Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: /Aim: Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT). Methods: We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1. Results: A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74). Conclusion The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.

Methyl salicylate is widely used in various topical products, including sports creams, ointments, patches, and oral hygiene products. These products are mainly used for localized treatment of musculoskeletal pain. Given their intended topical application, their ingestion can result in salicylic acid poisoning due to their high concentrations of methyl salicylate. Symptoms of salicylic acid poisoning may include dizziness, vomiting, hallucinations, seizures, and, in severe cases, unconsciousness, respiratory failure, and circulatory disorders. We report a case of a 71-year-old male who ingested Mensolatum Lotion to commit suicide and died.

Purpose: Cancer-related fatigue (CRF) is an important health concern among breast cancer survivors. However, CRF is apt to be dismissed as a minor discomfort by medical professionals. This study aimed to understand the experience of CRF in breast cancer survivors. Methods: We conducted a qualitative study using in-depth interviews and thematic analysis. The 14 participants were over the age of 40, complained of fatigue, and had been battling breast cancer for at least eight months to up to 16 years. Results: The analysis revealed four themes: traces of fighting cancer, inseparable part of the self, difficulty in daily life, and the body’s signals for care.Chronic fatigue initially disrupted the daily lives of the participants. As time passed, fatigue made it difficult for patients to fulfill their roles within their families, and gradually alienated friends and neighbors. Nevertheless, they tried to overcome the symptoms of fatigue by responding sensitively to the warning signs or changes in their bodies. Conclusion Breast cancer survivors often worry about relapse due to persistent fatigue after treatment completion. Therefore, the results of this study may be useful in developing strategies tailored for managing CRF using a multifaceted approach.

Gastrointestinal endoscopy plays an important diagnostic and therapeutic role in the field of gastrointestinal disease. As endoscopies have become more common due to the nationwide screening program for digestive cancer and an increasing interest in health among the general public, the risk of infection transmission between patients has emerged as a clinical challenge. Although endoscopes can become highly contaminated with secretions and blood during use, the thorough reprocessing of an endoscope before it is reused in subsequent patients can be difficult due to its complicated structure. Although the incidence of endoscopy-associated infections has been reported to be extremely low, compelling evidence suggests that the actual incidence is underestimated. It has been well established that endoscopes reprocessed appropriately, in accordance with standard guidelines, have no risk of infection transmission. Although revised guidelines for endoscope reprocessing were released in Korea in 2015, suboptimal infection prevention practices during endoscope reprocessing have been reported. Under these circumstances, the Korean Society of Digestive Endoscopy developed the ‘Guidelines of cleaning and disinfection in gastrointestinal endoscope for clinicians’ based on the currently available evidence. These guidelines provide accurate and updated information on reprocessing techniques, and can help improve the quality of reprocessing and compliance by health care personnel. As a result, infection control during gastrointestinal endoscopies can be expected to be achieved in Korea.
Compliance ; Delivery of Health Care ; Disease Transmission, Infectious ; Disinfection ; Endoscopes ; Endoscopes, Gastrointestinal ; Endoscopy ; Endoscopy, Gastrointestinal ; Gastrointestinal Diseases ; Humans ; Incidence ; Infection Control ; Korea ; Mass Screening

Gastrointestinal endoscopy plays an important diagnostic and therapeutic role in the field of gastrointestinal disease. As endoscopies have become more common due to the nationwide screening program for digestive cancer and an increasing interest in health among the general public, the risk of infection transmission between patients has emerged as a clinical challenge. Although endoscopes can become highly contaminated with secretions and blood during use, the thorough reprocessing of an endoscope before it is reused in subsequent patients can be difficult due to its complicated structure. Although the incidence of endoscopy-associated infections has been reported to be extremely low, compelling evidence suggests that the actual incidence is underestimated. It has been well established that endoscopes reprocessed appropriately, in accordance with standard guidelines, have no risk of infection transmission. Although revised guidelines for endoscope reprocessing were released in Korea in 2015, suboptimal infection prevention practices during endoscope reprocessing have been reported. Under these circumstances, the Korean Society of Digestive Endoscopy developed the ‘Guidelines of cleaning and disinfection in gastrointestinal endoscope for clinicians’ based on the currently available evidence. These guidelines provide accurate and updated information on reprocessing techniques, and can help improve the quality of reprocessing and compliance by health care personnel. As a result, infection control during gastrointestinal endoscopies can be expected to be achieved in Korea.

Selecting an appropriate antigen with optimal immunogenicity and physicochemical properties is a pivotal factor to develop a protein based subunit vaccine. Despite rapid progress in modern molecular cloning and recombinant protein technology, there remains a huge challenge for purifying and using protein antigens rich in hydrophobic domains, such as membrane associated proteins. To overcome current limitations using hydrophobic proteins as vaccine antigens, we adopted in silico analyses which included bioinformatic prediction and sequence-based protein 3D structure modeling, to develop a novel periodontitis subunit vaccine against the outer membrane protein FomA of Fusobacterium nucleatum. To generate an optimal antigen candidate, we predicted hydrophilicity and B cell epitope parameter by querying to web-based databases, and designed a truncated FomA (tFomA) candidate with better solubility and preserved B cell epitopes. The truncated recombinant protein was engineered to expose epitopes on the surface through simulating amino acid sequence-based 3D folding in aqueous environment. The recombinant tFomA was further expressed and purified, and its immunological properties were evaluated. In the mice intranasal vaccination study, tFomA significantly induced antigen-specific IgG and sIgA responses in both systemic and oral-mucosal compartments, respectively. Our results testify that intelligent in silico designing of antigens provide amenable vaccine epitopes from hard-to-manufacture hydrophobic domain rich microbial antigens.
Animals ; Cloning, Molecular ; Computational Biology ; Computer Simulation* ; Epitopes ; Epitopes, B-Lymphocyte ; Fusobacterium nucleatum* ; Fusobacterium* ; Hydrophobic and Hydrophilic Interactions ; Immunoglobulin A, Secretory ; Immunoglobulin G ; Membrane Proteins ; Mice ; Periodontitis ; Solubility ; Vaccination