Objective: The Daegu region experienced the first wave of the pandemic at the beginning of the coronavirus disease 2019 (COVID-19) outbreak in Korea. Other non-COVID-19-related treatments during a community outbreak, such as cardiovascular diseases, were expected to impact emergency departments. In acute myocardial infarctions, time is an important factor affecting the patient outcome. This study examined how community COVID-19 outbreak affected STsegment elevated myocardial infarction (STEMI) care in emergency departments. Methods: A retrospective analysis was performed on patients visiting five emergency departments in the Daegu area who were diagnosed with STEMI from February 18 to April 17 each year from 2018 to 2020. The demographic characteristics, prehospital variables, in-hospital time variables, and treatment results were collected. The cases were divided into the pre-COVID period and the COVID period for comparison. Results: The study included 254 patients (194 pre-COVID, 60 during COVID). The symptom-to-door time did not differ. Although the door-to-first doctor time was shortened (4 min vs. 2 min, P=0.01), the rate of coronary angiogram along with the door-to-angiogram time and the door-to-balloon time did not change. The length of stay in the emergency department was delayed during COVID-19 (median, 136 min vs. 404 min; P<0.01). The in-hospital length of stay and mortality were similar in both groups. Conclusion The time to treat STEMI was not delayed significantly during the first wave of the COVID-19 outbreak in the Daegu area compared with the pre-pandemic period. Mortality did not change. The length of stay was elongated significantly in the emergency department but not in the hospital.

Mesenchymal tumors of the breast, which originate from the mammary stroma, are rare accounting for only approximately 0.5%–1% of all breast tumors. Pathologically, they can exist on a spectrum, ranging from benign to malignant. Such tumors may present with nonspecific findings on breast imaging, including mammography, ultrasound, and MRI, which can lead to diagnostic challenges. In the 2019 revised 5th edition of the World Health Organization classification, breast mesenchymal tumors are categorized into six groups. The current pictorial essay aimed to explore the clinical, pathological, and imaging characteristics of representative lesions in each category according to this six-group classification, with the ultimate goal of enhancing awareness for early diagnosis.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

This case report presents an 81-year-old edentulous female patient with congenital cleft lip and palate, rehabilitated with a maxillary obturator and a mandibular complete denture. A defect in the hard palate causes nasal leakage and hypernasalization of speech. Velopharyngeal insufficiency due to a defect in the soft palate causes reflux during swallowing and decreased clarity of pronunciation.The anatomical structures, such as Passavant’s ridge, were considered to prevent respiratory problems when impression taking. We achieved satisfactory results including velopharyngeal closure for pronunciation, mastication, and swallowing, as well as improved aesthetics. Therefore, we report the process and considerations of the treatment.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Background: Although an appropriate weight management strategy is essential for obese individuals, weight loss can have adverse effects on bone mineral density (BMD). We conducted a systematic review of randomized controlled trials to evaluate changes in BMD after the implementation of various weight loss strategies. Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched to find articles published from database inception until June 2023. Randomized controlled trials of various treatments for obese patients that reported changes in BMD were selected. The primary outcome was BMD of the whole body, lumbar spine, and total hip, measured using dual X-ray absorptiometry. Results: Eighteen randomized controlled trials involving 2,510 participants with obesity were included in the analysis. At follow-up examination, the BMD of the lumbar spine decreased significantly after metabolic surgery (mean difference [MD]= –0.40 g/cm2 ; 95% confidence interval [CI], –0.73 to –0.07; I2 = 0%); lifestyle and pharmacological interventions did not result in a significant decrease in BMD at any location. Metabolic surgery also produced the most substantial difference in weight, with an MD of –3.14 (95% CI, –3.82 to –2.47). Conclusion This meta-analysis is the first to examine the effects of all categories of anti-obesity strategies, including the use of anti-obesity medications, on BMD. Bariatric metabolic surgery can have adverse effects on BMD. Moreover, medications can be used as a treatment for weight loss without compromising bone quality.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

To investigate the relationship between depression and AD, water avoidance stress (WAS) was induced for 10 days in both Tg2576 mice and wild-type (WT) mice. After WAS, memory function and depressive-like behavior were investigated in Tg2576 mice. Tg2576 WAS mice exhibited more depressive-like behaviors than WT WAS and Tg2576 control (CON) mice. Strikingly, Tg2576 CON mice showed more depressive-like behaviors than WT mice. Moreover, corticosterone and phospho-glucocorticoid receptor (p-GR) levels were also higher in Tg2576 WAS mice in comparison to Tg2576 CON mice. Spinster homologue 2 (SPNS2) is a member of non-ATP-dependent transporter. The role of SPNS2 was widely known as a sphingosine-1-phosphate (S1P) transporter, which export intracellular S1P from cells. Using GEO database to analyze SPNS2 gene expression changes in patients with AD and depression, we show that SPNS2 gene expression correlates with AD and depression. Interestingly, Tg2576 WAS mice displayed significantly increased levels of SPNS2 w1hen compared to Tg2576 CON counterparts. SPNS2 levels were also higher in Tg2576 CON mice in comparison with WT CON mice. Remarkably, we found a decrease in S1P brain levels and an increase in S1P serum levels of Tg2576 WAS mice in comparison with Tg2576 CON mice. Accordingly, WAS induced group further decreased S1P levels in the brains. However, the level in the serum further increased in comparison with non-induced group. Therefore, these results suggest that AD and depression could be associated, and that Tg2576 transgenic mice are more susceptible to stress-induced depression through the release of S1P by SPNS2 up-regulation.