Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients.

Managing mood disorders poses challenges in counseling and drug treatment, owing to limitations. Counseling is the most effective during hospital visits, and the side effects of drugs can be burdensome. Patient empowerment is crucial for understanding and managing these triggers. The daily monitoring of mental health and the utilization of episode prediction tools can enable self-management and provide doctors with insights into worsening lifestyle patterns. In this study, we test and validate whether the prediction of future depressive episodes in individuals with depression can be achieved by using lifelog sequence data collected from digital device sensors. Diverse models such as random forest, hidden Markov model, and recurrent neural network were used to analyze the time-series data and make predictions about the occurrence of depressive episodes in the near future. The models were then combined into a hybrid model. The prediction accuracy of the hybrid model was 0.78; especially in the prediction of rare episode events, the F1-score performance was approximately 1.88 times higher than that of the dummy model. We explored factors such as data sequence size, train-to-test data ratio, and class-labeling time slots that can affect the model performance to determine the combinations of parameters that optimize the model performance. Our findings are especially valuable because they are experimental results derived from large-scale participant data analyzed over a long period of time.

GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type. RESULT: We propose a method to model compounds and proteins for compound-protein interaction prediction. A graph neural network is used to represent the compounds, and a convolutional layer extended with a bidirectional recurrent neural network framework, Long Short-Term Memory, and Gate Recurrent unit is used for protein sequence vectorization. The convolutional layer captures regulatory protein functions, while the recurrent layer captures long-term dependencies between protein functions, thus improving the accuracy of interaction prediction with compounds. A database of 7000 sets of annotated compound protein interaction, containing 1000 base length proteins is taken into consideration for the implementation. The results indicate that the proposed model performs effectively and can yield satisfactory accuracy regarding compound protein interaction prediction. CONCLUSION: The performance of GCRNN is based on the classification accordiong to a binary class of interactions between proteins and compounds The architectural design of GCRNN model comes with the integration of the Bi-Recurrent layer on top of CNN to learn dependencies of motifs on protein sequences and improve the accuracy of the predictions.

Monolithic lateral p-n junction GaAs nanowire diodes via selective lateral epitaxy.

Semiconductor p-n junctions are essential building blocks of electronic and optoelectronic devices. Although vertical p-n junction structures can be formed readily by growing in sequence, lateral p-n junctions normal to surface direction can only be formed on specially patterned substrates or by post-growth implantation of one type of dopant while protecting the oppositely doped side. In this study, we report the monolithic formation of lateral p-n junctions in GaAs nanowires (NWs) on a planar substrate sequentially through the Au-assisted vapor-liquid-solid selective lateral epitaxy using metalorganic chemical vapor deposition. p-type and n-type segments are formed by modulating the gas phase flow of p-type (diethylzinc) and n-type (disilane) precursorsin situduring nanowire growth, allowing independent sequential control of p- and n-doping levels self-aligned in-plane in a single growth run. The p-n junctions formed are electrically characterized by fabricating arrays of p-n junction NW diodes with coplanar ohmic metal contacts and two-terminalI-Vmeasurements. The lateral p-n diode exhibits a 2.15 ideality factor and a rectification ratio of ∼106. The electron beam-induced current measurement confirms the junction position. The extracted minority carrier diffusion length is much higher compared to those previously reported, suggesting a low surface recombination velocity in these lateral NWp-n diodes.

Anti-inflammatory activity of the extracts from Rodgersia podophylla leaves through activation of Nrf2/HO-1 pathway, and inhibition of NF-κB and MAPKs pathway in mouse macrophage cells.

