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.

Probiotics exert various effects on the body and provide different health benefits. Previous reports have demonstrated that the P8 protein (P8), isolated from Lactobacillus rhamnosus, has anticancer properties. However, its efficacy in stem cells and normal cells has not been reported. In this study, the effect of P8 on cell proliferation and wound healing was evaluated, investigating its underlying mechanism. Based on scratch assay results, we demonstrated that P8 treatment significantly increases wound healing by activating the cell cycle and promoting stem cell stemness.Cellular mechanisms were further investigated by culturing stem cells in a medium containing Lactobacillus-derived P8 protein, revealing its promotion of cell proliferation and migration. Also, it is found that P8 enhances the expression of stemness markers, such as OCT4 and SOX2, along with activation of the mitogen-activated protein kinase (MAPK) signaling and Hippo pathways. These results indicate that P8 can promote cell growth by increasing stem cell proliferation, migration, and stemness in a manner associated with MAPK and Hippo signaling, which could contribute to the increased wound healing after P8 treatment. Furthermore, P8 could promote wound healing in keratinocytes by activating the MAPK signaling pathways. These results suggest that P8 might be a promising candidate to enhance stem cell culture efficiency by activating cell proliferation, and enhance therapeutic effects in skin diseases.

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.

Probiotics exert various effects on the body and provide different health benefits. Previous reports have demonstrated that the P8 protein (P8), isolated from Lactobacillus rhamnosus, has anticancer properties. However, its efficacy in stem cells and normal cells has not been reported. In this study, the effect of P8 on cell proliferation and wound healing was evaluated, investigating its underlying mechanism. Based on scratch assay results, we demonstrated that P8 treatment significantly increases wound healing by activating the cell cycle and promoting stem cell stemness.Cellular mechanisms were further investigated by culturing stem cells in a medium containing Lactobacillus-derived P8 protein, revealing its promotion of cell proliferation and migration. Also, it is found that P8 enhances the expression of stemness markers, such as OCT4 and SOX2, along with activation of the mitogen-activated protein kinase (MAPK) signaling and Hippo pathways. These results indicate that P8 can promote cell growth by increasing stem cell proliferation, migration, and stemness in a manner associated with MAPK and Hippo signaling, which could contribute to the increased wound healing after P8 treatment. Furthermore, P8 could promote wound healing in keratinocytes by activating the MAPK signaling pathways. These results suggest that P8 might be a promising candidate to enhance stem cell culture efficiency by activating cell proliferation, and enhance therapeutic effects in skin diseases.

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.

Probiotics exert various effects on the body and provide different health benefits. Previous reports have demonstrated that the P8 protein (P8), isolated from Lactobacillus rhamnosus, has anticancer properties. However, its efficacy in stem cells and normal cells has not been reported. In this study, the effect of P8 on cell proliferation and wound healing was evaluated, investigating its underlying mechanism. Based on scratch assay results, we demonstrated that P8 treatment significantly increases wound healing by activating the cell cycle and promoting stem cell stemness.Cellular mechanisms were further investigated by culturing stem cells in a medium containing Lactobacillus-derived P8 protein, revealing its promotion of cell proliferation and migration. Also, it is found that P8 enhances the expression of stemness markers, such as OCT4 and SOX2, along with activation of the mitogen-activated protein kinase (MAPK) signaling and Hippo pathways. These results indicate that P8 can promote cell growth by increasing stem cell proliferation, migration, and stemness in a manner associated with MAPK and Hippo signaling, which could contribute to the increased wound healing after P8 treatment. Furthermore, P8 could promote wound healing in keratinocytes by activating the MAPK signaling pathways. These results suggest that P8 might be a promising candidate to enhance stem cell culture efficiency by activating cell proliferation, and enhance therapeutic effects in skin diseases.

Background: Thyroid-stimulating hormone (TSH)-secreting pituitary neuroendocrine tumor (TSH PitNET) is a rare subtype of PitNET. We investigated the comprehensive characteristics and outcomes of TSH PitNET cases from a single medical center. Also, we compared diagnostic methods to determine which showed superior sensitivity. Methods: A total of 17 patients diagnosed with TSH PitNET after surgery between 2002 and 2022 in Samsung Medical Center was retrospectively reviewed. Data on comprehensive characteristics and treatment outcomes were collected. The sensitivities of diagnostic methods were compared. Results: Seven were male (41%), and the median age at diagnosis was 42 years (range, 21 to 65); the median follow-up duration was 37.4 months. The most common (59%) initial presentation was hyperthyroidism-related symptoms. Hormonal co-secretion was present in four (23%) patients. Elevated serum alpha-subunit (α-SU) showed the greatest diagnostic sensitivity (91%), followed by blunted response at thyrotropin-releasing hormone (TRH) stimulation (80%) and elevated sex hormone binding globulin (63%). Fourteen (82%) patients had macroadenoma, and a specimen of one patient with heavy calcification was negative for TSH. Among 15 patients who were followed up for more than 6 months, 10 (67%) achieved hormonal and structural remission within 6 months postoperatively. A case of growth hormone (GH)/TSH/prolactin (PRL) co-secreting mixed gangliocytoma-pituitary adenoma (MGPA) was discovered. Conclusion The majority of the TSH PitNET cases was macroadenoma, and 23% showed hormone co-secretion. A rare case of GH/TSH/PRL co-secreting MGPA was discovered. Serum α-SU and TRH stimulation tests showed great diagnostic sensitivity. Careful consideration is needed in diagnosing TSH PitNET. Achieving remission requires complete tumor resection. In case of nonremission, radiotherapy or medical therapy can improve the long-term remission rate.

Granular cell tumor is a rare disease, and it is even rarer in the male breast. Although it is typically a benign tumor, due to its features and image findings, it can be easily misdiagnosed and managed as a malignant tumor. Therefore, the extent of the surgery can inappropriately be expanded. To avoid misdiagnosis and overtreatment, surgeons must perform a careful evaluation. We describe a case of a granular cell tumor of the male breast treated with mastectomy.

Purpose: Renal tumors account for approximately 7% of all childhood cancers. These include Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor of the kidney (MRTK), renal cell carcinoma (RCC), congenital mesoblastic nephroma (CMN) and other rare tumors. We investigated the epidemiology of pediatric renal tumors in Korea. Materials and Methods: From January 2001 to December 2015, data of pediatric patients (0–18 years) newly-diagnosed with renal tumors at 26 hospitals were retrospectively analyzed. Results: Among 439 patients (male, 240), the most common tumor was WT (n=342, 77.9%), followed by RCC (n=36, 8.2%), CCSK (n=24, 5.5%), MRTK (n=16, 3.6%), CMN (n=12, 2.7%), and others (n=9, 2.1%). Median age at diagnosis was 27.1 months (range 0-225.5) and median follow-up duration was 88.5 months (range 0-211.6). Overall, 32 patients died, of whom 17, 11, 1, and 3 died of relapse, progressive disease, second malignant neoplasm, and treatment-related mortality. Five-year overall survival and event free survival were 97.2% and 84.8% in WT, 90.6% and 82.1% in RCC, 81.1% and 63.6% in CCSK, 60.3% and 56.2% in MRTK, and 100% and 91.7% in CMN, respectively (p < 0.001). Conclusion The pediatric renal tumor types in Korea are similar to those previously reported in other countries. WT accounted for a large proportion and survival was excellent. Non-Wilms renal tumors included a variety of tumors and showed inferior outcome, especially MRTK. Further efforts are necessary to optimize the treatment and analyze the genetic characteristics of pediatric renal tumors in Korea.