Background: Elevated active glucocorticoids (GCs) are implicated in skin barrier dysfunction, notably in aging and diabetes. The enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) converts inactive GCs to active forms, potentially exacerbating this dysfunction. Objective: We aimed to investigate the impact of a novel 11β-HSD1 inhibitor on skin inflammation using both in vitro and in vivo models. Methods: To elucidate the efficacy of a new 11β-HSD1 inhibitor in mitigating skin inflammation induced by various triggers, including dexamethasone treatment, ultraviolet B irradiation, and high glucose levels, in cultured human keratinocytes and the db/db mice as a type 2 diabetes murine model. In cultured keratinocytes, we assessed the effects of the 11β-HSD1 inhibitor on cortisol levels, 11β-HSD1 expression, and cytokine production under conditions simulating inflammation. In db/db mice, we evaluated the inhibitor’s impact on skin barrier function, hemoglobin A1c (HbA1c) levels, corticosterone levels, 11β-HSD1 expression, and cytokine profiles following a 2-week treatment regimen. Results: Our results demonstrated that both the novel 11β-HSD1 inhibitor and a known inhibitor reduced cortisol levels, 11β-HSD1 expression, and inflammatory cytokine production in cultured keratinocytes. In db/db mice, treatment with either inhibitor improved skin barrier function, lowered serum HbA1c levels, and decreased corticosterone, 11β-HSD1, and inflammatory cytokine expression. Conclusion A new 11β-HSD1 inhibitor, “11b-0048,” showed a significant inhibitory effect on the expression of 11β-HSD1 in keratinocytes activated by various conditions and diabetic skin.

Purpose: Chronic myeloid leukemia (CML) in children, adolescents, and young adults is rare and differs from older adults. This study evaluated the outcomes of allogeneic hematopoietic stem cell transplantation (HSCT) in young Korean CML patients during the tyrosine kinase inhibitor (TKI) era. Materials and Methods: A retrospective analysis of 35 CML patients aged < 40 years who underwent allogeneic HSCT from 2009 to 2019 was conducted using Korean Blood and Marrow Transplantation Registry data. Patients were grouped by age < 20 years at HSCT (group 1, n=15) and 20-40 years at HSCT (group 2, n=20). Survival outcomes including overall survival (OS), relapse-free survival (RFS), and event-free survival (EFS) were analyzed using the Kaplan-Meier method. Results: The median time between diagnosis and HSCT was 8.9 months. All the patients achieved engraftment but platelet recovery was significantly slower in group 1 (p=0.034). Acute and chronic graft-versus-host disease occurred in 54.3% and 34.3%, respectively. Five-year OS, RFS, and EFS rates of total patients were 66.8%, 50.8%, and 47.6%, with better OS was observed in group 1 by multivariable analysis (p=0.048). Disease status at HSCT was a significant predictor of OS (p=0.028), RFS (p=0.003), and EFS (p=0.004). Disease progression occurred in 13 out of 35 patients (37.1%); treatment-related mortality accounted for 63.6% of deaths (7 out of 11). Conclusion When performed at a younger age, allogeneic HSCT result in superior outcome in CML. Achieving remission before HSCT is critical for improved outcomes, highlighting the importance of pretransplant remission via optimal TKI strategies and minimal residual disease monitoring.

Background: Human muscle satellite (stem) cells (MuSCs) are essential for investigating muscle physiology, regeneration, and disease mechanisms. Primary cultures derived directly from human tissues offer a more physiologically relevant model than immortalized cell lines. However, the isolation and characterization of MuSCs from human upper limb tissues are limited. Therefore, this study aimed to establish and characterize a primary culture system for MuSCs obtained from human upper limb muscle tissue. Methods: Human muscle tissues were obtained from upper limb surgical specimens. Muscle samples were mechanically and enzymatically dissociated to isolate muscle-derived cells, which were cultured under standard growth conditions. Cell morphology and proliferation were monitored during the culture period. Myogenic characteristics were assessed by examining the expression of muscle-specific markers including myogenic regulatory factors and structural proteins. Additionally, myogenic differentiation capacity was evaluated by inducing differentiation and analyzing the formation of multinucleated myotubes. Results: Primary MuSCs were isolated from human upper limb tissues and expanded in vitro. The cultured cells exhibited a typical spindle-shaped morphology and demonstrated significant proliferative capacity. Characterization confirmed the expression of myogenic markers, indicating the presence of muscle-derived precursor cells. Following induction of differentiation, the cells formed multinucleated myotube-like structures and expressed muscle proteins associated with differentiation, highlighting their potential for myogenic differentiation. Conclusion This study established a reliable protocol for isolating and culturing MuSCs from human upper limb tissues. Cultured cells displayed typical myogenic characteristics and differentiation capacity, indicating that this model could be a valuable platform for studying human muscle biology and potential therapeutic applications.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.