No abstract available.
Animals ; Busulfan* ; Fetal Blood* ; Humans* ; Infant, Newborn* ; Liver* ; Mice* ; Umbilical Cord*

PURPOSE: Increase in the use of diagnostic imaging or occupational exposure to radiation have brought upon concerns on the safety and biological effects of low- or moderate-dose radiation. However, limited information is available on the effects of low or moderate dose radiation on human health. METHODS: Using C57BL/6 mice, we aimed to evaluate the biological effects of low- and moderate-dose radiation on the immune system. X-rays was chosen as a radiation source and we analyzed complete blood counts, various lymphocyte subsets and various cytokine levels after single fraction x-ray exposure (0.1 Gy, 1 Gy). RESULTS: No significant changes in the immunologic parameter of C57BL/6 mice were observed after radiation, except LIX (a cytokine equivalent to human CXCL5), that showed higher level after 0.1 Gy radiation compared to the control. CONCLUSION: We observed that a single fraction of low or moderate dose of X-ray radiation does not cause significant changes in the immune system of C57BL/6 mice. Further studies are necessary to elucidate the mechanism underlying our results.
Animals ; Blood Cell Count ; Diagnostic Imaging ; Humans ; Immune System* ; Lymphocyte Subsets ; Mice* ; Occupational Exposure ; Radiation Dosage

BACKGROUND: Humanized mouse models are still under development, and various protocols exist to improve human cell engraftment and function. METHODS: Fourteen NOD/SCID/IL-2Rγnull (NSG) mice (4‒5 wk old) were conditioned with busulfan and injected with human umbilical cord blood (hUCB)-derived CD34+ hematopoietic stem cells (HSC) via retro-orbital sinuses. The bone marrow (BM), spleen, and peripheral blood (PB) were analyzed 8 and 12 weeks after HSC transplantation. RESULTS: Most of the NSG mice tolerated the regimen well. The percentage of hCD45+ and CD19+ cells rose significantly in a time-dependent manner. The median percentage of hCD45+cells in the BM was 55.5% at week 8, and 67.2% at week 12. The median percentage of hCD45+ cells in the spleen at weeks 8 and 12 was 42% and 51%, respectively. The median percentage of hCD19+ cells in BM at weeks 8 and 12 was 21.5% and 39%, respectively (P=0.04). Similarly, the median percentage of hCD19+ cells in the spleen at weeks 8 and 12 was 10% and 24%, respectively (P=0.04). The percentage of hCD19+ B cells in PB was 23% at week 12. At week 8, hCD3+ T cells were barely detectable, while hCD7+ was detected in the BM and spleen. The percentage of hCD3+ T cells was 2‒3% at week 12 in the BM, spleen, and PB of humanized NSG mice. CONCLUSION: We adopted a simplified protocol for establishing humanized NSG mice. We observed a higher engraftment rate of human CD45+ cells than earlier studies without any significant toxicity. And human CD45+ cell engraftment at week 8 was comparable to that of week 12.
Animals ; B-Lymphocytes ; Bone Marrow ; Busulfan* ; Fetal Blood ; Hematopoietic Stem Cells ; Humans* ; Mice* ; Spleen ; T-Lymphocytes ; Umbilical Cord*

PURPOSE: Patients with unresectable, relapsed, or refractory osteosarcoma need a novel therapeutic agent. Metformin is a biguanide derivative used in the treatment of type II diabetes, and is recently gaining attention in cancer research. METHODS: We evaluated the effect of metformin against human osteosarcoma. Four osteosarcoma cell lines (KHOS/NP, HOS, MG-63, U-2 OS) were treated with metformin and cell proliferation was evaluated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Cell cycle progression and apoptosis were evaluated using flow cytometric analysis, and migration and wound healing assay were performed. Fourteen female Balb/c-nude mice received KHOS/NP cell grafts in their thigh, and were allowed access to metformin containing water (2 mg/mL) ad libitum. Tumor volume was measured every 3–4 days for a period of 4 weeks. RESULTS: Metformin had a significant antiproliferative effect on human osteosarcoma cells. In particular, metformin inhibited the proliferation and migration of KHOS/NP cells by activation of AMP-activated protein kinase and consequent inhibition of the mammalian target of rapamycin pathway. It also inhibited the proliferation of cisplatin-resistant KHOS/NP clone cells. Analysis of KHOS/NP xenograft Balb/c-nude models indicated that metformin displayed potent in vivo antitumor effects. CONCLUSION: Further studies are necessary to explore metformin's therapeutic potential and the possibilities for its use as an adjuvant agent for osteosarcoma.
AMP-Activated Protein Kinases ; Animals ; Apoptosis ; Cell Cycle ; Cell Line ; Cell Proliferation ; Clone Cells ; Female ; Heterografts ; Humans ; In Vitro Techniques* ; Metformin* ; Mice ; Osteosarcoma* ; Sirolimus ; Thigh ; Transplants ; Tumor Burden ; Water ; Wound Healing

PURPOSE: Features of epidermal growth factor receptor (EGFR) expression in osteosarcoma and in vitro efficacies of EGFR inhibitors against osteosarcoma cells were evaluated. MATERIALS AND METHODS: Thirty biopsy samples of osteosarcoma patients were retrospectively analyzed for EGFR protein expression by immunohistochemistry. Relationships between EGFR expression and clinicopathologic characteristics and treatment outcomes were evaluated. Four osteosarcoma cell lines were analyzed for EGFR and p-EGFR expression by western blotting. Efficacies of gefitinib and BIBW2992 on osteosarcoma cells were evaluated using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Tyrosine kinase domains in exons 18 to 21 were sequenced and gene expression analyses of EGFR and PTEN were performed in four osteosarcoma cell lines. RESULTS: EGFR protein was expressed in 27 (90%) samples (6 low, 12 intermediate, 9 high) and in three cell lines. Intermediate or high staining for EGFR was related to a tumor volume<150 mL (p<0.001) and histologic subtype other than osteoblastic type (p=0.03). However, EGFR expression was not associated with histologic response to preoperative chemotherapy or survival. Gefitinib and BIBW 2992 did not have any significant inhibitory effect on cell viabilities. DNA sequencing analysis revealed three osteosarcoma cell lines have single base changes at codon 2361 of exon 20 (G to A), without affecting translation results. Furthermore, no mutation was found to be associated with constitutive EGFR activation. CONCLUSION: In the present study, gefitinib and BIBW2992 were not effective against osteosarcoma cells. However, as osteosarcoma cells express EGFR, further studies are necessary to explore the potential of other therapeutic agents targeting EGFR.
Biopsy ; Blotting, Western ; Cell Line ; Cell Survival ; Codon ; Epidermal Growth Factor ; Exons ; Gene Expression ; Humans ; Immunohistochemistry ; Osteoblasts ; Osteosarcoma ; Protein-Tyrosine Kinases ; Quinazolines ; Receptor, Epidermal Growth Factor ; Retrospective Studies ; Sequence Analysis, DNA ; Tetrazolium Salts ; Thiazoles

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.

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.

Background: /Aim: Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT). Methods: We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1. Results: A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74). Conclusion The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.