Incidentally detected gallbladder polyps (GBPs) and gallbladder wall thickening (GBWT) are frequently encountered in clinical practice. However, characterizing GBPs and GBWT in asymptomatic patients can be challenging and may result in overtreatment, including unnecessary follow-ups or surgeries. The Korean Society of Abdominal Radiology (KSAR) Clinical Practice Guideline Committee has developed expert recommendations that focus on standardized imaging interpretation and follow-up strategies for both GBPs and GBWT, with support from the Korean Society of Radiology and KSAR. These guidelines, which address 24 key questions, aim to standardize the approach for the interpretation of imaging findings, reporting, imaging-based workups, and surveillance of incidentally detected GBPs and GBWT. This recommendation promotes evidence-based practice, facilitates communication between radiologists and referring physicians, and reduces unnecessary interventions.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Background: Chronic pain significantly affects daily activities, mental health, and the interpersonal relationships of patients. Consequently, physicians use various treatments to manage pain. This study investigated the perceptions of treatment, accompanying symptoms, and other problems in patients with chronic pain. Methods: The authors enrolled patients with chronic pain from 19 university hospitals in South Korea. Data was collected on age, gender, diagnosis, disease duration, severity of pain, perception of pain treatment, and accompanying symptoms or problems using an anonymous survey comprising 19 questions. Results: In total, 833 patients with chronic pain completed the survey, and 257 (31.0%) and 537 (64.5%) patientsexpressed concerns about the potential adverse effects of medication and opioid addiction, respectively. Personalitychanges such as irritability or anger were the most frequent accompanying symptoms in 507 (63.8%) patients, followed by depression and sleep disturbance in 462 (58.1%) and 450 (54.5%) patients, respectively. Depression (P = 0.001) and anxiety (P = 0.029) were more common among women, whereas divorce (P = 0.016), family conflict (P < 0.001), unemployment (P < 0.001), suicide attempts (P < 0.001), and restrictions on economic activity (P < 0.001) were more common among men. The frequency of accompanying symptoms, except for suicidal ideation,was higher in the younger patients aged ≤ 40 years than in the older patients aged > 40 years. Conclusions Many patients with chronic pain had concerns about adverse effects or medication tolerance and experienced anxiety, depression, or sleep disturbances. The prevalence of accompanying problems varies according to age and gender.

Accessory breast tissue can appear along the mammary ridge, extending from the axilla to the groin, with the axilla being the most common site. Malignancies arising in accessory breast tissue are rare, representing approximately 0.3%–0.6% of all breast cancer cases.When evaluating accessory breasts, it is essential to apply the same management strategies used for conventionally positioned breasts. This report presents a case of ductal carcinoma in situ originating from accessory breast tissue in the axilla of a 55-year-old woman. The diagnosis was established through a comprehensive assessment involving mammography, ultrasound, and magnetic resonance imaging. Since the axillary accessory breast tissue wasn’t initially included in the routine mammography, we added an axillary tail view to evaluate the extent and morphology of malignant microcalcifications.

Purpose: This study aimed to identify the key factors in nursing workforce management for pandemic response in general hospitals and long-term care hospitals and to analyze the relative importance of these factors. Methods: A validity test was conducted with experts to select four categories and 30 key factors related to nursing workforce management for pandemic response. Surveys were collected from 25 nursing managers in general hospitals and 21 nursing managers in long-term care hospitals, and the relative importance of the key factors was analyzed using the analytic hierarchy process method. Results: Differences were found between the two groups in the relative importance of nursing workforce management for pandemic response. Specifically, the highest-ranking category was “workforce recruitment and redeployment” for general hospitals, but “workforce support and protection” for long-term care hospitals. The most important factor regarding nursing workforce management was the “nurse-to-patient ratio” for both general and long-term care hospitals. Conclusion General and long-term care hospitals need to establish nursing workforce management strategies to effectively respond to pandemics with appropriate consideration of the relative importance and prioritization of key factors based on hospital characteristics.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to identify the key factors in nursing workforce management for pandemic response in general hospitals and long-term care hospitals and to analyze the relative importance of these factors. Methods: A validity test was conducted with experts to select four categories and 30 key factors related to nursing workforce management for pandemic response. Surveys were collected from 25 nursing managers in general hospitals and 21 nursing managers in long-term care hospitals, and the relative importance of the key factors was analyzed using the analytic hierarchy process method. Results: Differences were found between the two groups in the relative importance of nursing workforce management for pandemic response. Specifically, the highest-ranking category was “workforce recruitment and redeployment” for general hospitals, but “workforce support and protection” for long-term care hospitals. The most important factor regarding nursing workforce management was the “nurse-to-patient ratio” for both general and long-term care hospitals. Conclusion General and long-term care hospitals need to establish nursing workforce management strategies to effectively respond to pandemics with appropriate consideration of the relative importance and prioritization of key factors based on hospital characteristics.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.