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.

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.

Background: This study aimed to compare and evaluate the marginal fit of nanocomposite computer-aided design/computer-aided manufacturing (CAD/CAM) inlays. Three types of nanocomposite CAD/CAM blocks (HASEM, VITA Enamic, and Lava Ultimate) were used as materials. Methods: Class II disto-occlusal inlay restorations were prepared on a typodont mandibular right first molar using diamond rotary instruments. The inlays were fabricated using CAD/CAM technology and evaluated using the silicone replica technique to measure marginal gaps at five locations on each inlay. The data were analyzed by two-way analysis of variance and Tukey post hoc tests ( α=0.05). Results: There were no significant differences in the marginal gaps based on the type of nanocomposite CAD/CAM inlay used (p=0.209). However, there was a significant difference in the marginal gaps between the measurement regions. The gingival region consistently exhibited a larger marginal gap than the axial and occlusal regions (p<0.001). Conclusion Within the limitations of this in vitro study, the measurement location significantly influenced the marginal fit of class II disto-occlusal inlay restorations. However, there were no significant differences in the marginal gaps among the different types of CAD/CAM blocks. Furthermore, the overall mean marginal fits of the class II disto-occlusal inlay restorations made with the three types of nanocomposite CAD/CAM blocks were within the clinically acceptable range.

Despite long-term research achievements, the development of cell therapy (CT) products remains challenging. This is because the risks experienced by the subject and therapeutic effects in the clinical trial stage are unclear due to the various uncertainties of CT when administered to humans. Nevertheless, as autologous cell products for systemic administration have recently been approved for marketing, CT product development is accelerating, particularly in the field of unmet medical needs. The human experience of CT remains insufficient compared with other classes of pharmaceuticals, while there are countless products for clinical development. Therefore, for many sponsors, understanding the rationale of human application of an investigational product based on the consensus and improving the ability to apply it appropriately for CT are necessary. Thus, defining the level of evidence for safety and efficacy fundamentally required for initiating the clinical development and preparing it using a reliable method for CT. Furthermore, the expertise should be strengthened in the design of the first-in-human trial, such as the starting dose and dose-escalation plan, based on a sufficiently acceptable rationale. Cultivating development professionals with these skills will increase the opportunity for more candidates to enter the clinical development phase.

Plastic cannulae have attracted increasing interest as an alternative to traditional metal needles with the aim of reducing cannulation-related complications. We investigated whether the substitution volumes during hemodiafiltration differ using these two types of needles in dialysis patients. Methods: An intervention study involving 26 hemodialysis patients was conducted in Korea between March and September in 2021. Patients first received online hemodiafiltration using traditional metal needles, and thereafter plastic cannulae were used in a stepwise protocol. Repeated-measures design and linear mixed-effect models were used to compare substitution volumes between the two needle types with the same inner diameter. Results: The mean patient age was 62.7 years, and their mean dialysis vintage was 95.2 months. Most patients (92.3%) had an arteriovenous fistula as the vascular access. The substitution volume increased as blood flow and needle size increased for both plastic cannulae and metal needles. The substitution volume was significantly higher with 17-gauge (G) plastic cannulae than with 16-G metal needles at blood flow rates of 280, 300, and 330 mL/min. Similar results were obtained for 15-G metal needles and 16-G plastic cannulae at a blood flow rate of 330 mL/min. However, the patient ratings of pain on a visual analogue scale were higher for plastic cannulae. Conclusion: Higher substitution volumes were obtained at the same prescribed blood flow rate with plastic cannulae than with metal needles during online hemodiafiltration. Plastic cannulae are an option for achieving high-volume hemodiafiltration for patients with low blood flow rates.

Purpose: This study aims to explore a clear and evidence-based definition of adaptive behavior in stroke survivors and establish the antecedents, attributes, consequences, and empirical referents of the concept. Methods: The concept analysis was performed using the Walker and Avant method as a framework. Data from 90 publications were collected using various databases (PubMed, EMBASE, CINAHL, RISS, and KISS) and applied in the analysis. Results: Adaptive behavior in stroke survivors was defined according to four attributes: realizing change, taking an optimistic view, restructuring daily activities to suit oneself, and carrying out one's own daily life. The conceptual structure of their adaptive behavior comprised stroke onset, functional changes, and emotional liability as antecedents and autonomy, family equilibrium, and quality of life as consequences. Conclusions Clarifying the concept of adaptive behavior in stroke survivors provides an understanding of the underlying attributes of this concept. Furthermore, it will facilitate the development of scales to measure the concept and the application of a theory-based intervention program that can improve adaptive behavior.