Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories.

INTRODUCTION: We investigated the prevalence of amyloid beta (Aß) positivity (+) and cognitive trajectories in Koreans and non-Hispanic Whites (NHWs). METHODS: We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aß positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables. RESULTS: The prevalence of Aß+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aß+ Koreans showed a faster cognitive decline than Aß+ NHWs in the CU (B = -0.314, p = .004) and MCI stages (B = -0.385, p < .001). DISCUSSION: Ethnic characteristics of Aß biomarkers should be considered in research and clinical application of Aß-targeted therapies in diverse populations. HIGHLIGHTS: Koreans have a lower prevalence of Aß positivity compared to NHWs in the CU stage. The effects of Alzheimer's risk factors on Aß positivity differ between Koreans and NHWs. Aß-positive (Aß+) Koreans show faster cognitive decline than Aß+ NHWs in the CU and MCI stages.

Prediction of Amyloid Positivity in Patients with Subcortical Vascular Cognitive Impairment.

Background: Amyloid-ß (Aß) commonly coexists and impacts prognosis in subcortical vascular cognitive impairment (SVCI). Objective: This study aimed to examine the differences in clinical and neuroimaging variables between Aß-positive and Aß-negative SVCI and to propose a prediction model for Aß positivity in clinically diagnosed SVCI patients. Methods: A total of 130 patients with SVCI were included in model development, and a separate cohort of 70 SVCI patients was used in external validation. The variables for the prediction model were selected by comparing the characteristics of the Aß-negative and Aß-positive SVCI groups. The final model was determined using a stepwise method. The model performance was evaluated using the receiver operating characteristic (ROC) curve and a calibration curve. A nomogram was used for visualization. Results: Among 130 SVCI patients, 70 (53.8%) were Aß-positive. The Aß-positive SVCI group was characterized by older age, tendency to be in the dementia stage, a higher prevalence of APOEɛ4, a lower prevalence of lacune, and more severe medial temporal atrophy (MTA). The final prediction model, which excluded MTA grade following the stepwise method for variable selection, demonstrated good accuracy in distinguishing between Aß-positive and Aß-negative SVCI, with an area under the curve (AUC) of 0.80. The external validation demonstrated an AUC of 0.71. Conclusions: The findings suggest that older age, dementia stage, APOEɛ4 carrier, and absence of lacunes may be predictive of Aß positivity in clinically diagnosed SVCI patients.

Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study.

Purpose: Establishing a development environment for machine learning is difficult for medical researchers because learning to code is a major barrier. This study aimed to improve the accuracy of a postoperative vault value prediction model for implantable collamer lens (ICL) sizing using machine learning without coding experience. Methods: We used Orange data mining, a recently developed open-source, code-free machine learning tool. This study included eye-pair data from 294 patients from the B&VIIT Eye Center and 26 patients from Kim's Eye Hospital. The model was developed using OCULUS Pentacam data from the B&VIIT Eye Center and was internally evaluated through 10-fold cross-validation. External validation was performed using data from Kim's Eye Hospital. Results: The machine learning model was successfully trained using the data collected without coding. The random forest showed mean absolute errors of 124.8 µm and 152.4 µm for the internal 10-fold cross-validation and the external validation, respectively. For high vault prediction (>750 µm), the random forest showed areas under the curve of 0.725 and 0.760 for the internal and external validation datasets, respectively. The developed model performed better than the classic statistical regression models and the Google no-code platform. Conclusions: Applying a no-code machine learning tool to our ICL implantation datasets showed a more accurate prediction of the postoperative vault than the classic regression and Google no-code models. Translational Relevance: Because of significant bias in measurements and surgery between clinics, the no-code development of a customized machine learning nomogram will improve the accuracy of ICL implantation.

Association between cataract and fatty liver diseases from a nationwide cross-sectional study in South Korea.

