Papers

本文系统综述了由我国研究团队独立提出并发展的完整宇宙理论体系。该体系以“信息本体论”为哲学基石，以“引力维度主导”为物理主线，构建了自洽的“宇宙八大公设”刚性基础。在此基础上，衍生出描述宇宙结构的“24维弹性语法”、阐明底层运作机制的“τ-g₀基元耦合模型”与“时间-引力基元模型”，以及诠释意识本质的“意识锚点”理论。τ-g₀基元理论最终以其“公设-维度-基元-因果-系统”的五重闭环，构建了一个兼具现实可证伪性与虚拟可应用性的宇宙统一模型。本文旨在梳理各理论模块的核心定义、数学表达与内在逻辑衔接，阐明其从微观量子尺度到宏观宇宙结构、从物质几何到意识涌现的统一性与自洽性，展示该理论体系在基础物理、宇宙学及意识科学领域的原创贡献与应用潜力。

线粒体是真核细胞中负责能量代谢的关键细胞器，其自身携带的线粒体DNA（mtDNA）突变可导致多种严重的遗传性疾病。近年来，线粒体基因编辑技术的快速发展为治疗这类疾病提供了新的可能。本文综述了线粒体基因编辑技术的发展历程，从早期的锌指核酸酶（ZFN）和转录激活因子样效应物核酸酶（TALEN）到新一代的碱基编辑技术（DdCBE、TALED等），介绍了各类编辑工具的核心原理及其在线粒体中的适配策略。同时，本文总结了该技术在线粒体遗传病治疗、农业育种以及疾病模型构建等方面的应用进展，并对当前面临的脱靶效应、递送效率、伦理争议等挑战进行了分析，最后展望了未来的发展方向。

本文系统综述了单分子蛋白质测序的两大技术路线——电子隧穿识别和纳米孔传感，涵盖从氨基酸识别到全长蛋白质测序的关键突破，并展望了在精准医学和单细胞蛋白质组学中的应用前景。

本综述系统梳理了新兴单分子蛋白质测序技术的前沿进展，涵盖电子隧穿识别技术和纳米孔蛋白质测序技术两大主流路线，讨论了直接测序、标记辅助传感和天然蛋白质直接传感三种纳米孔核心策略，并分析了Quantum-Si等商业化平台的进展和临床转化前景，最后展望了技术挑战和未来发展方向。

CRISPR-Cas系统作为一种革命性的基因编辑工具，近年来在分子诊断领域展现出巨大的应用潜力。基于Cas12和Cas13蛋白的附带切割活性，研究者们开发了以SHERLOCK、DETECTR和HOLMES为代表的多种核酸检测平台，实现了对病原体核酸、肿瘤标志物及遗传变异的高灵敏度、高特异性快速检测。本文系统综述了CRISPR-Cas核酸检测技术的分子机制，梳理了主要诊断平台的技术迭代历程，总结了该技术在传染病诊断、肿瘤液体活检等临床场景中的应用进展，并对其在即时检测集成化、多重检测及监管审批等方面的未来发展方向与挑战进行了展望。

本文基于单一几何公理「时空标量曲率恒为零 $$R \equiv 0$$」，以无宇宙学常数的标准爱因斯坦场方程为基础框架，在均匀各向同性、尘埃近似的约束下，完成了无需引入暗物质、暗能量、暴胀场等特设概念的循环宇宙模型全程顺向推导。本文引入的几何能动张量 $$\Theta_{\mu\nu}$$ 是 $$R \equiv 0$$ 约束的必然推论，而非额外特设的物质场；核心几何-物质耦合系数 $$\kappa$$ 为模型公理体系的唯一代数解，无任何可调自由参数。通过Ia型超新星（Pantheon+样本）、BAO大尺度结构、Planck 2018 CMB角功率谱的联合验证，本模型在上述核心宇宙学观测特征上与现有数据完全吻合，与ΛCDM模型统计不可区分，且天然缓解哈勃张力，无需引入任何ΛCDM框架下的特设物理机制。 ---

