Objective: To evaluate the performance of supplemental automated breast ultrasound (ABUS) added to mammography-based breast cancer screening for women with nondense breasts. Materials and Methods: A retrospective search of radiology databases at two tertiary institutions identified asymptomatic women with nondense breasts who underwent breast cancer screening using both digital mammography (DM) and supplemental ABUS between January 2020 and December 2023. We excluded women without sufficient follow-up data or those without an established final diagnosis, including histopathologic results. The performance measures of DM alone and ABUS combined with DM (ABUS plus DM) were compared. The primary outcome was the cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed for women with scattered fibroglandular density and almost entirely fatty breasts. Results: A total of 2,904 pairs of screening examinations were performed in 1,683 women (59 ± 10 years), detecting 26 cancers. In comparison with DM alone, ABUS plus DM showed higher CDR (9.0 vs. 7.9 per 1,000 examinations, P < 0.001), higher sensitivity (100% [26/26] vs. 88.5% [23/26], P < 0.001), and lower specificity (95.0% [2,735/2,878] vs. 97.9% [2,817/2,878], P < 0.001). In women with scattered fibroglandular density, ABUS increased the CDR from 7.4 to 8.5 per 1,000 examinations and improved the sensitivity from 87.0% [20/23] to 100% [23/23] (P < 0.001). In women with almost entirely fatty breasts, ABUS plus DM showed the same CDR (16.4 per 1,000 examinations) and sensitivity (100% [3/3]) as DM alone. Three cancers (11.5% [3/26]), all of which were stage T1N0, were detected only by supplemental ABUS. Conclusion Supplemental ABUS improved cancer detection and sensitivity in women with nondense breasts, with the benefits primarily observed in those with scattered fibroglandular density.

Background: Asthma remission has recently emerged as an aspirational treatment goal, yet its definition remains inconsistent across studies and expert groups. The absence of a standardized framework hampers its application in clinical practice and research, particularly in Korea where biologics use is rapidly increasing. This study aimed to establish a consensus definition of clinical remission in severe asthma among Korean experts. Methods: A two-round modified Delphi survey, followed by a focused third round, was conducted among 28 board-certified pulmonologists from the Korean Academy of Tuberculosis and Respiratory Diseases (KATRD). The questionnaire consisted of six domains and 27 items. Responses were analyzed using agreement rates, interquartile ranges, and content validity ratios to determine consensus levels. Results: Consensus was reached on defining clinical remission as a composite of no exacerbations, no systemic corticosteroid use, sustained symptom control (Asthma Control Test ≥20 on at least three occasions over 12 months), and stabilization and optimization of pulmonary function while on maintenance treatment. Experts agreed that pulmonary function should be assessed based on clinical judgment rather than absolute thresholds. Complete remission was additionally defined as fulfilling all clinical remission criteria with normalization of type 2 inflammation (blood eosinophils <300/μL and fractional exhaled nitric oxide <25 ppb). Conclusion This Delphi consensus provides a regionally relevant and pragmatic framework for defining remission in severe asthma. These statements may help guide clinical practice, inform guideline development, and support future research on remission as a treatment goal.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Although gemcitabine-based regimens are widely used as an effective treatment for pancreatic cancer, acquired resistance to gemcitabine has become an increasingly common problem. Therefore, a novel therapeutic strategy to treat gemcitabine-resistant pancreatic cancer is urgently required. Piceamycin has been reported to exhibit antiproliferative activity against various cancer cells; however, its underlying molecular mechanism for anticancer activity in pancreatic cancer cells remains unexplored. Therefore, the present study evaluated the antiproliferation activity of piceamycin in a gemcitabine-resistant pancreatic cancer cell line and patient-derived pancreatic cancer organoids. Piceamycin effectively inhibited the proliferation and suppressed the expression of alpha-actinin-4, a gene that plays a pivotal role in tumorigenesis and metastasis of various cancers, in gemcitabine-resistant cells. Long-term exposure to piceamycin induced cell cycle arrest at the G0/G1 phase and caused apoptosis. Piceamycin alsoinhibited the invasion and migration of gemcitabine-resistant cells by modulating focal adhesion and epithelial-mesenchymal transition biomarkers. Moreover, the combination of piceamycin and gemcitabine exhibited a synergistic antiproliferative activity in gemcitabine-resistant cells. Piceamycin also effectively inhibited patient-derived pancreatic cancer organoid growth and induced apoptosis in the organoids. Taken together, these findings demonstrate that piceamycin may be an effective agent for overcoming gemcitabine resistance in pancreatic cancer.

