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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

Synaptic Characteristics of Amorphous Boron Nitride-Based Memristors on a Highly Doped Silicon Substrate for Neuromorphic Engineering.

In this study, the resistive switching and synaptic properties of a complementary metal-oxide semiconductor-compatible Ti/a-BN/Si device are investigated for neuromorphic systems. A gradual change in resistance is observed in a positive SET operation in which Ti diffusion is involved in the conducting path. This operation is extremely suitable for synaptic devices in hardware-based neuromorphic systems. The isosurface charge density plots and experimental results confirm that boron vacancies can help generate a conducting path, whereas the conducting path generated by a Ti cation from interdiffusion forms is limited. A negative SET operation causes a considerable decrease in the formation energy of only boron vacancies, thereby increasing the conductivity in the low-resistance state, which may be related to RESET failure and poor endurance. The pulse transient characteristics, potentiation and depression characteristics, and good retention property of eight multilevel cells also indicate that the positive SET operation is more suitable for a synaptic device owing to the gradual modulation of conductance. Moreover, pattern recognition accuracy is examined by considering the conductance values of the measured data in the Ti/a-BN/Si device as the synaptic part of a neural network. The linear and symmetric synaptic weight update in a positive SET operation with an incremental voltage pulse scheme ensures higher pattern recognition accuracy.

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.

Poison severity score and sequential organ failure assessment score: Carbon monoxide poisoning prognosis.

PURPOSE: We aimed to examine the utility of the Poison Severity Score (PSS) and Sequential Organ Failure Assessment (SOFA) score as early prognostic predictors of short-term outcomes in patients with carbon monoxide (CO) poisoning. We hypothesized that both the PSS and the SOFA score would be useful prognostic tools. METHODS: This was retrospective observational study of patients with CO poisoning who presented to the emergency department and were admitted for more than 24 hours. We calculated PSS, the initial SOFA score, a second (2nd) SOFA score, and a 24-hour delta SOFA score. The primary outcome was reported as the cerebral performance category (CPC) scale score at discharge. We classified those with CPC 1-2 as the good outcome group and those with CPC 3-5 as the poor outcome group. RESULTS: This study included 192 patients: 174 (90.6%) belonged to the good outcome group, whereas 18 (9.4%) belonged to the poor outcome group. The PSS (1.00 [0.00, 1.00] vs 3.00 [3.00, 3.00], p < 0.001), initial SOFA (1.00 [0.00, 2.00] vs 4.00 [3.25, 6.00], p < 0.001), 2nd SOFA score (0.00 [0.00, 1.00] vs 4.00 [3.00, 7.00], p < 0.001), and 24-hour delta SOFA score (-1.00 [-1.00, 0.00] vs 0.00 [-1.00, 1.00], p = 0.047) of the good outcome group were significantly higher than those of the poor outcome group. The areas under the receiver operating characteristic curve for PSS and the initial SOFA and 2nd SOFA scores were 0.977 (95% confidence interval [CI] 0.944-0.993), 0.945 (95% CI 0.903-0.973), and 0.978 (95% CI 0.947-0.994), respectively. CONCLUSION: The PSS, initial SOFA score, and 2nd SOFA score predict acute poor outcome accurately in patients with CO poisoning.

Investigating the effects of nitrous oxide sedation on frontal-parietal interactions.

Although functional connectivity has received considerable attention in the study of consciousness, few studies have investigated functional connectivity limited to the sedated state where consciousness is maintained but impaired. The aim of the present study was to investigate changes in functional connectivity of the parietal-frontal network resulting from nitrous oxide-induced sedation, and to determine the neural correlates of cognitive impairment during consciousness transition states. Electroencephalography was acquired from healthy adult patients who underwent nitrous oxide inhalation to induce cognitive impairment, and was analyzed using Granger causality (GC). Periods of awake, sedation and recovery for GC between frontal and parietal areas in the delta, theta, alpha, beta, gamma and total frequency bands were obtained. The Friedman test with post-hoc analysis was conducted for GC values of each period for comparison. As a sedated state was induced by nitrous oxide inhalation, power in the low frequency band showed increased activity in frontal regions that was reversed with discontinuation of nitrous oxide. Feedback and feedforward connections analyzed in spectral GC were changed differently in accordance with EEG frequency bands in the sedated state by nitrous oxide administration. Calculated spectral GC of the theta, alpha, and beta frequency regions in the parietal-to-frontal direction was significantly decreased in the sedated state while spectral GC in the reverse direction did not show significant change. Frontal-parietal functional connectivity is significantly affected by nitrous oxide inhalation. Significantly decreased parietal-to-frontal interaction may induce a sedated state.

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.

Suppressive effects of coixol, glyceryl trilinoleate and natural products derived from Coix Lachryma-Jobi var. ma-yuen on gene expression, production and secretion of airway MUC5AC mucin.

In this study, we investigated whether natural products including coixol derived from Coix Lachryma-Jobi var. ma-yuen affect MUC5AC mucin gene expression, production and secretion from airway epithelial cells. Confluent NCI-H292 cells were pretreated with oleic acid, linoleic acid, glyceryl trilinoleate, beta-stigmasterol or coixol for 30 min and then stimulated with PMA (phorbol 12-myristate 13-acetate), EGF (epidermal growth factor) or TNF-α (tumor necrosis factor-α) for 24 h. The MUC5AC mucin gene expression, mucin protein production and secretion were measured by RT-PCR and ELISA. The results were as follows: (1) Oleic acid, linoleic acid, glyceryl trilinoleate, beta-stigmasterol and coixol inhibited the expression of MUC5AC mucin gene induced by PMA from NCI-H292 cells; (2) Oleic acid, linoleic acid, glyceryl trilinoleate, beta-stigmasterol and coixol also inhibited the production of MUC5AC mucin protein induced by the same inducers from NCI-H292 cells; (3) Coixol inhibited the expression of MUC5AC mucin gene and production of MUC5AC mucin protein, induced by EGF or TNF-α from NCI-H292 cells; (4) Coixol decreased PMA-induced MUC5AC mucin secretion from NCI-H292 cells. This result suggests that coixol, the characteristic component among the examined five natural products derived from C. Lachryma-Jobi var. ma-yuen, can regulate gene expression, production and secretion of mucin, by directly acting on airway epithelial cells.