The impact of end-stage kidney disease on mortality in patients after acute myocardial infarction: A nationwide study.

BACKGROUND: This study aimed to evaluate the impact of end-stage kidney disease (ESKD) on mortality in patients with first-time acute myocardial infarction (AMI). METHODS: This was a retrospective nationwide cohort study. Patients diagnosed with first-time AMI between January 1, 2000, and December 31, 2012, were included. All patients were followed-up until death or December 31, 2012, whichever occurred first. A one-to-one propensity score matching technique was used to match patients with ESKD to those without ESKD of similar sex, age, comorbidities, and coronary intervention (including percutaneous coronary intervention [PCI] and coronary artery bypass grafting [CABG]). Kaplan-Meier cumulative survival curves were constructed to compare AMI patients with and without ESKD. RESULTS: A total of 186 112 patients were enrolled and 8056 patients with ESKD were identified. Propensity score matched 8056 patients without ESKD were included in the comparison. Overall, the 12-year mortality rate was significantly higher in patients with ESKD than in those without ESKD (log-rank p < 0.0001), including the sex, age, and PCI and CABG subgroups. In Cox proportional-hazard regression analysis, ESKD was an independent risk factor for mortality after patients suffered from first-time AMI (hazard ratio, 1.77; 95% CI, 1.70-1.84; p < 0.0001). A forest plot for subgroup analysis revealed that in AMI patients, ESKD had a higher impact on mortality in male; younger age; without comorbidities such as hypertension, diabetes mellitus, peripheral vascular disease, heart failure, cerebrovascular accident, and chronic obstructive pulmonary disease; and receiving PCI and CABG subgroups. CONCLUSION: ESKD significantly increases the mortality risk in patients with first-time AMI, including both sexes, different ages, and whether PCI or CABG was performed. In patients with AMI, ESKD has a high impact on mortality in male, younger age, without comorbidities, and those undergoing PCI and CABG.

Genome-wide association study and identification of systemic comorbidities in development of age-related macular degeneration in a hospital-based cohort of Han Chinese.

Background: Age-related macular degeneration (AMD) is the main cause of severe vision loss in elderly populations of the developed world with limited therapeutic medications available. It is a multifactorial disease with a strong genetic susceptibility which exhibits the differential genetic landscapes among different ethnic groups. Methods: To investigate the Han Chinese-specific genetic variants for AMD development and progression, we have presented a genome-wide association study (GWAS) on 339 AMD cases and 3,390 controls of a Han Chinese population recruited from the Taiwan Precision Medicine Initiative (TPMI). Results: In this study, we have identified several single nucleotide polymorphisms (SNPs) significantly associated with AMD, including rs10490924, rs3750848, and rs3750846 in the ARMS2 gene, and rs3793917, rs11200638, and rs2284665 in the HTRA1 gene, in which rs10490924 was highly linked to the other variants based upon linkage disequilibrium analysis. Moreover, certain systemic comorbidities, including chronic respiratory diseases and cerebrovascular diseases, were also confirmed to be independently associated with AMD. Stratified analysis revealed that both non-exudative and exudative AMD were significantly correlated with these risk factors. We also found that homozygous alternate alleles of rs10490924 could lead to an increased risk of AMD incidence compared to homozygous references or heterozygous alleles in the cohorts of chronic respiratory disease, cerebrovascular disease, hypertension, and hyperlipidemia. Ultimately, we established the SNP models for AMD risk prediction and found that rs10490924 combined with the other AMD-associated SNPs identified from GWAS improved the prediction model performance. Conclusion: These results suggest that genetic variants combined with the comorbidities could effectively identify any potential individuals at a high risk of AMD, thus allowing for both early prevention and treatment.

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches.

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.