GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors.

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

High Aspect Ratio β-GaO Fin Arrays with Low-Interface Charge Density by Inverse Metal-Assisted Chemical Etching.

β-GaO, with a bandgap of ∼4.6-4.9 eV and readily available bulk substrates, has attracted tremendous interest in the wide bandgap semiconductor community.

Sageretia thea Inhibits Inflammation through Suppression of NF- 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.

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.

Enhanced Performance of Ge Photodiodes via Monolithic Antireflection Texturing and α-Ge Self-Passivation by Inverse Metal-Assisted Chemical Etching.

Surface antireflection micro and nanostructures, normally formed by conventional reactive ion etching, offer advantages in photovoltaic and optoelectronic applications, including wider spectral wavelength ranges and acceptance angles. One challenge in incorporating these structures into devices is that optimal optical properties do not always translate into electrical performance due to surface damage, which significantly increases surface recombination. Here, we present a simple approach for fabricating antireflection structures, with self-passivated amorphous Ge (α-Ge) surfaces, on single crystalline Ge (c-Ge) surface using the inverse metal-assisted chemical etching technology (I-MacEtch).

Self-Anchored Catalyst Interface Enables Ordered Via Array Formation from Submicrometer to Millimeter Scale for Polycrystalline and Single-Crystalline Silicon.

Defying text definitions of wet etching, metal-assisted chemical etching (MacEtch), a solution-based, damage-free semiconductor etching method, is directional, where the metal catalyst film sinks with the semiconductor etching front, producing 3D semiconductor structures that are complementary to the metal catalyst film pattern. The same recipe that works perfectly to produce ordered array of nanostructures for single-crystalline Si (c-Si) fails completely when applied to polycrystalline Si (poly-Si) with the same doping type and level. Another long-standing challenge for MacEtch is the difficulty of uniformly etching across feature sizes larger than a few micrometers because of the nature of lateral etching.

Damage-Free Smooth-Sidewall InGaAs Nanopillar Array by Metal-Assisted Chemical Etching.

Producing densely packed high aspect ratio InGaAs nanostructures without surface damage is critical for beyond Si-CMOS nanoelectronic and optoelectronic devices. However, conventional dry etching methods are known to produce irreversible damage to III-V compound semiconductors because of the inherent high-energy ion-driven process. In this work, we demonstrate the realization of ordered, uniform, array-based InGaAs pillars with diameters as small as 200 nm using the damage-free metal-assisted chemical etching (MacEtch) technology combined with the post-MacEtch digital etching smoothing.

Direct Electrical Probing of Periodic Modulation of Zinc-Dopant Distributions in Planar Gallium Arsenide Nanowires.

Selective lateral epitaxial (SLE) semiconductor nanowires (NWs), with their perfect in-plane epitaxial alignment, ability to form lateral complex p-n junctions in situ, and compatibility with planar processing, are a distinctive platform for next-generation device development. However, the incorporation and distribution of impurity dopants in these planar NWs via the vapor-liquid-solid growth mechanism remain relatively unexplored. Here, we present a detailed study of SLE planar GaAs NWs containing multiple alternating axial segments doped with Si and Zn impurities by metalorganic chemical vapor deposition.

Sex-specific metabolic interactions between liver and adipose tissue in MCD diet-induced non-alcoholic fatty liver disease.

Higher susceptibility to metabolic disease in male exemplifies the importance of sexual dimorphism in pathogenesis. We hypothesized that the higher incidence of non-alcoholic fatty liver disease in males involves sex-specific metabolic interactions between liver and adipose tissue. In the present study, we used a methionine-choline deficient (MCD) diet-induced fatty liver mouse model to investigate sex differences in the metabolic response of the liver and adipose tissue.