Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine - a prospective cohort study by the AGMT

The current gold standard of response assessment in patients with myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia (CMML), and acute myeloid leukemia (AML) is morphologic complete remission (CR) and CR with incomplete count recovery (CRi), both of which require an invasive BM evaluation. Outside of clinical trials, BM evaluations are only performed in ~50% of patients during follow-up, pinpointing a clinical need for response endpoints that do not necessitate BM assessments. We define and validate a new response type termed "peripheral blood complete remission" (PB-CR) that can be determined from the differential blood count and clinical parameters without necessitating a BM assessment. We compared the predictive value of PB-CR with morphologic CR/CRi in 1441 non-selected, consecutive patients diagnosed with MDS (n = 522; 36.2%), CMML (n = 132; 9.2%), or AML (n = 787; 54.6%), included within the Austrian Myeloid Registry (aMYELOIDr; NCT04438889). Time-to-event analyses were adjusted for 17 covariates remaining in the final Cox proportional hazards (CPH) model. DeepSurv, a CPH neural network model, and permutation-based feature importance were used to validate results. 1441 patients were included. Adjusted median overall survival for patients achieving PB-CR was 22.8 months (95%CI 18.9-26.2) versus 10.4 months (95%CI 9.7-11.2) for those who did not; HR = 0.366 (95%CI 0.303-0.441; p < .0001). Among patients achieving CR, those additionally achieving PB-CR had a median adjusted OS of 32.6 months (95%CI 26.2-49.2) versus 21.7 months (95%CI 16.9-27.7; HR = 0.400 [95%CI 0.190-0.844; p = .0161]) for those who did not. Our deep neural network analysis-based findings from a large, prospective cohort study indicate that BM evaluations solely for the purpose of identifying CR/CRi can be omitted

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Susunan Ilmu Pengetahuan

vi; 121 Hlm.; 21 Cm

The Middle East In The World Economy 1800-1914

viii, 92 hlm.; ilus.; 30 c

Layanan perpustakaan keliling study kausus pada perpustakaan umum daerah kota Tanggerang Selatan

vii,68 hlm,; ilus 30 cm