From Genome to Monitor: Integrating Multi-Omics into Patient-Specific Anesthesia Digital Twins

Journal Submission Metadata & Descriptions When uploading your manuscript to a journal submission portal (e.g., Editorial Manager, ScholarOne), you will be prompted to provide short descriptions, highlights, and a summary of how the paper fits the journal's scope. You can copy and paste the text below into those specific submission fields: 1. Short Description / Overview (For the Editorial Office) This review article introduces an innovative paradigm in precision medicine by outlining the integration of multi-omics data layers (genomics, proteomics, and metabolomics) into Artificial Intelligence (AI)-driven anesthesia Digital Twins (DTs). While current perioperative digital twins rely strictly on reactive, macro-physiological vital signs, this manuscript presents a conceptual and structural framework to transition anesthesia care into a proactive, cell-to-monitor parallel simulation. The paper establishes a clear four-layer technical architecture, details how molecular markers optimize closed-loop drug titration, and outlines the critical technical and ethical barriers that must be resolved to bring these computational phantoms to the bedside. 2. Research Highlights (Bullet Points) Evaluates the novel integration of genomics, proteomics, and metabolomics into perioperative AI digital twins. Bridges the gap between micro-cellular dynamics and macro-physiological clinical monitoring to eliminate population-averaged anesthesia guesswork. Establishes a robust, four-tier technical architecture consisting of Static, Dynamic, Cognitive Fusion, and Closed-Loop Control layers. Identifies critical translational bottlenecks, including point-of-care assay latency, data interoperability, and algorithmic explainability. 3. Relevance to Journal Scope (Why it should be published) This manuscript is highly relevant to journals focusing on anesthesiology, perioperative medicine, digital health, and artificial intelligence in healthcare. It addresses a critical data blindspot in modern intraoperative care—the omission of patient-specific molecular and metabolic variations. By offering a detailed architectural blueprint and addressing safety, ethics, and engineering gaps, this paper provides highly interdisciplinary insights that will drive collaborative cross-talk among anesthesiologists, data scientists, and medical device manufacturers. Part 2: Formatted Manuscript for Word File Copy everything below this line into a blank Microsoft Word document, format the headings to your preference, and save it as your main manuscript file. Title: From Genome to Monitor: Integrating Multi-Omics into Patient-Specific Anesthesia Digital Twins Running Title: Multi-Omics Anesthesia Digital Twins