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Summary A sequentially adaptive Bayesian design is presented for a clinical trial of cord-blood-derived natural killer cells to treat severe haematologic malignancies. Given six prognostic subgroups defined by disease type and severity, the goal is to optimize cell dose in each subgroup. The trial has five co-primary outcomes: the times to severe toxicity, cytokine release syndrome, disease progression or response and death. The design assumes a multivariate Weibull regression model, with marginals depending on dose, subgroup and patient frailties that induce association between the event times. Utilities of all possible combinations of the non-fatal outcomes over the first 100 days following cell infusion are elicited, with posterior mean utility used as a criterion to optimize the dose. For each subgroup, the design stops accrual to doses having an unacceptably high death rate and at the end of the trial selects the optimal safe dose. A simulation study is presented to validate the design's safety, ability to identify optimal doses and robustness, and to compare it with a simplified design that ignores patient heterogeneity.
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Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics
This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.
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- Award ID(s):
- 2215705
- PAR ID:
- 10535038
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 25
- Issue:
- 11
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 5941
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijms25115941
