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人群健康管理、基因组新生儿筛查与多组学交叉研究。

Population health management genomic new-born screens and multi-omics intercepts.

作者信息

Henry James Andrew

机构信息

Institute of Biomedical Sciences, London, United Kingdom.

出版信息

Front Artif Intell. 2025 Jul 29;7:1496942. doi: 10.3389/frai.2024.1496942. eCollection 2024.

Abstract

INTRODUCTION

The Population Health Management (PHM) Genomic Newborn Screens (GNBS) and Multi-Omics Intercepts for Human Phenotype Ontology (HPO) using Federated Data Platforms (FDP) represent a groundbreaking innovation in global health. This reform, supported by the UK's Genomic Medical Services (GMS) through "The Generation Study," aims to significantly reduce infant mortality by identifying and managing over 200 rare diseases from birth, paving the way for personalised health planning.

METHODS

Using an ecosystem approach, this study evaluates a diverse pangenome to predict health outcomes or confirm diagnoses prior to symptomatic manifestations. GNBS standardises care by integrating diagnostic techniques such as blood spot analysis and full blood cell diagnostics to stratify risk. The approach enhances the understanding of rare diseases in primary care medicine, with biomedical and haematology diagnoses re-evaluated. Scientific proof of concept and fit-for-purpose technology align multi-omics in pre-eXams (X = Gen AI).

RECOMMENDATIONS

The Digital Regulation Service (DRS) assembles an agile group of experts to enhance medical science through human phenotype ontology (HPO) for precise disease segmentation, scheduling accurate eXam intercepts where needed. This team strategically plans regulation services for digital HPO eXam assurance and implements Higher Expert Medical Science Safety (HEMSS) frameworks. The DRS is responsible for overseeing gene, oligonucleotide, and recombinant protein intercepts; commissioning blood pathology HPO eXam intercepts; and monitoring preliminary eXams with advanced imaging techniques.

DISCUSSION

In pursuit of excellence in PHM of HPO, HEMSS with Agile Group Development leverages the Genomic Newborn Screens (GNBS) and multi-omics to create personalised health plans integrated with NHS England Genomics and AI-driven DRS. The discourse extends to examining GNBS predictors and intercepts, focusing on their impact on public health and patient safety. Discussions encompass structured HPO knowledge addressing newborn health, ethical considerations, family privacy, and the benefits and limitations of pre-eXam screenings and life eXam intercepts. These debates involve stakeholders in adopting HPO-enhanced clinical pathways through Alliances for Health Systems Networking-Genomic Enterprise Partnerships (AHSN-GEP).

CONCLUSION

"The Generation Study" represents a paradigm in digital child health management using an HPO-X-Gen-AI framework, transitioning from trusted research to evidence-based discovery. This approach sets a standard for personalised healthcare practices, incorporating ontology risk stratification and future-ready analytics as outlined in the NHS Constitution. The discourse on higher expert medical science safety governance will continue in the forthcoming manuscript, "PHM Fit Lifecycles in Future Analytics," which will further explore developing localised health solutions for "Our Future Health."

摘要

引言

利用联邦数据平台(FDP)进行的人群健康管理(PHM)基因组新生儿筛查(GNBS)以及针对人类表型本体(HPO)的多组学拦截代表了全球健康领域的一项突破性创新。这项改革由英国基因组医疗服务(GMS)通过“世代研究”提供支持,旨在通过从出生起识别和管理200多种罕见疾病来显著降低婴儿死亡率,为个性化健康规划铺平道路。

方法

本研究采用生态系统方法,评估多样化的泛基因组,以在症状出现之前预测健康结果或确诊。GNBS通过整合血斑分析和全血细胞诊断等诊断技术来分层风险,从而规范医疗护理。该方法增强了对初级保健医学中罕见疾病的理解,同时对生物医学和血液学诊断进行了重新评估。概念验证的科学依据和适用技术使多组学在预检查(X = 生成式人工智能)中保持一致。

建议

数字监管服务(DRS)组建了一个敏捷的专家小组,通过人类表型本体(HPO)来加强医学科学,以实现精确的疾病细分,并在需要时安排准确的检查拦截。该团队战略性地规划数字HPO检查保证的监管服务,并实施更高专家医学科学安全(HEMSS)框架。DRS负责监督基因、寡核苷酸和重组蛋白的拦截;委托进行血液病理学HPO检查拦截;并使用先进成像技术监测初步检查。

讨论

为追求HPO人群健康管理的卓越性,具有敏捷小组开发的HEMSS利用基因组新生儿筛查(GNBS)和多组学来创建与英国国民保健服务体系(NHS)英格兰基因组学和人工智能驱动的DRS相结合的个性化健康计划。讨论范围扩展到检查GNBS预测因子和拦截,重点关注它们对公共卫生和患者安全的影响。讨论涵盖了针对新生儿健康的结构化HPO知识、伦理考量、家庭隐私以及预检查筛查和终身检查拦截的益处和局限性。这些辩论涉及通过卫生系统网络 - 基因组企业伙伴关系联盟(AHSN - GEP)采用HPO增强临床路径的利益相关者。

结论

“世代研究”代表了使用HPO - X - 生成式人工智能框架进行数字儿童健康管理的一种范式,从可信研究转向基于证据的发现。这种方法为个性化医疗实践设定了标准,纳入了本体风险分层和NHS宪法中概述的面向未来的分析。关于更高专家医学科学安全治理的讨论将在即将发表的手稿《未来分析中的PHM健康生命周期》中继续,该手稿将进一步探索为“我们未来的健康”开发本地化健康解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edd4/12354460/ddba7a6011fd/frai-07-1496942-g001.jpg

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