandible. On the other hand, lip pressure had a significant influence on maxillary incisor angulation. Skeletal pattern was not found to be significantly related with lip pressure. The anterior tongue pressure appeared as a mixed pattern of the two results. Tongue pressure was related to skeletal measurements, such as short posterior facial height, and lip pressure was related to the angulation of the anterior teeth. This study suggests that there may be differences in dentofacial morphology according to the differences in perioral muscle force.

Targeted suicide gene therapy for liver cancer based on ribozyme-mediated RNA replacement through post-transcriptional regulation.

Hepatocellular carcinoma (HCC) has high fatality rate and limited therapeutic options. Here, we propose a new anti-HCC approach with high cancer-selectivity and efficient anticancer effects, based on adenovirus-mediated Tetrahymena group I trans-splicing ribozymes specifically inducing targeted suicide gene activity through HCC-specific replacement of telomerase reverse transcriptase (TERT) RNA. To confer potent anti-HCC effects and minimize hepatotoxicity, we constructed post-transcriptionally enhanced ribozyme constructs coupled with splicing donor and acceptor site and woodchuck hepatitis virus post-transcriptional regulatory element under the control of microRNA-122a (miR-122a). Adenovirus encoding post-transcriptionally enhanced ribozyme improved trans-splicing reaction and decreased human TERT (hTERT) RNA level, efficiently and selectively retarding hTERT-positive liver cancers. Adenovirus encoding miR-122a-regulated ribozyme caused selective liver cancer cytotoxicity, the efficiency of which depended on ribozyme expression level relative to miR-122a level. Systemic administration of adenovirus encoding the post-transcriptionally enhanced and miR-regulated ribozyme caused efficient anti-cancer effects at a single dose of low titers and least hepatotoxicity in intrahepatic multifocal HCC mouse xenografts. Minimal liver toxicity, tissue distribution, and clearance pattern of the recombinant adenovirus were observed in normal animals administered either systemically or via the hepatic artery. Post-transcriptionally regulated RNA replacement strategy mediated by a cancer-specific ribozyme provides a clinically relevant, safe, and efficient strategy for HCC treatment.

Improved Intrinsic Nonlinear Characteristics of Ta2O5/Al2O3-Based Resistive Random-Access Memory for High-Density Memory Applications.

The major hindrance for high-density application of two-terminal resistive random-access memory (RRAM) array design is unintentional sneak path leakage through adjacent cells. Herein, we propose a bilayer structure of Ta2O5/Al2O3-based bipolar type RRAM by evaluating the intrinsic nonlinear characteristics without integration with an additional transistor and selector device. We conducted X-ray photoelectron spectroscopy (XPS) analysis with different etching times to verify Ta2O5/Al2O3 layers deposited on the TiN bottom electrode. The optimized nonlinear properties with current suppression are obtained by varying Al2O3 thickness. The maximum nonlinearity (~71) is achieved in a Ta2O5/Al2O3 (3 nm) sample. Furthermore, we estimated the comparative read margin based on the I-V characteristics with different thicknesses of Al2O3 film for the crossbar array applications. We expect that this study about the effect of the Al2O3 tunnel barrier thickness on Ta2O5-based memristors could provide a guideline for developing a selector-less RRAM application.