核酸检测技术在传染病诊断、食品安全监控与环境微生物监测等领域发挥着不可替代的作用。传统核酸检测方法如聚合酶链式反应（PCR）虽灵敏度高，但对精密仪器和专业操作人员的依赖严重制约了其在基层与现场环境中的推广应用。近年来，基于CRISPR-Cas系统的新一代核酸检测技术迅速发展，其中Cas12和Cas13蛋白凭借其独特的反式切割活性，分别催生了DETECTR、HOLMES和SHERLOCK等代表性检测平台。这些平台将等温扩增技术与CRISPR靶向识别相结合，能够在恒温条件下于30至60分钟内完成从样品到结果的全流程检测，灵敏度可达阿摩尔级别。本文围绕CRISPR-Cas12/13系统的分子作用机制，系统综述了其在传染性疾病快速诊断、食品安全检测及信号读出技术集成等方面的最新研究进展，并就临床转化路径、多重检测平台构建和智能化融合趋势进行了前瞻性讨论。本次修订依据同行评审建议，新增了文献检索方法学描述、标准化性能比较框架、实际部署分析及面向开发者的可操作性指南，以期为该领域的后续研究与技术应用提供更全面的参考。

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the 'where' and 'how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs.

Recent advances in artificial intelligence (AI), high-performance computing, and systems biology have accelerated the development of AI-powered virtual biological systems, from virtual cells to multiscale organ models and programmable virtual humans. These systems promise transformative applications in drug discovery, precision medicine, and in silico clinical trials. This review provides a critical synthesis of current progress, key technologies, and future directions across this spectrum. We explore hybrid modeling strategies that combine mechanistic models—such as ordinary and partial differential equations—with deep learning methods including convolutional, recurrent, and graph neural networks. We emphasize the importance of robust uncertainty quantification, simulation validation, and multiscale integration across molecular, cellular, organ-level, and systemic processes. A core contribution is the introduction of the SIM-CARD framework, a standardized simulation accountability protocol to document data provenance, modeling assumptions, performance metrics, and regulatory alignment. We propose a three-phase translational roadmap: (1) validated AI-augmented virtual cells and organs (by 2030), (2) interoperable multi-organ physiological systems (by 2040), and (3) programmable full-body virtual humans supporting personalized simulations and regulatory use cases (by 2055). We identify key enablers—including high-fidelity multiscale data, computational scalability, and simulation governance—as well as bottlenecks such as algorithmic bias, explainability, and regulatory uncertainty. Finally, we call for collaborative efforts to establish minimal benchmarking suites, FAIR-compliant simulation metadata, and cross-institutional federated learning infrastructure. This review aims to guide the scientific, regulatory, and clinical communities in navigating the complex yet promising trajectory toward clinically actionable programmable human simulations.

The convergence of artificial intelligence and systems biology is giving rise to a new paradigm in biomedical research—AI-powered virtual biological systems. From single-cell simulations to organ-level models and ultimately programmable virtual humans, this digital continuum holds transformative potential for disease modeling, personalized medicine, and therapeutic discovery. In this review, we critically examine the state of the art in AI-driven simulations, including the numerical foundations, multiscale integration strategies, and the emerging class of hybrid models that bridge mechanistic and data-driven approaches. We explore the challenges of validation, uncertainty quantification, and regulatory alignment across simulation scales, with particular focus on the development of simulation accountability frameworks such as SIM-CARDs. Ethical and privacy concerns, including algorithmic bias and data sovereignty in patient-specific models, are also addressed, alongside concrete proposals for governance and federated simulation workflows. Special attention is given to the technical complexity of multiscale modeling, including the integration of mechanistic solvers with neural architectures and the computational resources required for real-time, clinically actionable simulations. We conclude with a translational roadmap for virtual biology that projects validated virtual cells for drug screening by 2030, multi-organ simulations by 2040, and the emergence of programmable virtual humans by 2055. By unifying high-fidelity numerical models with explainable AI, and aligning simulation design with ethical, regulatory, and clinical needs, the field of digital biology is positioned to unlock scalable and trustworthy biomedical innovation.