Enhancing Sleep and Reducing Occupational Stress Through Forest Therapy: A Comparative Study Across Job Groups.

Objective: The coronavirus disease-2019 (COVID-19) pandemic radically shifted occupational patterns, leading to increased telecommuting and related stressors. Healthcare providers, among the most impacted group, faced heightened risks and workplace changes. Our study examined the efficacy of forest therapy in alleviating work-induced stress across various professions, exploring the need for profession-specific stress-relief strategies in the post-pandemic workplace.

Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma.

Tertiary lymphoid structures (TLS) are associated with improved response in solid tumors treated with immune checkpoint blockade, but understanding of the prognostic and predictive value of TLS and the circumstances of their resolution is incomplete. Here we show that in hepatocellular carcinoma treated with neoadjuvant immunotherapy, high intratumoral TLS density at the time of surgery is associated with pathologic response and improved relapse-free survival. In areas of tumor regression, we identify a noncanonical involuted morphology of TLS marked by dispersion of the B cell follicle, persistence of a T cell zone enriched for T cell-mature dendritic cell interactions and increased expression of T cell memory markers.

A Study on the Development of Information and Communication Technology-Based Oral Functional Rehabilitation Exercise Program Content for Elderly People.

: This study was conducted to develop information and communication technology (ICT)-based oral functional rehabilitation exercise (OFRE) program content to effectively improve the oral function of the elderly people. : After selecting evidence-based effective OFRE items through systematic review, the final items were constructed through the validity evaluation of detailed items through an expert Delphi survey. The items were composed in a simple content form that can be performed directly and applied to ICT-based mobile applications.

Biomarkers in lymphedema assessment: integrating elastography and muti-frequency bioimpedance analysis.

Multi-frequency bioimpedance analysis (MFBIA) is used to measure lymphedema, but it is a biomarker that is sensitive to stiffness. Lymphedema is a condition that can be accompanied by stiffness, but no studies have considered this, so we tried to use non-invasive elastography as a biomarker for stiffness. This retrospective study included 102 patients with lymphedema, divided into two groups according to the elastography strain ratio: stiff group (elastography strain ratio <0.

Single-Administration Self-Boosting Microneedle Patch for The Treatment of Obesity.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are one of the most effective treatments for obesity. The current therapy associated with repeated subcutaneous injections to maintain the drug therapeutic effect causes patient compliance issues and raises environmental concerns (due to sharp biohazard waste from disposed syringes/needles). Herein, we report a programmable scheduled release microneedles (PSR-MNs) system for delivering Semaglutide (a GLP-1 RA agent with a half-life of ~ 7 days) to manage and treat obesity.

Semaphorin 3D promotes pancreatic ductal adenocarcinoma progression and metastasis through macrophage reprogramming.

Axon guidance molecules are frequently altered in pancreatic ductal adenocarcinoma (PDA) and influence PDA progression. However, the molecular mechanism remained unclear. Using genetically engineered mouse models to examine semaphorin 3D (SEMA3D), we identified a dual role for tumor- and nerve-derived SEMA3D in the malignant transformation of pancreatic epithelial cells and invasive PDA development.

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).

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models.

Adherence to the nutritional recommendations according to diabetes status in Korean adults: a cross-sectional study.

Background: Nutritional therapy plays a crucial role in diabetes management. Assessing adherence to nutritional recommendations is critical for evaluating whether the current status of nutrition education is appropriate. This study aimed to evaluate adherence to nutritional recommendations according to diabetes status using data from the Korean National Health and Nutrition Examination Survey from 2016 to 2019.

Claudin 7 suppresses invasion and metastasis through repression of a smooth muscle actin program.

Metastasis initiates when cancer cells escape from the primary tumor, which requires changes to intercellular junctions. Claudins are transmembrane proteins that form the tight junction, and their expression is reduced in aggressive breast tumors. However, claudins' roles during breast cancer metastasis remain unclear.

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.

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.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress.

Assessing maternity care access: impacts on cesarean sections and dystocia.

Background: As South Korea grapples with a declining birthrate, maternity care accessibility has become challenging. This study examines the association with labour intervention and pregnancy complication, specifically focusing on C-section and dystocia in maternity disparities.

Methods: Data from the South Korean NHIS-NID was used to analyze 1,437,186 women with childbirths between 2010 and 2015.