撰文丨王聪 编辑丨王多鱼 排版丨水成文 霍维茨奖 （全称为 路易莎·格罗斯·霍维茨奖 ） ，是生物化学、生理学或生物物理学领域最具声望的科学 奖项之一，由美国 哥伦比亚大学 颁发，授予在基础生物学研究领域做出杰出贡献、对科学和人类健康具有 深远影响的科学家。 霍维茨奖是著名的" 诺奖风向标 "，据统计，此前 118 位霍维茨奖得主中，已有 55 位获得了诺贝尔奖。 例如，mRNA 疫苗先驱 Katalin Kariko 和 Drew Weissman 于 2021 年获霍维茨奖，于同年获诺贝尔生理 学或医学奖；于 2023 年获霍维茨奖，次年获诺贝尔化学奖。miRNA 研究先驱 Victor Ambros 和 Gary Ruvkun 于 2009 年获霍维茨奖，于 2024 年获诺贝尔生理学或医学奖。 近日，哥伦比亚大学公布 2025 年 霍维茨奖 获奖名单—— Kevin Campbell （爱荷华大学） 、 Louis Kunkel （波士顿儿童医院/哈佛医学院） 、 Eric Olson （德州大学西南医学中心） ，他们三人" 因揭示 了肌萎缩症的病因而获奖 "， 该奖项旨在表彰他们在揭示 杜氏肌营养不 ...

Core Viewpoint - The article discusses a significant advancement in artificial intelligence (AI) where the first AI-designed virus (bacteriophage) has been created, capable of infecting and killing antibiotic-resistant bacteria, showcasing the potential of AI in developing biotechnological tools and therapies for bacterial infections [3][11]. Group 1: AI and Virus Design - The research published on September 17, 2025, demonstrates the ability of AI to design a complete viral genome, marking a major step towards AI-generated life [3][6]. - The AI models, Evo 1 and Evo 2, were trained on extensive genomic data, enabling them to predict and generate DNA sequences at a genome scale [7][11]. - The team used the bacteriophage ΦX174 as a template for designing the viral genome, which consists of 5,386 nucleotides and encodes 11 genes, showcasing the complexity of the design process [8][9]. Group 2: Research Findings - The research team evaluated thousands of AI-generated sequences, narrowing them down to 302 viable candidates, with 16 capable of self-replication and specifically infecting E. coli strains [11][12]. - Among the functional genomes, each contained 67-392 new mutations compared to their closest natural counterparts, indicating the potential for creating new species through AI [12]. - The AI-designed phages demonstrated the ability to infect and kill three different E. coli strains, outperforming the wild-type ΦX174 phage [11][12]. Group 3: Safety and Future Applications - Concerns about the potential misuse of AI models for harmful virus design are addressed, emphasizing that the training data excluded viruses affecting eukaryotes, including humans [14]. - The research team aims to utilize this method for safely generating AI-designed viruses to tackle various diseases and public health issues, particularly antibiotic resistance [15].

编辑丨王多鱼 排版丨水成文 雷帕霉素靶蛋白复合物1 （mTORC1） 通过整合生长因子 （GF） 与营养信号，激活与细胞生长相关的合 成代谢过程，同时抑制自噬等分解代谢过程。通过结节性硬化复合体传导的生长因子信号，可调控溶酶体 定位的小 GTP 酶——脑内富集 RAS 同源物 （RHEB） 的活性。RHEB-GTP 与 mTORC1 中 mTOR 激酶 亚基的直接结合，通过诱导大规模构象变化，以变构方式激活该激酶活性。 2025 年 9 月 17 日，加州大学伯克利分校 James H. Hurley 团队 （ 崔志成 博士为第一作者） 在国际顶 尖学术期刊 Nature 上 发表了题为： Structural basis for mTORC1 activation on the lysosomal membrane 的研究论文。 该研究揭示了溶酶体膜上 mTORC1 激活的结构基础 ，从而解析了 mTORC1 激 活的完整结构机制。 4）mTOR 与膜结合实现完全酶活激活。 RHEB 作用与膜结合效应共同导致完整的 mTORC1 催化激活，这一发现从结构层面解释了生长因子和营 养信号在溶酶体上的整合机， 为 ...

