Purpose: This study aimed to investigate the effects of postnatal systemic corticosteroids on neurodevelopment in very low birth weight (VLBW) preterm infants. Methods: This was a population-based study of the Korean Neonatal Network of VLBW infant born at 23+0 and 31+6 weeks of gestation between 2013 and 2020. VLBW preterm infants assessed using the Bayley Scales of Infant and Toddler Development, third edition (BSID-III) at 18–24 months of corrected age and 3 years of age were enrolled. The primary outcomes were BSID-III scores and neurodevelopmental delays, with scores of <85. Socioeconomic status and clinical variables were adjusted for using multivariate regression analyses. Results: In total, 517 infants were enrolled in this study. Among the 216 (41.8%) infants who received postnatal systemic corticosteroids, the rate of cognitive delay was significantly higher at 18–24 months of corrected age than at 3 years of age. The rates of language and motor delays were significantly higher both at 18–24 months of corrected age and at 3 years of age. When multivariate logistic regression was performed, postnatal systemic corticosteroid use was significantly associated with cognitive delay at 18–24 months of corrected age, but not at 3 years of age. There was no significant association between postnatal systemic corticosteroid use and language or motor delay at 18-24 months of corrected age or at 3 years of age after multivariate logistic regression. Conclusion Postnatal systemic corticosteroid use in VLBW preterm infants increased the risk of cognitive delay at 18–24 months of corrected age, but not at 3 years.

Objective: Apnea in newborns is defined as a respiratory pause of 20 seconds or longer, or apnea accompanied by bradycardia or cyanosis. To date, research on neonatal apnea has focused on premature infants born within 34 weeks of gestation. The aim of this study is to present clinical significance of apnea in full-term infants compared with late premature infants born over 34 weeks of gestation. Methods: In a retrospective study, we reviewed medical records of neonates born over 34 weeks of gestation hospitalized for apnea and their mothers from November 2020 to May 2024. A total of 124 neonates were collected and divided into full-term infants (n=54) and late preterm infants (n=70) groups. Results: The mean gestational age of full-term and late preterm infants was 38 +5 weeks and 35 +2weeks, and the mean birth weight was 3.16 kg and 2.14 kg. Apnea was associated with diseases in 44.4% of full-term infants and 38.6% of late preterm infants. The rates of multiple births, small for gestational age, and cesarean section deliveries were significantly higher in late preterm infants.Apnea occurred significantly earlier and recovered faster in full-term infants. Neurologic disease was significantly more occurred in full-term infants (P=0.021). Especially, cerebral infarction and seizure were diagnosed only in full-term infants. Conclusion Apnea occurred earlier in full-term infants and severe neurologic diseases were significantly more found in full-term infants compared with late preterm infants. A close examination is needed in full-term infants with apnea.

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: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

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.

Purpose: The purpose of this study was to develop an approach to alleviate the discomfort caused by sandbag compression after a bone marrow examination. This research examined the effects of applying a pressure hemostasis band on bleeding, pain, and discomfort at the bone marrow examination site. Methods: This study was conducted with a nonequivalent control group non-synchronized design. For 74 patients under evaluation who underwent bone marrow examination, sandbag compression was applied to the examination site in the control group (n=37), and a pressure hemostasis band was applied to the intervention group (n=37). In both groups, absolute bed rest was performed for two hours, and bleeding, pain, and discomfort at the examination site were measured. Results: After two hours of the bone marrow examination, there was no difference in bleeding on the gauze between the two groups (F=0.59, p=.444). Bleeding occurred in three patients in the intervention group and six in the control group (χ 2 =1.14, p=.479), with no cases of hematoma detected in either group. One hour post-examination, the control group experienced significantly higher pain (F=5.45, p=.022) and discomfort (F=5.68, p=.020) than the intervention group. However, pain and discomfort levels were similar between groups after two hours. Conclusion Compared to the sandbag compression group, the band application group showed no difference in bleeding and experienced less pain and discomfort at the examination site. This confirms that the pressure hemostasis band is a suitable alternative to sandbag compression in post-examination care.

Purpose: Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world. Materials and Methods: We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis. Results: NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs. Conclusion Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

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.

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.