Enhancement of skin regeneration through activation of signaling pathway by meyer non-edible callus-derived extracellular vesicles.

Background: This study aimed to investigate the effects of ginseng non-edible callus-derived extracellular vesicle (GNEV) on skin regeneration, particularly focusing on its impact on proliferation and migration in human dermal fibroblast (HDF).

Methods: GNEV was isolated from ginseng non-edible callus using sequential filtration and size exclusion chromatography (SEC). The extracellular vesicle was characterized using nanoparticle tracking analysis (NTA).

Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers.

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth.

Correlation Study Between TV Viewing Variables and Cognitive Level, Depression Level, and Activities of Daily Living in Older Individuals Living Alone.

Although there are studies on TV viewing and the health status of elderly, they do not present direct associations with specific variables. The aim of this study was to determine correlations between TV viewing variables and elderly health variables in older adults living alone. Data were collected from 50 elderly individuals with an average age of 82.

Compared Inhibitory Activities of Tamoxifen and Avenanthramide B on Liver Esterase and Correlation Based on the Superimposed Structure Between Porcine and Human Liver Esterase.

Exposure to tamoxifen can exert effects on the human liver, and esterases process prodrugs such as antibiotics and convert them to less toxic metabolites. In this study, the porcine liver esterase (PLE)-inhibitory activity of tamoxifen has been investigated. PLE showed inhibition of a PLE isoenzyme (PLE5).

Effect of extract on anti-inflammatory by inhibition of and cytokine levels.

Chronic inflammation is persistent disease caused by unregulated activation of pathway and production of pro-inflammatory cytokines, induced by accumulation of reactive oxygen species above thresholds. The purpose of this study was to evaluate the anti-oxidant, anti-lipidemia, inhibition pathway, and reduction of pro-inflammatory cytokine activities of extract (IDE) to demonstrate its inhibitory ability against chronic inflammatory in RAW264.7.

and Rosmarinic Acid Suppress the Growth and Metastasis of Gastric Cancer AGS Cells by Promoting Apoptosis and Inhibiting EMT.

Gastric cancer (GC) ranks as the fifth most prevalent cancer globally. Owing to the absence of early manifest symptoms, it is difficult to diagnose GC until it has metastasized to other organs. Hence, the prevention and treatment of GC have become major concerns for patients.

The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology.

Globally, life expectancy has been increasing with South Korea focusing on improving health to enhance quality of life. The COVID-19 pandemic further emphasized the need for digital transformation in healthcare, accelerating digital health adoption. This study explores the digital divide between 'Digital Natives (20-39 Y)' and 'Digital Immigrants (40-69 Y)', focusing on digital device usage and confidence.

Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes.

Cyclophilin A knockdown inhibits the proliferation and metastatic ability of AGS gastric cancer stem cells by downregulating CD147/STAT3/AKT/ERK and epithelial‑mesenchymal transition.

Gastric cancer stem cells (GCSCs) contribute to the challenging aspects of gastric cancer, such as progression, metastasis, treatment resistance and recurrence. Inhibitors targeting cyclophilin A (CypA) have shown potential in curtailing GCSC growth. Building upon this, the current study delved deeper into understanding the functional role of CypA in controlling the proliferation and metastatic capabilities of GCSCs, employing CypA‑specific small interfering RNA.

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.

Transcriptomic Analysis of the Combined Effects of Methyl Jasmonate and Wounding on Flavonoid and Anthraquinone Biosynthesis in .

Jasmonates, including jasmonic acid (JA) and its derivatives such as methyl jasmonate (MeJA) or jasmonly isoleucine (JA-Ile), regulate plant responses to various biotic and abiotic stresses. In this study, we applied exogenous MeJA onto leaves subjected to wounding and conducted a transcriptome deep sequencing analysis at 1 (T1), 3 (T3), 6 (T6), and 24 (T24) h after MeJA induction, along with the pretreatment control at 0 h (T0). Out of 18,883 mapped genes, we identified 10,048 differentially expressed genes (DEGs) between the T0 time point and at least one of the four treatment times.

The prevention and response to infectious diseases in long-term care facilities in Korea: a nationwide survey.