OBJECTIVE: Recently, Rodgersia podophylla has been reported to exhibit anti-inflammatory activity. However, little is known about the potential mechanisms about its anti-inflammatory activity. We elucidated the anti-inflammatory mechanisms of leaves extracts from Rodgersia podophylla (RP-L) in RAW264.7 cells. MATERIALS AND METHODS: LPS-induced NO was measured by Griess and mRNA of pro-inflammatory mediators was analyzed by RT-PCR. Cell viability was measured using MTT assay. The protein level was analyzed by Western blot. RESULTS: RP-L significantly inhibited the production of the pro-inflammatory mediators such as NO, iNOS, IL-1ß and IL-6 in LPS-stimulated RAW264.7 cells. RP-L increased HO-1 expression in RAW264.7 cells, and the inhibition of HO-1 by ZnPP reduced the inhibitory effect of RP-L against LPS-induced NO production in RAW264.7 cells. Inhibition of p38, ROS and GSK3ß attenuated RP-L-mediated HO-1 expression. Inhibition of ROS inhibited p38 phosphorylation and GSK3ß expression induced by RP-L. In addition, inhibition of GSK3ß blocked RP-L-mediated p38 phosphorylation. RP-L induced nuclear accumulation of Nrf2, and inhibition of p38, ROS and GSK3ß abolished RP-L-mediated nuclear accumulation of Nrf2. Furthermore, RP-L blocked LPS-induced degradation of IκB-α and nuclear accumulation of p65. RP-L also attenuated LPS-induced phosphorylation of ERK1/2 and p38. In GC/MS analysis of RP-L, pyrogallol was detected as bioactive compound for anti-inflammatory activity of RP-L. Pyrogallol was observed to activate HO-1 expression through ROS/GSK3ß/p38/Nrf2/HO-1 signaling. CONCLUSIONS: Our results suggest that RP-L exerts potential anti-inflammatory activity by activating ROS/GSK3ß/p38/Nrf2/HO-1 signaling and inhibiting NF-κB and MAPK signaling in RAW264.7 cells. These findings suggest that RP-L may have great potential for the development of anti-inflammatory drug.

Extracts from Sageretia thea reduce cell viability through inducing cyclin D1 proteasomal degradation and HO-1 expression in human colorectal cancer cells.

BACKGROUND: Sageretia thea (S. thea) has been used as the medicinal plant for treating hepatitis and fevers in Korea and China. Recently, anticancer activity of S. thea has been reported, but the potential mechanism for the anti-cancer property of S. thea is still insufficient. Thus, we evaluated whether extracts from the leaves (STL) and branches (STB) of S. thea exert anticancer activity and elucidated its potential mechanism in SW480 cells. METHODS: MTT assay was performed for measuring cell viability. Western blot and RT-PCR were used for analyzing the level of protein and mRNA, respectively. RESULTS: Treatment of STL or STB decreased the cell viability and induced apoptosis in SW480 cells. Decreased level of cyclin D1 protein was observed in SW480 cells treated with STL or STB, but no change in cyclin D1 mRNA level was observed with the treatment of STL or STB. MG132 blocked downregulation of cyclin D1 protein by STL or STB. Thr286 phosphorylation of cyclin D1 by STL or STB occurred faster than downregulation of cyclin D1 protein in SW480 cells. When SW480 cells were transfected with T286A-cyclin D1, cyclin D1 degradation by STL or STB did not occur. Inhibition of GSK3ß and cyclin D1 nuclear export attenuated STL or STB-mediated cyclin D1 degradation. In addition, STL or STB increased HO-1 expression, and the inhibition of HO-1 attenuated the induction of apoptosis by STL or STB. HO-1 expression by STL or STB resulted from Nrf2 activation through ROS-dependent p38 activation. CONCLUSIONS: These results indicate that STL or STB may induce GSK3ß-dependent cyclin D1 degradation, and increase HO-1 expression through activating Nrf2 via ROS-dependent p38 activation, which resulted in the decrease of the viability in SW480 cells. These findings suggest that STL or STB may have great potential for the development of anti-cancer drug.

Anti-inflammatory effect of Vaccinium oldhamii stems through inhibition of NF-κB and MAPK/ATF2 signaling activation in LPS-stimulated RAW264.7 cells.

