Liquid Biopsy in Non-Oncological Disorders: Emerging Paradigms and Translational Challenges
Description
This dataset compiles the supporting information and evidence base for the comprehensive review entitled "Liquid Biopsy in Non-Oncological Disorders: Emerging Paradigms and Translational Challenges". What data contains: The dataset includes curated literature analysis, synthesized summary tables of key liquid biopsy biomarkers (such as cell-free DNA, extracellular vesicles, and proteins), and their emerging clinical applications across various medical specialties. These specialties include Cardiology, Neurology, Autoimmunity, Infectious Diseases, and Perinatology. How data was gathered: The data were systematically gathered from published peer-reviewed literature. The process involved identifying relevant studies, extracting key findings on biomarker performance (e.g., correlation coefficients, predictive values), and organizing them into a structured format to facilitate the analysis presented in the review. Why this data is valuable: This structured dataset provides the empirical foundation for the review's conclusions regarding the transformative potential of liquid biopsy in non-cancer diseases. It enables transparency, reproducibility, and serves as a valuable resource for researchers and clinicians seeking to understand the current evidence landscape. It highlights the utility of liquid biopsy for early diagnosis, dynamic monitoring, and prognostic assessment in a wide spectrum of non-malignant conditions.
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Steps to reproduce
This dataset supports the comprehensive review titled "Liquid Biopsy in Non-Oncological Disorders: Emerging Paradigms and Translational Challenges". The data presented here include the curated literature analysis, summary tables of key biomarkers and their clinical utilities across various specialties (Cardiology, Neurology, Autoimmunity, Infectious Diseases, Perinatology), and synthesized findings on technological platforms (e.g., cfDNA fragmentomics, extracellular vesicle analysis, ultrasensitive protein detection). 1. To reproduce the analysis and conclusions of the review, refer to the main manuscript for the conceptual framework and interpretation. 2. The accompanying data files (e.g., summary_tables.xlsx, literature_metadata.csv) contain the structured evidence base. 3. Key findings, such as the correlation of myocardial-derived cfDNA with infarct size and the predictive value of phosphorylated tau in Alzheimer's disease, are derived from the analysis of the primary studies compiled within this dataset.