Core Viewpoint - The article discusses the challenges and findings related to the ASO drug BIIB078, developed for treating C9orf72-associated ALS (c9ALS), highlighting its distribution in the central nervous system (CNS) and the lack of clinical benefits despite its presence [4][8]. Group 1: Drug Development and Mechanism - C9orf72-associated ALS is caused by the expansion of the G4C2 repeat sequence in the first intron of the C9orf72 gene, leading to toxic RNA transcripts and dipeptide repeat proteins (DPR) [7]. - BIIB078 is an ASO drug targeting an 18-base pair sequence in the C9orf72 gene, aiming to degrade the toxic G4C2 repeat transcripts [3]. - Preclinical studies indicated that BIIB078 could silence the G4C2 repeat transcripts and reduce the burden of toxic DPR proteins [3]. Group 2: Clinical Trial Outcomes - Clinical trials of BIIB078 in c9ALS patients failed to meet any secondary endpoints and did not demonstrate clinical benefits, leading to the termination of its development in March 2022 [3]. - Despite widespread distribution in the CNS, BIIB078 did not effectively reduce toxic proteins or pathological changes, nor did it improve clinical outcomes [8]. Group 3: Research Findings - A recent study published in Cell analyzed the effects of BIIB078 on c9ALS patients, revealing that while the drug achieved extensive CNS distribution, it did not significantly reduce toxic protein levels or alter disease pathology [4][5]. - The study found an increase in inflammatory biomarkers, indicating a persistent immune response, and an unexpected interaction with the RNase T2 enzyme [8][14]. - The research provides critical insights into the drug's distribution, efficacy, and inflammatory response, which could guide future clinical trial designs [5][13].

撰文丨王聪 编辑丨王多鱼 排版丨水成文 复杂且难以愈合的伤口，由于细菌密度高和感染风险大，需要精准干预。然而，传统治疗方法缺乏对伤口微环境的动态监测和系统调节。能否开发出一种精准的 治疗干预措施来降低伤口感染风险，加速伤口愈合？ 2025 年 9 月 1 日，浙江大学 俞梦飞 研究员、 贺永 教授等在 Cell 子刊 Cell Biomaterials 上发表了题为 ： AI-feedback bioelectronics promote infectious wound healing 的研究论文。 该将 再生生物电子学 与 人工智能 （AI） 相结合，其中 AI 充当修复信号的反馈，兼具智能响应和促进伤口愈合的双重功能。在感染早期，高电流 （4 毫安） 刺 激促使液态金属释放高剂量的镓离子，以实现快速广谱抗菌作用。在后期愈合阶段，生物电子学被设计为能够智能感知伤口状况，并在低电流 （0-2 毫安） 刺激 下精确控制镓离子的释放，从而在 14 天内促进组织再生。 该研究利用再生生物电子学实现了伤口修复的闭环控制，推动了伤口护理解决方案的发展，并通过 AI 的整合 开创了 智能愈合 的新领域 ， 为慢性伤口治 ...

Core Insights - The article discusses advancements in brain-computer interfaces (BCI), particularly focusing on non-invasive BCIs enhanced by artificial intelligence (AI) to improve user control and performance [2][4][11]. Group 1: BCI Types and Functionality - Brain-computer interfaces can be categorized into invasive and non-invasive types, with invasive BCIs providing more accurate readings by implanting electrodes in the brain, while non-invasive BCIs are simpler and carry lower risks [2]. - Non-invasive BCIs traditionally rely solely on decoded brain signals to control devices, but many human actions are goal-directed, which can be enhanced by AI interpreting user intent [3][4]. Group 2: AI Integration and Performance Improvement - A study published in Nature Machine Intelligence demonstrated that an AI-powered non-invasive BCI could significantly enhance the control capabilities of users, particularly benefiting paralyzed individuals [4][6]. - The AI-enhanced BCI allowed paralyzed users to perform tasks with nearly four times the effectiveness compared to using a standard BCI alone [4][10]. - The integration of AI as a co-pilot in the BCI system improved task completion speed and success rates, with paralyzed users achieving a 3.9 times improvement in cursor control tasks and a 93% success rate in moving objects with a robotic arm [10][11]. Group 3: Future Implications and Challenges - The AI-BCI system reduces the need for extensive decoding of brain activity, as AI can infer user intentions, making the system more practical and efficient for daily use [11]. - The challenge lies in balancing AI assistance without undermining user autonomy, as users prefer to maintain control over their actions rather than having AI dictate movements [11].

