In forensic medicine, age estimation commonly involves assessing age-related changes in teeth and skeletal structures. Vertebral morphological alterations, such as osteophyte formation, serve as age indicators. Recent studies using deep-learning techniques, such as neural networks, for age estimation from radiographic images have been conducted, reporting significantly higher accuracy than previous studies. This study aimed to estimate age using neural network-based deep-learning techniques applied to computed tomography (CT) cross-sectional images of the spine and evaluate its feasibility. Postmortem CT scans of 214 cadavers with varying decomposition levels were used. Coronal and sagittal cross-sectional images penetrating the center of each vertebral body were extracted for the 11th and 12th thoracic vertebrae and the first to fifth lumbar vertebrae. Using these images, along with the chronological ages of deceased individuals, an age estimation model was developed through regression analysis in PyTorch, employing a convolutional neural networks architecture with five-fold cross-validation. The model achieved a mean absolute error of 5.385 years, root mean squared error of 7.029 years, and coefficient of determination of 0.793. Although the sample size was relatively small, the results suggested the potential applicability of vertebral imagingbased age estimation in the Korean population. Further research using a larger dataset may improve the accuracy and reliability of the model.

Background and Objectives: Clinical characteristics and outcomes in acute heart failure (AHF) vary by phenotype. We assessed phenotype-specific features, treatment patterns, and outcomes in a nationwide Korean cohort. Methods: The Korean Heart Failure III registry prospectively enrolled 7,351 AHF admissions at 47 hospitals. Among 6,777 patients with available left ventricular ejection fraction (EF), phenotypes were defined as heart failure with reduced EF (HFrEF, ≤40%), mildly reduced EF (HFmrEF,41–49%), or preserved EF (HFpEF, ≥50%). The primary endpoint was a 12-month composite of all-cause death or heart transplantation, evaluated from index admission and, among hospital survivors, from discharge. We used inverse probability weighting (multinomial generalized boosted models with stabilized, trimmed weights) and weighted Cox proportional-hazards models to estimate hazard ratios (HRs). Results: Phenotype distribution was 58.9% HFrEF, 13.6% HFmrEF, and 27.5% HFpEF. Crude 12-month composite rates from index admission were 13.4% (HFrEF), 12.7% (HFmrEF), and 16.8% (HFpEF). After weighting, from index admission, HFmrEF (HR, 0.892; 95% confidence interval [CI], 0.731–1.088) and HFpEF (HR, 1.101; 95% CI, 0.939–1.291) did not differ from HFrEF; from discharge, HFpEF had modestly higher risk (HR, 1.207; 95% CI, 1.008–1.445) whereas HFmrEF did not (HR, 1.039; 95% CI, 0.844–1.279). Hyponatremia and chronic kidney disease were consistent adverse markers, while angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker use at discharge was protective. Conclusions Across the EF spectrum, phenotypes showed distinct profiles and risk. Postdischarge risk was modestly higher in HFpEF, supporting phenotype-tailored care and systematic discharge optimization in Korean patients with AHF.

Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Background: Calcineurin inhibitor (CNI) toxicity is a significant cause of graft dysfunction in kidney transplant recipients, yet distinguishing it from acute rejection (AR) and acute tubular necrosis (ATN) remains challenging. This study investigated the use of urinary mRNA biomarkers as a noninvasive tool for identifying CNI toxicity. Methods: We retrospectively enrolled 110 kidney transplant recipients and classified them into four groups based on pathological findings: stable graft function (n=35), CNI toxicity (n=25), AR (n=30), and ATN (n=20). Candidate biomarkers were selected using the GEO database. Urinary mRNA was extracted from cell pellets, reverse-transcribed, and quantified by real-time polymerase chain reaction. Results: Estimated glomerular filtration rates were comparable among the CNI toxicity, AR, and ATN groups. Four transcripts (LTF, NNMT, WFDC2, and HIF1A) were identified as candidate biomarkers. Urinary mRNA levels of LTF, NNMT, and HIF1A were significantly lower in the CNI toxicity group than the AR group. NNMT and HIF1A levels were also significantly lower than those observed in the ATN group. In contrast, WFDC2 levels did not differ significantly across groups. A three-gene signature (LTF, NNMT, and HIF1A) effectively differentiated CNI toxicity from AR and ATN (area under the curve [AUC], 0.867;95% confidence interval [CI], 0.787–0.947) and significantly enhanced the diagnostic performance of the clinical variable-based model (AUC increased from 0.776; 95% CI, 0.660–0.892, to 0.934; 95% CI, 0.881–0.986). Conclusions Urinary mRNA levels of LTF, NNMT, and HIF1A may serve as useful biomarkers for identifying CNI toxicity in kidney transplant recipients with graft dysfunction.

