Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: To evaluate whether insulin resistance and impaired insulin secretion are useful predictors of incident diabetes in Koreans using nationwide population-representative data to enhance data privacy. Methods: This study analyzed the data of individuals without diabetes aged >40 years from the Korea National Health and Nutrition Examination Survey (KNHANES) 2007–2010 and 2015 and the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Owing to privacy concerns, these databases cannot be linked using direct identifiers. Therefore, we generated 10 synthetic datasets, followed by statistical matching with the NHIS-HEALS. Homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β-cell function (HOMA-β) were used as indicators of insulin resistance and insulin secretory function, respectively, and diabetes onset was captured in NHIS-HEALS. Results: A median of 4,580 (range, 4,463 to 4,761) adults were included in the analyses after statistical matching of 10 synthetic KNHANES and NHIS-HEALS datasets. During a mean follow-up duration of 5.8 years, a median of 4.7% (range, 4.3% to 5.0%) of the participants developed diabetes. Compared to the reference low–HOMA-IR/high–HOMA-β group, the high–HOMA-IR/low– HOMA-β group had the highest risk of diabetes, followed by high–HOMA-IR/high–HOMA-β group and low–HOMA-IR/low– HOMA-β group (median adjusted hazard ratio [ranges]: 3.36 [1.86 to 6.05], 1.81 [1.01 to 3.22], and 1.68 [0.93 to 3.04], respectively). Conclusion Insulin resistance and impaired insulin secretion are robust predictors of diabetes in the Korean population. A retrospective cohort constructed by combining cross-sectional synthetic and longitudinal claims-based cohort data through statistical matching may be a reliable resource for studying the natural history of diabetes.

Recent amendments to regulatory frameworks have placed a greater emphasis on the utilization of in vitro testing platforms for preclinical drug evaluations and toxicity assessments. This requires advanced tissue models capable of accurately replicating liver functions for drug efficacy and toxicity predictions. Liver organoids, derived from human cell sources, offer promise as a reliable platform for drug evaluation. However, there is a lack of standardized quality evaluation methods, which hinders their regulatory acceptance. This paper proposes comprehensive quality standards tailored for liver organoids, addressing cell source validation, organoid generation, and functional assessment. These guidelines aim to enhance reproducibility and accuracy in toxicity testing, thereby accelerating the adoption of organoids as a reliable alternative or complementary tool to animal testing in drug development. The quality standards include criteria for size, cellular composition, gene expression, and functional assays, thus ensuring a robust hepatotoxicity testing platform.

Background: Complications from osteoradionecrosis (ORN) and medication-related osteonecrosis of the jaw (MRONJ) include oro-cutaneous fistulas, necrotic bone exposure, soft-tissue defects, and pathologic fractures. The fibula free flap (FFF) is a common free flap method used to reconstruct the mandible in severe cases. Recently, we have used the FFF successfully for the reconstruction of ORN and MRONJ mandibular defects. We report this method as a recommended technique for the treatment of ORN and MRONJ and the management method of postoperative infections. Methods: Four patients who were diagnosed with ORN of the mandible and 3 patients who were diagnosed with MRONJ of the mandible were included in the study. Among the 7 patients, 3 patients also had pathologic fractures. Partial mandibulectomy and FFF reconstruction were performed at the Department of Oral and Maxillofacial Surgery, Samsung Medical Center from April 2019 to March 2021. Results: All 7 patients recovered following the reconstruction of the defect by FFF. Four patients experienced infections after surgery and pus cultures were performed. All were well healed without flap damage after changing the antibiotics by consultation with infectious medicine experts. Conclusion FFF is a widely used method and can provide an extensive flap to reconstruct the mandible, especially those affected by ORN or MRONJ. If an infection occurs after surgery, appropriate antibiotic changes should be made through cooperation with the infectious medicine department. Therefore, FFF is a well-established and recommended method even in cases of challenging reconstruction.

Objective: The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically. Materials and Methods: We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O’Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery. Results: Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00–2.22) and 1.68 (1.16–2.43), respectively, while sMT was not (HR of 1.40 [0.94–2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07–2.35), while sMT and cMT were not. Conclusion Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.

Objective: Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. Materials and Methods: Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. Results: All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged -2.1 to 0.3 cm2 for SMA, -12.0 to 2.6 cm2 for NAMA, and -2.2 to 9.9 cm2 for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). Conclusion SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.