Core Insights - Prostate cancer is the second most common cancer among men globally, with a rapid annual increase in incidence in China at 13%, now ranking sixth among male malignancies [2] - The number of new prostate cancer cases in China is projected to reach 144,000 in 2024, 199,000 by 2030, and 250,000 by 2035 [2] Diagnosis Challenges - Diagnosis primarily relies on PSA blood tests, ultrasound, and digital rectal exams, with 1/3 of men over 50 showing suspicious nodules and nearly 10% having elevated PSA levels [3] - The PI-RADS scoring system for MRI has significant subjective and accuracy flaws, leading to potential misdiagnosis and unnecessary procedures [3] Need for Advanced Tools - There is an urgent need for an efficient, accurate, and non-invasive diagnostic tool to assist in the diagnosis and grading of clinically suspicious prostate cancer patients [4] - The emergence of AI technologies offers new possibilities for correlating imaging data with pathological results, paving the way for non-invasive diagnosis [4] AI Model Development - A multi-center study developed and validated an AI-based model, MRI-PTPCa, for efficient, accurate, and non-invasive diagnosis and grading of prostate cancer [5][11] - The model integrates advanced techniques such as self-supervised learning and transfer learning, significantly enhancing predictive performance [7] Model Performance - The MRI-PTPCa model demonstrated high consistency with pathological evaluations, outperforming clinical assessments and other predictive models, achieving an AUC of 0.983 for prostate cancer detection [9] - The model's predictive accuracy for grading was 89.1%, indicating its potential as a new non-invasive diagnostic tool [9] Interpretability and Validation - The study provided a comprehensive analysis correlating MRI-PTPCa scores with Gleason grading, highlighting the model's interpretability through visual heatmaps and quantitative features [10] - The model's features were significantly associated with various pathological characteristics, supporting the feasibility of linking imaging and pathology [10]

Core Viewpoint - The research highlights the immune-regulating properties of Zeaxanthin, a nutrient found in leafy vegetables and corn, which enhances CD8+ T cell function and improves anti-tumor immunity, indicating its potential as a dietary supplement in cancer therapy [3][10]. Group 1: Research Findings - Zeaxanthin is identified as an immune modulator that enhances the function of CD8+ effector T cells, thereby increasing anti-tumor immunity [3][10]. - Oral supplementation of Zeaxanthin improves the efficacy of anti-PD-1 immune checkpoint inhibitors and enhances the cytotoxicity of TCR-engineered CD8+ T cells against tumor cells [9][10]. - The study utilized a "blood nutrient" library to identify dietary nutrients that influence CD8+ T cell function, revealing that trans-18:1 fatty acid (TVA) also promotes CD8+ T cell activity and response to immunotherapy [6][8]. Group 2: Mechanisms of Action - Zeaxanthin enhances T cell receptor (TCR) signaling on CD8+ T cells, improving their functional response [9][10]. - The research indicates that the structural isomer Lutein does not exhibit the same immune-enhancing effects as Zeaxanthin, underscoring the unique properties of Zeaxanthin [8][10]. - The findings suggest that dietary components can play a significant role in modulating immune responses, particularly in the context of cancer treatment [5][12].