Purpose: Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry. Methods: In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected. Results: In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set. Conclusions The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.

With advancements in both pharmacologic and non-pharmacologic treatments, significant changes have occurred in heart failure (HF) management. The previous Korean HF registries, namely the Korea Heart Failure Registry (KorHF-registry) and Korean Acute Heart Failure Registry (KorAHF-registry), no longer accurately reflect contemporary acute heart failure (AHF) patients. Our objective is to assess contemporary AHF patients through a nationwide registry encompassing various aspects, such as clinical characteristics, management approaches, hospital course, and long-term outcomes of individuals hospitalized for AHF in Korea. This prospective observational multicenter cohort study (KorHF III) is organized by the Korean Society of Heart Failure. We aim to prospectively enroll 7,000 or more patients hospitalized for AHF at 47 tertiary hospitals in Korea starting from March 2018. Eligible patients exhibit signs and symptoms of HF and demonstrate either lung congestion or objective evidence of structural or functional cardiac abnormalities in echocardiography, or isolated right-sided HF. Patients will be followed up for up to 5 years after enrollment in the registry to evaluate long-term clinical outcomes. KorHF III represents the nationwide AHF registry that will elucidate the clinical characteristics, management strategies, and outcomes of contemporary AHF patients in Korea.

The COVID-19 Vaccine Safety Research Committee (CoVaSC) was established in November 2021 to address the growing need for independent, in-depth scientific evidence on adverse events (AEs) following coronavirus disease 2019 (COVID-19) vaccination. This initiative was requested by the Korea Disease Control and Prevention Agency and led by the National Academy of Medicine of Korea. In September 2022, the COVID-19 Vaccine Safety Research Center was established, strengthening CoVaSC’s initiatives. The center has conducted various studies on the safety of COVID-19 vaccines. During CoVaSC’s second research year, from September 29, 2022 to July 19, 2023, the center was restructured into 4 departments: Epidemiological Research, Clinical Research, Communication & Education, and International Cooperation & Policy Research. Its main activities include (1) managing CoVaSC and the COVID-19 Vaccine Safety Research Center, (2) surveying domestic and international trends in AE causality investigation, (3) assessing AEs following COVID-19 vaccination, (4) fostering international collaboration and policy research, and (5) organizing regular fora and training sessions for the public and clinicians. Causality assessments have been conducted for 27 diseases, and independent research has been conducted after organizing ad hoc committees comprising both epidemiologists and clinical experts on each AE of interest. The research process included protocol development, data analysis, interpretation of results, and causality assessment. These research outcomes have been shared transparently with the public and healthcare experts through various fora. The COVID-19 Vaccine Safety Research Center plans to continue strengthening and expanding its research activities to provide reliable, high-quality safety information to the public.

Objective: This study was performed to evaluate the efficacy and safety of lurasidone (160 mg/day) compared to quetiapine XR (QXR; 600 mg/day) in the treatment of acutely psychotic patients with schizophrenia. Methods: Patients were randomly assigned to 6 weeks of double-blind treatment with lurasidone 160 mg/day (n=105) or QXR 600 mg/day (n=105). Primary efficacy measure was the change from baseline to week 6 in Positive and Negative Syndrome Scale (PANSS) total score and Clinical Global Impressions severity (CGI-S) score. Adverse events, body measurements, and laboratory parameters were assessed. Results: Lurasidone demonstrated non-inferiority to QXR on the PANSS total score. Adjusted mean±standard error change at week 6 on the PANSS total score was -26.42±2.02 and -27.33±2.01 in the lurasidone and QXR group, respectively. The mean difference score was -0.91 (95% confidence interval -6.35–4.53). The lurasidone group showed a greater reduction in PANSS total and negative subscale on week 1 and a greater reduction in end-point CGI-S score compared to the QXR group. Body weight, body mass index, and waist circumference in the lurasidone group were reduced, with significantly lower mean change compared to QXR. Endpoint changes in glucose, cholesterol, triglycerides, and low-density lipoprotein levels were also significantly lower. The most common adverse drug reactions with lurasidone were akathisia and nausea. Conclusion Lurasidone 160 mg/day was found to be non-inferior to QXR 600 mg/day in the treatment of schizophrenia with comparable efficacy and tolerability. Adverse effects of lurasidone were generally tolerable, and beneficial effects on metabolic parameters can be expected.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders.In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.