Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.
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http://dx.doi.org/10.1007/s43441-024-00627-4 | DOI Listing |
Electrophoresis
January 2025
Institute for Infectious Diseases, University of Bern, Bern, Switzerland.
Computer simulation was utilized to characterize the electrophoretic processes occurring during the enantioselective capillary electrophoresis-mass spectrometry (CE-MS) analysis of ketamine, norketamine, and hydroxynorketamine in a system with partial filling of the capillary with 19 mM (equals 5%) of highly sulfated γ-cyclodextrin (HS-γ-CD) and analyte detection on the cathodic side. Provided that the sample is applied without or with a small amount of the chiral selector, analytes become quickly focused and separated in the thereby formed HS-γ-CD gradient at the cathodic end of the sample compartment. This gradient broadens with time, remains stationary, and gradually reduces its span from the lower side due to diffusion such that analytes with high affinity to the anionic selector become released onto the other side of the focusing gradient where anionic migration and defocusing occur concomitantly.
View Article and Find Full Text PDFArch Pathol Lab Med
January 2025
the Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles (Petersen, Stuart, He, Ju, Ghezavati, Siddiqi, Wang).
Context.—: The co-occurrence of plasma cell neoplasm (PCN) and lymphoplasmacytic lymphoma (LPL) is rare, and their clonal relationship remains unclear.
Objective.
Brief Bioinform
November 2024
Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.
View Article and Find Full Text PDFBrief Bioinform
November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
View Article and Find Full Text PDFBrief Bioinform
November 2024
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs.
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