The effect of the guide hole height on the accuracy of implant placement: an in-vitro study.

Objective: Guided implant surgery using three-dimensional (3-D) planning software and 3-D printed surgical guides has become a critical tool for enhancing accuracy. This study aims to determine the minimum guide hole height necessary to maintain implant placement accuracy.

Materials And Methods: Ten maxillary models with edentulous areas were created using CT and optical scan data.

PredCMB: Predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data.

Motivation: Microbiota-derived metabolites significantly impact host biology, prompting extensive research on metabolic shifts linked to the microbiota. Recent studies have explored both direct metabolite analyses and computational tools for inferring metabolic functions from microbial shotgun metagenome data. However, no existing tool specifically focuses on predicting changes in individual metabolite levels, as opposed to metabolic pathway activities, based on shotgun metagenome data.

The value-for-money assessment and funding arrangements for high-priced drugs in an era of uncertainty: a comparative analysis of national health technology assessment agencies in South Korea, England, Australia, and Canada.

Background: Innovative health technologies have increasingly emerged as a promising solution for patients with untreatable or challenging conditions. However, these technologies often come with expensive costs and limited evidence at the time of launch. This study assessed how these high-priced drugs with limited evidence were appraised and introduced in South Korea, England, Australia, and Canada, where cost-effectiveness analysis (CEA) generally plays a central role in pricing and reimbursement decisions.

Determinants of traditional Korean medicine utilization by individuals with physical disabilities: inference of causal relationships using Korea health panel data.

Background: The demand for health management services has grown among individuals with physical disabilities. It is noteworthy that a significant proportion of this demographic has sought the services of traditional Korean medicine (TKM). Nevertheless, there is a lack of research on the characteristics of TKM utilization within this population.

Role of 11β-hydroxysteroid dehydrogenase type 1 inhibition in the antiobesity effect of J2H-1702 on adipocytes and a high-fat diet-induced NASH model.

Obesity due to excessive body fat accumulation remains a global problem. Patients with obesity have high cortisol levels, and its dysregulation is caused by increased 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) levels. The effects and mechanism of J2H-1702, an 11β-HSD1 inhibitor, on nonalcoholic steatohepatitis (NASH) were explored.

The intersection of finTech adoption, HR competency potential, service innovation, and firm growth in the banking sectors using Entropy and TOPSIS.

The adoption of Financial Technology (FinTech), along with the enhancement of Human Resource (HR) competencies, service innovation, and firm growth, plays a crucial role in the development of the banking sector. Despite their importance, obtaining reliable results is often challenging due to the complex, high-dimensional correlations among various features that affect the industry. To address this issue, this research introduces a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Entropy-Weighted Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.

Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards.

Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.

Dynamic modeling of cellular states has emerged as a pivotal approach for understanding complex biological processes such as cell differentiation, disease progression, and tissue development. This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions.

Impact of Government Healthcare Policy Changes on Consumption and Human Movements During COVID-19: An Interrupted Time Series Analysis in Korea.

Background: The coronavirus disease 2019 (COVID-19) pandemic has altered daily behavioral patterns based on government healthcare policies, including consumption and movement patterns. We aimed to examine the extent to which changes in the government's healthcare policy have affected people's lives, primarily focusing on changes in consumption and population movements.

Methods: We collected consumption data using weekly credit card transaction data from the Hana Card Corporation and population mobility data using mobile phone data from SK Telecom in Seoul, South Korea.

Redox-Mediated Pyrene Electrolytes for Enhancing the Reversibility of Vertically Arranged Tin Electrodes in Seawater Batteries.

Seawater batteries (SWBs) have emerged as a next-generation battery technology that does not rely on lithium, a limited resource essential for lithium-ion batteries. Instead, SWBs utilize abundant sodium from seawater, offering a sustainable alternative to conventional battery technologies. Previous studies have demonstrated the feasibility of achieving high energy densities in SWB anodes using vertically aligned electrodes.

Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases.

Background: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).

Methods: Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions.

LFP via Nanoscale Surface Reforming with a Tiny Minimal Amount of Conductivity-Enhancing Material.

LiFePO (LFP) typically requires a conductive additive to improve its low ion and electron conductivity. In this study, we achieved significant enhancements in Li and electron mobility by applying a minimal amount of conductive material through a new coating process. The coin cell demonstrated an excellent capacity of 157.

Enhancing chemical signal transformation in lateral flow assays using aptamer-architectured plasmonic nanozymes and para-phenylenediamine.

The widespread adoption and commercialization of lateral flow assays (LFAs) for clinical diagnosis have been hindered by limitations in sensitivity, specificity, and the absence of quantitative data. To address these challenges, we developed aptamer-architectured gold nanoparticles as nanozymes that catalytically convert -phenylenediamine (PPD) into Bandrowski's base (BB), thereby amplifying signal strength and sensitivity. The physiochemical properties of the nanozymes were characterized and their specific binding efficiency was demonstrated using experimental studies.