声免疫疗法 （ Sono-immunotherapy ） 是治疗实体瘤的一种很有前景的方法。其主要作用机制是在 超声 （ Ultrasound ） 照射下产生 活性氧 （ROS） ，从 而在肿瘤细胞中诱导氧化应激，并导致抗原及损伤相关分子模的释放，从而激活癌症免疫循环。 声免疫疗法的有效性在很大程度上取决于声敏剂的声催化活性和免疫调节能力。因此，开发兼具高声催化活性和强大免疫调控特性的声敏剂，对于提高声免疫疗 法的癌症治疗疗效至关重要。 2025 年 9 月 17 日，苏州大学功能纳米与软物质研究院 程亮 教授团队在 Cell 子刊 Cell Biomaterials 上发表了题为： Metal-doping engineering-driven STING-PANoptosis cascade for enhanced cancer sono-immunotherapy 的研究论文。 该研究利用 金属掺杂工程 驱动的 STING- PANoptosis 级联反应，增强了癌症 声免疫疗法 （ sono-immunotherapy ） ， 不仅证明了金属掺杂工程在驱动泛凋 亡 （ PANoptosis ） 方面 ...

Core Viewpoint - CAR-T cell therapy has shown clinical success in hematological malignancies, but its efficacy in solid tumors remains significantly lower, highlighting the challenges faced by T cell therapies in this area [2][3]. Group 1: Challenges in T Cell Therapy for Solid Tumors - Three key obstacles for T cell therapy in solid tumors include: 1) antigen heterogeneity; 2) tumor microenvironment suppressing T cell function; 3) difficulty in cultivating sufficient non-exhausted T cells in vitro [3][5]. Group 2: Research Findings on ScTIL - A study published in Cell Reports Medicine introduced a "three-in-one" strategy to produce super circulating TIL-like cells (ScTIL) for the treatment of biliary tract cancer, demonstrating safety and significantly extending survival for late-stage patients [3][5][9]. - The ScTIL production involves: 1) isolating PD-1+ T cells from patient peripheral blood; 2) modifying these T cells with enhanced receptors to counteract tumor microenvironment suppression; 3) co-expressing amplification factors and anti-CD19 CAR for rapid in vivo expansion [5][8]. - In a clinical trial with 10 late-stage biliary tract cancer patients, ScTIL treatment resulted in a median overall survival (OS) of 18.3 months, surpassing the standard treatment OS of approximately 12 months [5][8]. Group 3: Implications for Future Treatments - The study indicates that ScTIL significantly improves survival rates for late-stage biliary tract cancer patients, suggesting it as a promising therapy for solid tumors and offering new hope for cell therapy in this domain [9].

Core Viewpoint - The article discusses the association between radiation exposure from medical imaging, particularly CT scans, and the increased risk of hematologic cancers in children and adolescents, emphasizing the need for safer and more judicious use of imaging technologies [3][10]. Group 1: Research Findings - A large cohort study published in NEJM indicates that approximately 10.1% of hematologic cancer cases in children and adolescents can be attributed to radiation from medical imaging, primarily from CT scans [3][10]. - The study tracked 3,724,623 children born between 1996 and 2016 across six healthcare systems in the U.S. and Ontario, Canada, with a follow-up period averaging 10.1 years [6]. - Among the 29,61 cases of hematologic cancer diagnosed, the risk of cancer increased with cumulative radiation exposure, particularly in children exposed to at least 30 mGy [6][10]. Group 2: Risk Assessment - The average radiation exposure for children with hematologic cancers was 24.5±36.4 mGy, with a significant correlation between bone marrow radiation dose and increased cancer risk [7]. - Relative risks (RR) for different radiation exposure levels were reported: RR of 1.41 for 1-5 mGy, RR of 1.82 for 15-20 mGy, and RR of 3.59 for 50-100 mGy [7]. - The study suggests that for every 100 mGy of excess radiation, the relative risk increases by 2.54, highlighting the importance of monitoring cumulative radiation exposure [7]. Group 3: Comparative Studies - A related study published in JAMA Internal Medicine estimated that CT scans could account for 5% of cancer cases annually, with infants being at the highest risk [11]. - The study noted that in 2023, approximately 93 million CT scans were performed in the U.S., potentially leading to around 100,000 cancer cases, comparable to risks associated with alcohol consumption and obesity [11]. - Another study in Nature Medicine found that each cumulative 100 mGy of radiation doubles the risk of hematologic malignancies, with a typical CT scan increasing the risk of lymphoid or myeloid malignancies by about 16% [13].

