Background: Chromosomal alterations serve as diagnostic and prognostic markers in acute leukemia. Given the evolving landscape of chromosomal abnormalities in acute leukemia, we previously studied these over two periods. In this study, we investigated the frequency of these abnormalities and clinical trends in acute leukemia in Korea across three time periods. Methods: We retrospectively analyzed data from 1,787 patients with acute leukemia (319 children and 1,468 adults) diagnosed between 2006 and 2020. Conventional cytogenetics, FISH, and multiplex quantitative PCR were used for analysis. The patient groups were divided according to the following three study periods: 2006–2009 (I), 2010–2015 (II), and 2016–2020 (III). Results: Chromosomal aberrations were detected in 92% of patients. The PML::RARA translocation was the most frequent. Over the 15-yr period, chromosomal aberrations showed minimal changes, with specific fusion transcripts being common among patients.ALL was more prevalent in children than in adults and correlated significantly with the ETV6::RUNX1 and RUNX1::RUNX1T1 aberrations. The incidence of ALL increased during the three periods, with PML::RARA remaining common. Conclusions The frequency of chromosomal abnormalities in acute leukemia has changed subtly over time. Notably, the age of onset of adult AML has continuously increased. Our results may help in establishing diagnoses and clinical treatment strategies and developing various molecular diagnostic platforms.

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.

Coronavirus disease 2019 (COVID-19) has multiple negative impacts on the psychiatric health of both those previously infected and not infected with severe acute respiratory syndrome coronavirus 2. Moreover, the negative impacts of COVID-19 are closely associated with geographical region, culture, medical system, and ethnic background. We summarized the evidence of the impact of COVID-19 on the psychiatric health of the Korean population. This narrative review included thirteen research articles, which investigated the impact of COVID-19 on the psychiatric health of Koreans. COVID-19 survivors were reported to have a 2.4 times greater risk of developing psychiatric disorders compared to members of a control group, and anxiety and stress-related disorders were the most common newly diagnosed psychiatric illnesses. Studies also reported that COVID-19 survivors had a 3.33-fold higher prevalence of insomnia, a 2.72-fold higher prevalence of mild cognitive impairment, and a 3.09-fold higher prevalence of dementia compared to the control group. In addition, more than four studies have highlighted that the medical staff members, including nurses and medical students, exhibit a greater negative psychiatric impact of COVID-19. However, none of the articles investigated the biological pathophysiology or mechanism linking COVID-19 and the risk of diverse psychiatric disorders. Moreover, none of the studies were actual prospective studies. Thus, longitudinal studies are needed to more clearly elucidate the effect of COVID-19 on the psychiatric health of the Korean population. Lastly, studies focusing on preventing and treating COVID-19–associated psychiatric problems are needed to provide a benefit in real clinical settings.

Urine chloride has recently been suggested as a biomarker of renal tubule function in patients with nondialysis chronic kidney disease (CKD), as low urinary chloride concentration is associated with an increased risk of CKD progression. We investigate the association between urinary chloride excretion and the progression of coronary artery calcification (CAC). Methods: A total of 1,065 patients with nondialysis CKD were divided into tertiles by spot urine chloride-to-creatinine ratios. The 1st, 2nd, and 3rd tertiles were defined as low, moderate, and high urinary chloride excretion, respectively. The study outcome was CAC progression, which was defined as an increase in coronary artery calcium score of more than 200 Agatston units during the 4-year follow-up period. Results: Compared to moderate urinary chloride excretion, high urinary chloride excretion was associated with decreased risk of CAC progression (adjusted odds ratio, 0.379; 95% confidence interval, 0.190–0.757), whereas low urinary chloride excretion was not associated with risk of CAC progression. Restricted cubic spine depicted an inverted J-shaped curve, with a significant reduction in the risk of CAC progression in subjects with high spot urine chloride-to-creatinine ratios. Conclusion: High urinary chloride excretion is associated with decreased risk of CAC progression in patients with nondialysis CKD.

Background: The risk of device thrombosis and device-oriented clinical outcomes with bioresorbable vascular scaffold (BVS) was reported to be significantly higher than with contemporary drug-eluting stents (DESs). However, optimal device implantation may improve clinical outcomes in patients receiving BVS. The current study evaluated mid-term safety and efficacy of Absorb BVS with meticulous device optimization under intravascular imaging guidance. Methods: The SMART-REWARD and PERSPECTIVE-PCI registries in Korea prospectively enrolled 390 patients with BVS and 675 patients with DES, respectively. The primary endpoint was target vessel failure (TVF) at 2 years and the secondary major endpoint was patientoriented composite outcome (POCO) at 2 years. Results: Patient-level pooled analysis evaluated 1,003 patients (377 patients with BVS and 626 patients with DES). Mean scaffold diameter per lesion was 3.24 ± 0.30 mm in BVS group.Most BVSs were implanted with pre-dilatation (90.9%), intravascular imaging guidance (74.9%), and post-dilatation (73.1%) at proximal to mid segment (81.9%) in target vessel.Patients treated with BVS showed comparable risks of 2-year TVF (2.9% vs. 3.7%, adjusted hazard ratio [HR], 1.283, 95% confidence interval [CI], 0.487–3.378, P = 0.615) and 2-year POCO (4.5% vs. 5.9%, adjusted HR, 1.413, 95% CI, 0.663–3.012,P = 0.370) than those with DES. The rate of 2-year definite or probable device thrombosis (0.3% vs. 0.5%, P = 0.424) was also similar. The sensitivity analyses consistently showed comparable risk of TVF and POCO between the 2 groups. Conclusion With meticulous device optimization under imaging guidance and avoidance of implantation in small vessels, BVS showed comparable risks of 2-year TVF and device thrombosis with DES.