Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Background: The life expectancy of people living with human immunodeficiency virus (PLWH) has significantly improved with advancements in antiretroviral therapy (ART). However, aging PLWH face a growing burden of noncommunicable diseases (NCDs), polypharmacy, and drug-drug interactions (DDIs), which pose challenges in their management. This study investigates the prevalence of NCDs, polypharmacy, and DDIs among PLWH aged ≥50 years in Korea and their impact on quality of life (QOL). Materials and Methods: A cross-sectional study was conducted among 243 PLWH aged ≥50 years receiving ART for at least three months at three university hospitals in Korea between January and July 2022. Data were collected through electronic medical records and personal interviews, assessing demographics, comorbidities, polypharmacy, ART adherence, and QOL using the Korean version of WHOQOL-HIV BREF scale. Potential DDIs were analyzed using the University of Liverpool HIV Drug Interaction Database, and potentially inappropriate medications (PIMs) were identified using the 2023 American Geriatrics Society Beers Criteria. We classified participants into three age groups:50–<65 years, 65–<75 years, and ≥75 years. Results: The prevalence of comorbidities was 71.6%, with older participants (≥75 years) showing a significantly higher burden, including bone diseases, osteoarthritis, and dementia (P<0.001). Polypharmacy was observed in 28.4% of participants and increased with age, with 53.3% of those aged ≥75 years taking ≥10 pills daily.Polypharmacy was associated with poorer QOL (71.6 vs. 76.6, P=0.010). Amber-flag DDIs were found in 81 participants (33.3%), most commonly involving metformin and divalent cations. No red-flag DDIs were identified.PIMs were observed in 6.6% of participants aged ≥65 years. Conclusion Aging PLWH in Korea face significant challenges from comorbidities, polypharmacy, and DDIs, which negatively impact QOL. Integrated, age-specific, and multidisciplinary care strategies are urgently needed to improve outcomes and ensure the well-being of older PLWH.

The extremely low frequency-electromagnetic field (ELF-EMF) refers to the frequency range of 0-300 Hz. It has been reported that it causes biological effects on cell survival, growth, and function. Structural changes were observed in the cell membranes of bacteria exposed to a certain intensity of ELF-EMF, indicating that exposure of bacteria to ELF-EMF can directly affect the cell membrane and affect the survival and growth of bacteria. The purpose of this study was to investigate the effect of the application of low frequency square wave positive voltages on Aggregatibactor actinomycetemcomitans (A. actinomycetemcomitans), a putative pathogen of periodontal disease. A square wave positive voltage output of 20 V or less at low frequency (0-300 Hz) was applied to A. actinomycetemcomitans in a range of 60 minutes. Changes in the population of bacteria were observed by absorbance measurement, colony forming unit (CFU/ml) evaluation, and high-resolution field-emission scanning electron microscopy (HR FE-SEM). The results show that the most effective offset and frequency in inhibiting bacterial growth are 0.7V and 7.83 Hz (Schumann resonance). As the applied time increased and the voltage increased, it was effective in inhibiting bacterial growth. These results led to the conclusion that bacterial growth can be inhibited even at low frequencies below 10 Hz, and it was experimentally proven that the frequency, voltage setting, and exposure time of ELF-EMF have a significant effect on reducing the growth of A. actinomycetemcomitans.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Background and Objectives: Beta-blockers (BBs) improve prognosis in heart failure (HF), which is mediated by lowering heart rate (HR). However, HR has no prognostic implication in atrial fibrillation (AF) and also BBs have not been shown to improve prognosis in heart failure with preserved ejection fraction (HFpEF) with AF. This study assessed the prognostic implication of BB in HFpEF with AF according to discharge HR. Methods: From the Korean Acute Heart Failure Registry, 687 patients with HFpEF and AF were selected. Study subjects were divided into 4 groups based on 75 beats per minute (bpm) of HR at discharge and whether or not they were treated with BB at discharge. Results: Of the 687 patients with HFpEF and AF, 128 (36.1%) were in low HR group and 121 (36.4%) were in high HR group among those treated with BB at discharge. In high HR group, HR at discharge was significantly faster in BB non-users (85.5±9.1 bpm vs. 89.2±12.5 bpm, p=0.005). In the Cox model, BB did not improve 60-day rehospitalization (hazard ratio, 0.93;95% confidence interval [95% CI], 0.35–2.47) or mortality (hazard ratio, 0.77; 95% CI, 0.22– 2.74) in low HR group. However, in high HR group, BB treatment at discharge was associated with 82% reduced 60-day HF rehospitalization (hazard ratio, 0.18; 95% CI, 0.04–0.81), but not with mortality (hazard ratio, 0.77; 95% CI, 0.20–2.98). Conclusions In HFpEF with AF, in patients with HR over 75 bpm at discharge, BB treatment at discharge was associated with a reduced 60-day rehospitalization rate.