This study examined the link between fatty liver disease (FLD) and cataracts, as previous research has suggested that FLD may contribute to metabolic syndrome, systemic inflammation, and potentially cataracts. We studied a nationwide cross-sectional cohort of the Fifth Korean National Health and Nutrition Examination Survey 2010-2011. FLD was defined as nonalcoholic FLD (NAFLD) and metabolic dysfunction-associated FLD (MAFLD). Multinomial logistic regression was utilized to investigate the relationship between cataracts and FLD after adjustment for potential confounders. Participants with cataracts had higher liver fibrosis scores, including the NAFLD fibrosis score (NFS; P < 0.001), fibrosis-4 index (FIB4; P < 0.001), and fatty liver index (FLI; P = 0.001). NAFLD was not associated with a higher odds ratio (OR) for cataracts in the fully adjusted model (OR = 1.23, P = 0.058). MAFLD was significantly associated with a higher OR (OR = 1.34, P = 0.006). After adjusting for all factors, the severity of FLD was linked to an increased risk of cataracts, with significant linear trends (P values for linear trends of NFS, FIB4, and FLI < 0.05). After adjusting for well-known cataract risk factors, MAFLD was significantly associated with cataracts. Our analysis suggests that FLD may serve as an independent risk factor for cataracts.

Characterization of thermostable bacteriophage CPD2 and its endolysin LysCPD2 as biocontrol agents against Clostridium perfringens.

Clostridium perfringens is one of the major foodborne pathogens in humans and animals. With the prevalence of antibiotic-resistant C. perfringens strains, bacteriophages and their endolysins have received considerable attention as promising alternatives to antibiotics. In this study, C. perfringens phage CPD2 was isolated from retail chicken samples. CPD2 belongs to the Podoviridae family and exhibits remarkable thermostability. While CPD2 has narrow host specificity, its endolysin LysCPD2 showed a broader lytic range, killing not only C. perfringens strains but other Gram-positive bacteria, such as B. cereus and B. subtilis. In addition, due to its exceptional thermal stability, LysCPD2 showed significant antibacterial ability against germinating C. perfringens spores during the heat activation process (75 °C for 20 min). Taken together, these results indicate that both thermostable phage CPD2 and its endolysin LysCPD2 can be used as efficient antimicrobial agents to control C. perfringens during thermal processing of foods.

Comparison of treatment effects between 4.9% N-acetyl-aspartyl glutamic acid and 0.05% cyclosporine A eye drops in dry eye patients.

PURPOSE: We aimed to investigate the difference in the treatment effects of 4.9% N-acetyl-aspartyl glutamic acid (NAAGA) and 0.05% cyclosporine A (CsA) eye drops in dry eye patients. METHODS: We retrospectively reviewed the medical records of 86 patients (86 eyes) who were diagnosed with dry eye and treated with NAAGA or CsA eye drops. Patients treated with NAAGA or CsA eye drops were designated as group A or B, respectively. We also calculated Ocular Surface Disease Index (OSDI), dry eye, and meibomian gland dysfunction (MGD) parameters before treatment and at 1 and 3 months after treatment. Eye drop discomfort was assessed by calculating visual analog scale (VAS) scores at 1 month and 3 months after treatment. RESULTS: There were no significant differences in patients' demographics and OSDI, dry eye, and MGD parameters between the two groups. OSDI, dry eye, and MGD parameters at 1 month and 3 months after treatment were found to be improved in both groups. However, at 1 month after treatment, the dry eye and MGD parameters of group A, except for corneal fluorescein staining, showed more improvement than those of group B. Additionally, at 3 months after treatment, the lid margin abnormality score, corneal staining score, tear break-up time, and OSDI of group A were significantly lower than those of group B. VAS scores of group A at 1 and 3 months after treatment were significantly lower than those of group B. CONCLUSION: Treatment with NAAGA eye drops was effective in dry eye patients and demonstrated faster treatment response and less discomfort during application than CsA eye drops.