Objectives: Long-term care facilities (LTCFs) are communal environments for patients with chronic diseases or older adults, making them particularly susceptible to significant harm during infectious disease outbreaks. Nonetheless, LTCFs have historically been subject to less stringent infection prevention and control (IPC) mandates. This study aimed to assess the current state of LTCFs and to develop an IPC system tailored for these facilities following the coronavirus disease 2019 (COVID-19) pandemic.

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).

Influential factors affecting nursing performance amid COVID-19: A cross-sectional study on nurse preparedness for infectious diseases.

Background: The emergence of infectious diseases such as SARS, MERS, and COVID-19 underscores the need for effective nursing preparedness.

Objectives: This cross-sectional study sought to pinpoint the factors that impact nursing performance during the COVID-19 pandemic, focusing on nurses' self-efficacy, work environment, knowledge, and attitudes.

Design: The study utilized a cross-sectional design.

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.

Natural products with γ-pyrone scaffold from Streptomyces.

The Streptomyces sp. is considered the vast reservoir of bioactive natural products belonging to different classes like polyketides, terpenoids, lanthipeptides, and non-ribosomal peptides to name a few. The ubiquitous distribution of the genus makes them capable of producing distinct compounds.

Evaluation of the Properties of 3D-Printed Onyx-Fiberglass Composites.

This study evaluated the properties of 3D-printed Onyx-fiberglass composites. These composites were 3D-printed with zero, one, two, three, and four layers of fiberglass. Ten samples of each configuration were printed for the tensile and flexural tests.

In Silico Analysis and Biochemical Characterization of PET Hydrolase with Bis(2-Hydroxyethyl) Terephthalate Biodegradation Activity.

Polyethylene terephthalate (PET), one of the most widely used plastics in the world, causes serious environmental problems. Recently, scientists have been focused on the enzymatic degradation of PET, an environmentally friendly method that offers an attractive approach to the degradation and recycling of PET. In this work, PET hydrolase from sp.

Randomized Controlled Trial on the Effects of Home-Based Breathing Exercises on Respiratory Function and Fatigue in COVID-19-Cured Young Patients.

This study investigates the effects of home-based Kakao Healthcare breathing exercises and stretching on respiratory function and fatigue in COVID-19-cured patients. A total of 35 participants performed four movements of home-based breathing exercises and five respiratory muscle stretching exercises four times a week for four weeks. Respiratory function was measured using forced vital capacity(FVC), forced expiratory volume in one second(FEV1), FEV1/FVC ratio, and peak expiratory flow(PEF).

Daily Self-Monitoring and Feedback of Circadian Rhythm Measures in Major Depression and Bipolar Disorder Using Wearable Devices and Smartphones-The Circadian Rhythm for Mood (CRM®) Trial Protocol: A Randomized Sham Controlled Double-Blind Trial.

The circadian rhythm for mood (CRM) is a digital therapeutic, which aims to prevent mood episode and improve clinical course in patients with major mood disorders. Developed on the circadian rhythm hypothesis of mood disorder, CRM predicts the impending risk of mood episode with its built-in algorithm, utilizing wearable devices data and daily self-reports, and provides personalized feedback. In a pilot study of the CRM, the users experienced less frequent and shorter duration of mood episodes than the non-users.

Manipulation of feeding patterns in high fat diet fed rats improves microbiota composition dynamics, inflammation and gut-brain signaling.

Chronic consumption of high fat (HF) diets has been shown to increase meal size and meal frequency in rodents, resulting in overeating. Reducing meal frequency and establishing periods of fasting, independently of caloric intake, may improve obesity-associated metabolic disorders. Additionally, diet-driven changes in microbiota composition have been shown to play a critical role in the development and maintenance of metabolic disorders.

Carbon-Based Nanocomposite Membranes for Membrane Distillation: Progress, Problems and Future Prospects.

The development of an ideal membrane for membrane distillation (MD) is of the utmost importance. Enhancing the efficiency of MD by adding nanoparticles to or onto a membrane's surface has drawn considerable attention from the scientific community. It is crucial to thoroughly examine state-of-the-art nanomaterials-enabled MD membranes with desirable properties, as they greatly enhance the efficiency and reliability of the MD process.