BACKGROUND: Vaccinium oldhamii (V. oldhamii) has been reported to exert a variety of the pharmacological properties such as anti-oxidant activity, anti-cancer activity, and inhibitory activity of α-amylase and acetylcholinesterase. However, the anti-inflammatory activity of V. oldhamii has not been studied. In this study, we aimed to investigate anti-inflammatory activity of the stem extracts from V. oldhamii, and to elucidate the potential mechanisms in LPS-stimulated RAW264.7 cells. METHODS: Cell viability was evaluated by MTT assay. The determination of NO and PGE2 production was performed using Griess reagent and Prostaglandin E2 ELISA Kit, respectively. The change of mRNA or protein level was evaluated by RT-PCR and Western blot. RESULTS: Among VOS, VOL and VOF, the inhibitory effect of NO and PGE2 production induced by LPS was highest in VOS treatment. Thus, VOS was selected for the further study. VOS dose-dependently blocked LPS-induced NO and PGE2 production by inhibiting iNOS and COX-2 expression, respectively. VOS inhibited the expression of pro-inflammatory cytokines such as IL-1ß, IL-6 and TNF-α. In addition, VOS suppressed TRAP activity and attenuated the expression of the osteoclast-specific genes such as NFATc1, c-FOS, TRAP, MMP-9, cathepsin K, CA2, OSCAR and ATPv06d2. VOS inhibited LPS-induced NF-κB signaling activation through blocking IκB-α degradation and p65 nuclear accumulation. VOS inhibited MAPK signaling activation by attenuating the phosphorylation of ERK1/2, p38 and JNK. Furthermore, VOS inhibited ATF2 phosphorylation and blocked ATF2 nuclear accumulation. CONCLUSIONS: These results indicate that VOS may exert anti-inflammatory activity by inhibiting NF-κB and MAPK/ATF2 signaling. From these findings, VOS has potential to be a candidate for the development of chemopreventive or therapeutic agents for the inflammatory diseases.

Heracleum moellendorffii roots inhibit the production of pro-inflammatory mediators through the inhibition of NF-κB and MAPK signaling, and activation of ROS/Nrf2/HO-1 signaling in LPS-stimulated RAW264.7 cells.

BACKGROUND: Heracleum moellendorffii roots (HM-R) have been long treated for inflammatory diseases such as arthritis, backache and fever. However, an anti-inflammatory effect and the specific mechanism of HM-R were not yet clear. In this study, we for the first time explored the anti-inflammatory of HM-R. METHODS: The cytotoxicity of HM-R against RAW264.7 cells was evaluated using MTT assay. The inhibition of NO and PGE2 production by HM-R was evaluated using Griess reagent and Prostaglandin E2 ELISA Kit, respectively. The changes in mRNA or protein level following HM-R treatment were assessed by RT-PCR and Western blot analysis, respectively. RESULTS: HM-R dose-dependently blocked LPS-induced NO and PGE2 production. In addition, HM-R inhibited LPS-induced overexpression of iNOS, COX-2, IL-1ß and IL-6 in RAW264.7 cells. HM-R inhibited LPS-induced NF-κB signaling activation through blocking IκB-α degradation and p65 nuclear accumulation. Furthermore, HM-R inhibited MAPK signaling activation by attenuating the phosphorylation of ERK1/2, p38 and JNK. HM-R increased nuclear accumulation of Nrf2 and HO-1 expression. However, NAC reduced the increased nuclear accumulation of Nrf2 and HO-1 expression by HM-R. In HPLC analysis, falcarinol was detected from HM-R as an anti-inflammatory compound. CONCLUSIONS: These results indicate that HM-R may exert anti-inflammatory activity by inhibiting NF-κB and MAPK signaling, and activating ROS/Nrf2/HO-1 signaling. These findings suggest that HM-R has a potential as a natural material for the development of anti-inflammatory drugs.

Association between TAAR6 polymorphisms and airway responsiveness to inhaled corticosteroids in asthmatic patients.

BACKGROUND: Genetic polymorphisms may be responsible for the wide variation in response to inhaled corticosteroids in asthmatic patients. We had previously reported that one polymorphism rs7772821, located on the 3'-UTR of trace amine-associated receptor 6 (TAAR6), is significantly associated with percentile changes in the forced expiratory volume in 1 s (%ΔFEV1) after inhaled corticosteroid treatment in asthmatics using a genome-wide association study. The aim of the present study was to validate the association between 15 single-nucleotide polymorphisms (SNPs) on the TAAR6 and airway responsiveness to inhaled corticosteroids in the asthmatics. METHODS: The %ΔFEV1 induced by 4 weeks' treatment with inhaled fluticasone propionate (1000 µg daily) was measured in 246 asthmatics. The 15 SNPs of TAAR6 were genotyped using a TaqMan assay. An association analysis between %ΔFEV1 and TAAR6 polymorphisms was carried out using a linear regression model controlling for age, sex, smoking status, presence of atopy, and baseline FEV1 as covariates. RESULTS: Among the 15 SNPs and seven haplotypes of TAAR6, rs7772821 (T>G) on the 3'-UTR showed the strongest correlation with inhaled corticosteroid-induced %ΔFEV1 (Pcorr=0.002 in the codominant model, Pcorr=0.03 in the dominant model, Pcorr=0.01 in the recessive model). The %ΔFEV1 of the rs7772821T>G minor homozygotes (60.77%) was higher than that of patients harboring either the rs7772821 T/G or T/T genotypes (21.32 and 31.60%, respectively). CONCLUSION: The TAAR6 rs7772821 polymorphism may be one of the important genetic factors for predicting the response to treatment with inhaled corticosteroids in asthmatics.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

BACKGROUND: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. OBJECTIVE: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. METHODS: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. RESULTS: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. CONCLUSION: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.