Core Insights - Global warming is exacerbating the occurrence of wildfires, which in turn affects the global carbon cycle, ecosystems, air quality, and human health [2] - A study by Tsinghua University predicts a significant increase in wildfire-related carbon emissions and premature deaths by the end of the century [3][6] Group 1: Wildfire Emissions and Health Impact - The research developed a machine learning framework to predict global wildfire emissions and premature deaths, indicating a 23% increase in carbon emissions from 2010-2014 to 2095-2099 under a moderate emissions scenario (SSP 2-4.5) [3][6] - By 2095-2099, annual premature deaths due to wildfire smoke could reach 1.4 million, nearly six times the current level, with Africa expected to see the highest increase in wildfire-related mortality, projected to rise by 11 times [6][10] Group 2: Long-range PM2.5 Pollution - A separate study quantified the global impact of long-range PM2.5 pollution from the 2023 Canadian wildfires, revealing an increase in global average PM2.5 exposure by 0.17 µg/m³ [10][11] - North America experienced the highest increase in PM2.5 exposure at 1.08 µg/m³, while Europe saw an increase of 0.41 µg/m³ due to long-distance transport [10] - The study estimated that 354 million people in North America and Europe were exposed to PM2.5 pollution from the Canadian wildfires, leading to approximately 5,400 acute premature deaths in North America and 64,300 chronic premature deaths across North America and Europe [10][11]

Core Insights - The article discusses the development of an AI model named Delphi-2M, which can predict the risk of over 1,000 diseases based on an individual's medical history and lifestyle factors, potentially forecasting health outcomes decades in advance [2][5]. Group 1: AI Model Development - Delphi-2M is a generative transformer-based AI model that can predict the likelihood of developing 1,258 diseases over a 20-year period by analyzing health records and lifestyle choices [2][5]. - The model was trained using long-term biomedical monitoring data from 400,000 participants in the UK Biobank, incorporating factors such as age, gender, BMI, and health-related habits like smoking and drinking [5][9]. Group 2: Predictive Accuracy - Delphi-2M's predictions are comparable to or exceed the accuracy of existing models that assess single disease risks, and it outperforms machine learning algorithms that use biomarkers for multi-disease risk prediction [7][9]. - The model was tested on a dataset of 1.9 million health records from the Danish National Patient Registry, showing reliable predictive capabilities even outside its training dataset [9]. Group 3: Limitations and Future Directions - While Delphi-2M represents a significant advancement in modeling multiple diseases, it has limitations, such as relying on initial disease occurrence data, which may not fully capture an individual's health trajectory [9]. - The research team plans to evaluate Delphi-2M's predictive accuracy using datasets from multiple countries to broaden its applicability [9].

Core Insights - The article discusses a significant study from Stanford University published in *Nature*, which reveals that decision-making in the brain is a distributed process rather than being solely controlled by the cortex, suggesting a more complex understanding of brain function and potential new targets for treating neurological diseases [2]. Group 1: Research Findings - The study created a comprehensive map of neural activity in mice during complex behaviors, indicating that brain decision-making involves various regions, including subcortical areas, which can show selection signals earlier than the cortex [2]. - This research challenges the traditional view of the brain as a centralized command center and opens new avenues for understanding and treating neurological disorders [2]. Group 2: Industry Engagement - In conjunction with World Alzheimer's Day, the article prompts reflection on the complexities of neurodegenerative diseases and the potential for better utilization of mouse and cell models in research [5]. - A survey is initiated to gather insights on research pain points and industry trends, offering participants a chance to receive a resource package on neuroscience research and win various prizes [5][7]. Group 3: Product Offerings - The company offers over 20 types of gene-edited and drug-induced mouse models for neurological and muscular diseases, including various knockout and transgenic models tailored to researchers' needs [9]. - Specific mouse models for diseases such as Alzheimer's, Parkinson's, and spinal muscular atrophy are detailed, showcasing the company's commitment to providing relevant research tools [10][11].

Core Insights - The research published by the team from USC Keck School of Medicine demonstrates the successful construction of human kidney organoids with complex three-dimensional structures, which replicate most physiological functions of the kidney and produce urine-like fluids after transplantation [3][4]. Group 1: Research Findings - The study introduces kidney progenitor assembloids (KPA) derived from human pluripotent stem cells (hPSC), which exhibit significant advancements in cellular complexity and maturity, successfully mimicking the self-assembly process of kidney progenitor cells observed in vivo [4][5]. - The KPA model allows for high-fidelity disease modeling, specifically creating a model for autosomal dominant polycystic kidney disease (ADPKD), which replicates cystic phenotypes and the molecular and cellular characteristics of the disease [5][7]. Group 2: Implications and Applications - This innovative platform for kidney organoids opens new avenues for high-fidelity disease modeling and lays a solid foundation for regenerative medicine in the field of nephrology, with significant implications for drug development and disease simulation [3][7].