Previous studies suggested effectiveness of psilocybin in the field of mental health. FDA designated psilocybin as a “breakthrough therapy” for the treatment of treatment-resistant depression (TRD) in 2018. This paper provided a review of psilocybin’s potential role in treatment of depression by focusing on published clinical trials. Studies showed that psilocybin, an agonist on 5-HT2A receptors, manifests antidepressant and anxiolytic effects by increasing glutamate transmission, reducing brain inflammation, decreasing default mode network activity. In terms of clinical trials, eleven studies (six open-label and five double blinded randomized clinical trials, DB-RCTs) trials exploring psilocybin’s impact on depression were found. Among open-label studies, a pilot study on TRD patients demonstrated significant reductions in depressive symptoms after two psilocybin sessions. Psilocybin also improved cognitive bias associated with depression. Extension studies confirmed sustained improvements and high remission rates. Among five DB-RCTs, two showed that psilocybin led to significant reductions in anxiety and depression in cancer patients, and the improvements sustained for over 6 months. In MDD, psilocybin showed rapid reductions in depression, with higher remission rates compared to escitalopram in a DB-RCT. Another DB-RCT showed that psilocybin induced higher decrease in depression around 6 hours after their administrations than placebo. The last DB-RCT showed that in patients with TRD, a single dose of psilocybin 25 mg, but not psilocybin 10 mg, resulted in superior antidepressant effect than psilocybin 1 mg. Overall, psilocybin showed promise in treating depression and anxiety, with notable safety profiles. Further research should explore optimal dosages and long-term effects.

Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decade. The available reporting ICH guidelines are realistically possible in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

Background: Respiratory pathogen infections and air pollution are main causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Air pollution has a direct effect on the airway epithelial barrier and the immune system, which can have an influence on infection. However, studies on the relationship between respiratory infections and air pollutants in severe AECOPD are limited. Thus, the objective of this study was to investigate the correlation between air pollution and respiratory pathogen in severe AECOPD. Methods: This multicenter observational study was conducted by reviewing electronic medical records of patients with AECOPD at 28 hospitals in South Korea. Patients were divided into four groups according to the comprehensive air-quality index (CAI) used in Korea. Identification rates of bacteria and viruses of each group were analyzed. Results: Viral pathogens were identified in 270 (36.7%) of 735 patients. Viral identification rate was different (P = 0.012) according to air pollution. Specifically, the virus detection rate was 55.9% in the group of CAI ‘D’ with the highest air pollution. It was 24.4% in the group of CAI ‘A’ with the lowest air pollution. This pattern was clearly seen for influenza virus A (P = 0.042). When further analysis was performed with particulate matter (PM), the higher/lower the PM level, the higher/lower the virus detection rate. However, no significant difference was found in the analysis related to bacteria. Conclusion Air pollution may make COPD patients more susceptible to respiratory viral infections, especially influenza virus A. Thus, on days with poor air quality, COPD patients need to be more careful about respiratory infections.

Background and Objectives: Although the shortage of donor is a common problem worldwide, a significant portion of unutilized hearts are classified as marginal donor (MD) hearts. However, research on the correlation between the MD and the prognosis of heart transplantation (HTx) is lacking. This study was conducted to investigate the clinical impact of MD in HTx. Methods: Consecutive 73 HTxs during 2014 and 2021 in a tertiary hospital were analyzed.MD was defined as follows; a donor age >55 years, left ventricular ejection fraction <50%, cold ischemic time >240 minutes, or significant cardiac structural problems. Preoperative characteristics and postoperative hemodynamic data, primary graft dysfunction (PGD), and the survival rate were analyzed. Risk stratification by Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score was performed to examine the outcomes according to the recipient state. Each group was sub-divided into 2 risk groups according to the IMPACT score (low <10 vs. high ≥10). Results: A total of 32 (43.8%) patients received an organ from MDs. Extracorporeal membrane oxygenation was more frequent in the non-MD group (34.4% vs. 70.7, p=0.007) There was no significant difference in PGD, 30-day mortality and long-term survival between groups. In the subgroup analysis, early outcomes did not differ between low- and high-risk groups. However, the long-term survival was better in the low-risk group (p=0.01). Conclusions The outcomes of MD group were not significantly different from non-MD group. Particularly, in low-risk recipient, the MD group showed excellent early and longterm outcomes. These results suggest the usability of selected MD hearts without increasing adverse events.