Predicting Protein-Ligand Binding Affinity (PLBA) is pivotal in drug development, as accurate estimations of PLBA expedite the identification of promising drug candidates for specific targets, thereby accelerating the drug discovery process. Despite substantial advancements in PLBA prediction, developing an efficient and more accurate method remains non-trivial. Unlike previous computer-aid PLBA studies which primarily using ligand SMILES and protein sequences represented as strings, this research introduces a Deep Learning-based method, the Enhanced Representation Learning on Protein-Ligand Graph Structured data for Binding Affinity Prediction (ERL-ProLiGraph). The unique aspect of this method is the use of graph representations for both proteins and ligands, intending to learn structural information continued from both to enhance the accuracy of PLBA predictions. In these graphs, nodes represent atomic structures, while edges depict chemical bonds and spatial relationship. The proposed model, leveraging deep-learning algorithms, effectively learns to correlate these graphical representations with binding affinities. This graph-based representations approach enhances the model's ability to capture the complex molecular interactions critical in PLBA. This work represents a promising advancement in computational techniques for protein-ligand binding prediction, offering a potential path toward more efficient and accurate predictions in drug development. Comparative analysis indicates that the proposed ERL-ProLiGraph outperforms previous models, showcasing notable efficacy and providing a more suitable approach for accurate PLBA predictions.

Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.

The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively.

Human activity recognition for analyzing stress behavior based on Bi-LSTM.

BACKGROUND: Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used non-invasive wearable physiological sensors. Moreover, deep learning techniques are becoming a significant tool for analyzing health data. OBJECTIVE: In this paper, we propose a human lifelog monitoring model for stress behavior recognition based on deep learning, which analyses stress levels during activity. The proposed approach considers activity and physiological data for recognizing physical activity and stress levels. METHODS: To tackle these issues, we proposed a model that utilizes hand-crafted feature generation techniques compatible with a Bidirectional Long Short-Term Memory (Bi-LSTM) based method for physical activity and stress level recognition. We have used a dataset called WESAD, collected using wearable sensors for model evaluation. This dataset presented four levels of stress emotion, including baseline, amusement, stress, and meditation. RESULTS: The following results are from the hand-crafted feature approaches compatible with the bidirectional LSTM model. The proposed model achieves an accuracy of 95.6% and an F1-score of 96.6%. CONCLUSION: The proposed HAR model efficiently recognizes stress levels and contributes to maintaining physical and mental well-being.

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction.

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning.

In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.

Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711.

BACKGROUND: Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source. RESULTS: While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway. CONCLUSIONS: Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

LLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM.

Life-Log is a term used for the daily monitoring of health conditions and recognizing anomalies from data generated by sensor devices. The development of smart sensors enables collection of health data, which can be considered as a solution to risks associated with personal healthcare by raising awareness regarding health conditions and wellness. Therefore, Life-Log analysis methods are important for real-life monitoring and anomaly detection. This study proposes a method for the improvement and combination of previous methods and techniques in similar fields to detect anomalies in health log data generated by various sensors. Recurrent neural networks with long short-term memory units are used for analyzing the Life-Log data. The results indicate that the proposed model performs more effectively than conventional health data analysis methods, and the proposed approach can yield a satisfactory accuracy in anomaly detection.

Elastocapillary Force Induced Alignment of Large Area Planar Nanowires.

Achieving large scale precise positioning of the vapor-liquid-solid (VLS) nanowires is one of the biggest challenges for mass production of nanowire-based devices. Although there have been many noteworthy progresses in postgrowth nanowire alignment method development over the past few decades, these methods are mostly suitable for low density applications only. For high density applications such as transistors, both high yield and density are required. Here, we report an elastocapillary force-induced nanowire-aligning method that is extremely simple, clean, and can achieve single/multiple nanowire arrays with up to 98.8% yield and submicron pitch between the nanowires.