Relationships between Chronic Cough and Asthma Control and Quality-of-Life in Patients with Severe Asthma: A 6-Month Longitudinal Analysis.

Background: The prevalence and clinical implications of chronic cough (CC) in patients with severe asthma receiving asthma treatment remain relatively unknown.

Objective: This study aimed to evaluate the relationships between CC and asthma control and quality-of-life (QoL) in patients with severe asthma through longitudinal analysis.

Methods: Baseline and 6-month follow-up data from the Korean Severe Asthma Registry were analyzed.

Recent advances in bioactive materials: Future perspectives and opportunities in oral cancer biosensing.

Bioactive materials and biosensing technologies are emerging as pivotal tools in the early detection and management of oral cancer, a disease characterized by high morbidity and mortality rates. Recent advancements in nanotechnology have facilitated the development of innovative biosensors that utilize bioactive materials for non-invasive diagnostics, particularly through salivary analysis. These biosensors, including electrochemical, optical, and molecular types, target specific biomarkers such as DNA, RNA, and proteins associated with oral cancer.

Factors associated with the detection of atrial fibrillation in patients with embolic stroke of undetermined source.

Background: Detection of atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS) is important for the secondary prevention of stroke. We investigated the factors associated with the detection of newly diagnosed AF in ESUS patients during follow-up.

Methods: Patients with acute ischemic stroke classified as ESUS were included.

Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm.

Agriculture is an essential component of human sustenance in this world. These days, with a growing population, we must significantly increase agricultural productivity to meet demand. Agriculture moved toward technologies as a result of the demand for higher yields with less resources.

Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering.

Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.

Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing.

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.

Security Evaluation of Provably Secure ECC-Based Anonymous Authentication and Key Agreement Scheme for IoT.

The proliferation of the Internet of Things (IoT) has worsened the challenge of maintaining data and user privacy. IoT end devices, often deployed in unsupervised environments and connected to open networks, are susceptible to physical tampering and various other security attacks. Thus, robust, efficient authentication and key agreement (AKA) protocols are essential to protect data privacy during exchanges between end devices and servers.

The Glucose-Lowering Effect of and D-Pinitol: Studies on Insulin Secretion in INS-1 Cells and the Reduction of Blood Glucose in Diabetic Rats.

Ice plant () is a vegetable with various therapeutic uses, one of which is its ability to prevent diabetes. The present study examined the insulin secretion effect related to the mechanism of action of ice plant extract (IPE) and its active compound D-pinitol in a rat insulin-secreting β-cell line, INS-1, as well as in diabetic rats. : The glucose-stimulated insulin secretion (GSIS) test and Western blotting were used to measure GSIS.

Effects of Metabolites Derived from Guava ( L.) Leaf Extract Fermented by on Hepatic Energy Metabolism via SIRT1-PGC1α Signaling in Diabetic Mice.

Background/objectives: Type 2 diabetes mellitus (T2DM) is considered a serious risk to public health since its prevalence is rapidly increasing worldwide despite numerous therapeutics. Insulin resistance in T2DM contributes to chronic inflammation and other metabolic abnormalities that generate fat accumulation in the liver, eventually leading to the progression of metabolic dysfunction-associated fatty liver disease (MAFLD). Recently, the possibility that microbial-derived metabolites may alleviate MAFLD through enterohepatic circulation has emerged, but the underlying mechanism remains unclear.

Emerging Role of the DREAM Complex in Cancer and Therapeutic Opportunities.

The DREAM (dimerization partner, RB-like, E2F, and multi-vulval class B) complex is an evolutionarily conserved transcriptional repression complex that coordinates nearly one thousand target genes, primarily associated with the cell cycle processes. The formation of the DREAM complex consequently inhibits cell cycle progression and induces cellular quiescence. Given its unique role in cell cycle control, the DREAM complex has gained significant interest across various physiological and pathological contexts, particularly in conditions marked by dysregulated cell cycles, such as cancer.

The Relationship Between Impulsivity Traits and In Vivo Cerebral Serotonin Transporter and Serotonin 2A Receptor Binding in Healthy Individuals: A Double-Tracer PET Study with C-11 DASB and C-11 MDL100907.

To elucidate the potential roles of presynaptic and postsynaptic serotonergic activity in impulsivity traits, we investigated the relationship between self-reported impulsiveness and serotonin transporter (5-HTT) and 5-HT2A receptors in healthy individuals. In this study, 26 participants completed 3-Tesla magnetic resonance imaging and positron emission tomography with [C]DASB and [C]MDL100907. To quantify 5-HTT and 5-HT2A receptor availability, the binding potential (BP) of [C]DASB and [C]MDL100907 was derived using the simplified reference tissue model with cerebellar gray matter as the reference region.