Carotenogenesis in Haematococcus lacustris: role of protein tyrosine phosphatases.

In the present study, we examined the inhibitory effects of protein tyrosine phosphatase (PTPase) inhibitors, including sodium orthovanadate (SOV), ammonium molybdate (AM), and iodoacetamide (IA), on cell growth, accumulation of astaxanthin, and PTPase activity in the photosynthetic algae Haematococcus lacustris. PTPase activity was assayed spectrophotometrically and was found to be inhibited 60% to 90% after treatment with the inhibitors. SOV markedly abolished PTPase activity, significantly activating the accumulation of astaxanthin. These data suggest that the accumulation of astaxanthin in H. lacustris results from the concerted actions of several PTPases.

CMOS-Compatible Catalyst for MacEtch: Titanium Nitride-Assisted Chemical Etching in Vapor phase for High Aspect Ratio Silicon Nanostructures.

Metal-assisted chemical etching (MacEtch) is an emerging anisotropic chemical etching technique that has been used to fabricate high aspect ratio semiconductor micro- and nanostructures. Despite its advantages in unparalleled anisotropy, simplicity, versatility, and damage-free nature, the adaptation of MacEtch for silicon (Si)-based electronic device fabrication process is hindered by the use of a gold (Au)-based metal catalyst, as Au is a detrimental deep-level impurity in Si. In this report, for the first time, we demonstrate CMOS-compatible titanium nitride (TiN)-based MacEtch of Si by establishing a true vapor-phase (VP) MacEtch approach in order to overcome TiN-MacEtch-specific challenges. Whereas inverse-MacEtch is observed using conventional liquid phase MacEtch because of the limited mass transport from the strong adhesion between TiN and Si, the true VP etch leads to forward MacEtch and produces Si nanowire arrays by engraving the TiN mesh pattern in Si. The etch rate as a function of etch temperature, solution concentration, TiN dimension, and thickness is systematically characterized to uncover the underlying nature of MacEtching using this new catalyst. VP MacEtch represents a significant step toward scalability of this disruptive technology because of the high controllability of gas phase reaction dynamics. TiN-MacEtch may also have direct implications in embedded TiN-based plasmonic semiconductor structures for photonic applications.

Sageretia thea Inhibits Inflammation through Suppression of NF- κ B and MAPK and Activation of Nrf2/HO-1 Signaling Pathways in RAW264.7 Cells.

Sageretia thea (S. thea) commonly known as Chinese sweet plum or Chinese bird plum has been used for treating hepatitis and fevers in Korea and China. S. thea has been reported to exert anti-oxidant, anticancer and anti-human immunodeficiency virus activity. However, there is little study on the anti-inflammatory activity of S. thea. Thus, we evaluated the anti-inflammatory effect of extracts of leaves (ST-L) and branches (ST-B) from Sageretia thea in LPS-stimulated RAW264.7 cells. ST-L and ST-B significantly inhibited the production of the pro-inflammatory mediators such as NO, iNOS, COX-2, IL-1 ß and IL-6 in LPS-stimulated RAW264.7 cells. ST-L and ST-B blocked LPS-induced degradation of I κ B- α and nuclear accumulation of p65, which resulted in the inhibition of NF- κ B activation in RAW264.7 cells. ST-L and ST-B also attenuated the phosphorylation of ERK1/2, p38 and JNK in LPS-stimulated RAW264.7 cells. In addition, ST-L and ST-B increased HO-1 expression in RAW264.7 cells, and the inhibition of HO-1 by ZnPP reduced the inhibitory effect of ST-L and ST-B against LPS-induced NO production in RAW264.7 cells. Inhibition of p38 activation and ROS elimination attenuated HO-1 expression by ST-L and ST-B, and ROS elimination inhibited p38 activation induced by ST-L and ST-B. ST-L and ST-B dramatically induced nuclear accumulation of Nrf2, but this was significantly reversed by the inhibition of p38 activation and ROS elimination. Collectively, our results suggest that ST-L and ST-B exerts potential anti-inflammatory activity by suppressing NF- κ B and MAPK signaling activation, and activating HO-1 expression through the nuclear accumulation of Nrf2 via ROS-dependent p38 activation. These findings suggest that ST-L and ST-B may have great potential for the development of anti-inflammatory drug to treat acute and chronic inflammatory disorders.