Core Viewpoint - Shanghai Bangyao Biotechnology Co., Ltd. has been recognized as a city-level enterprise technology center, highlighting its innovation capabilities and achievements in the field of gene and cell therapy [1][6][20]. Group 1: Recognition and Achievements - The recognition as a city-level enterprise technology center is a significant credential for Bangyao Biotechnology, following its previous accolades as a national intellectual property advantage enterprise and a Shanghai patent demonstration unit [1][2]. - This recognition reflects the company's leading position in the CGT (cell and gene therapy) sector, showcasing its organizational structure, management mechanisms, innovation efficiency, talent development, and technological achievements [6][20]. Group 2: Technological Platforms and Innovations - Bangyao Biotechnology has established five proprietary technology platforms: CRISTARS (gene editing technology innovation platform), ModiHSC (hematopoietic stem cell platform), Quikin CART (non-viral site-specific integration CAR-T platform), TyUCell (universal cell platform), and HyperTCell (enhanced T cell platform) [8][23]. - The company has generated over 100 patent achievements and has more than 10 innovative drug pipelines, with 5 projects having received IND (Investigational New Drug) approval in China, entering the registration clinical trial phase [8][23]. Group 3: Notable Products and Clinical Trials - The first product, BRL-101, is the world's first gene therapy for β0/β0 thalassemia using CRISPR/Cas9 technology, which has helped 15 patients become completely independent of blood transfusions [9][10]. - The non-viral site-specific integration CAR-T therapies, BRL-201 and BRL-203, have shown a 100% objective response rate in clinical trials for relapsed/refractory B-cell non-Hodgkin lymphoma [12][14]. - The allogeneic universal CAR-T therapies, BRL-301 and BRL-303, have been developed to address the risks of host rejection and graft-versus-host disease, significantly reducing production costs and patient wait times [15][18]. Group 4: Future Outlook - The recognition as a city-level enterprise technology center not only affirms Bangyao Biotechnology's past achievements but also sets expectations for its future development [20][21]. - The company aims to continue collaborating with global partners to enhance basic research and promote the clinical advancement of innovative products, contributing to the development of the CGT field in China [20][21].

Core Insights - The article discusses the discovery of a new gene, SCREP, which originated through a multi-step process and significantly inhibits the synthesis of the key aromatic compound, eugenol, in roses [3][5][6] - This research provides new perspectives on the mechanisms of gene origin in plants and opens new avenues for synthetic biology in designing new genes and improving biological traits [3][9] Gene Origin Mechanism - The study reveals that the SCREP gene originated from a non-coding DNA sequence approximately 63 million years ago, evolving into a complete protein-coding gene framework over time [5][6] - The insertion of a miniature inverted-repeat transposable element (MITE) into the promoter region of SCREP enhanced its expression level, explaining the differences in floral scent among various rose species [5][6] Functional Role of SCREP - The SCREP gene acts as a "scent switch" in the rose family, with its presence leading to a significant reduction in eugenol content in strawberries and petunias when transferred [6][9] - The absence of the SCREP gene or the lack of MITE insertion in certain rose varieties correlates with a stronger release of eugenol, indicating that SCREP expression levels are crucial in shaping the diversity of floral scents in the rose genus [6][9] Implications for Synthetic Biology - The findings offer theoretical foundations for the targeted regulation of floral scent traits in roses and have significant potential applications in synthetic biology [9][11] - The research suggests a shift from traditional methods of genetic modification to creating new genes from scratch, allowing for precise improvements in plant traits [9][11]

Core Viewpoint - The article discusses the development of a peptide-encoded organ-selective targeting (POST) method that enhances the delivery of mRNA to extrahepatic organs using lipid nanoparticles (LNP) [4][11]. Group 1: mRNA Delivery and LNP Technology - mRNA-based gene and protein replacement technologies present significant opportunities for vaccine, cancer treatment, and regenerative therapy development [2]. - LNPs have been widely adopted as delivery vehicles for mRNA COVID-19 vaccines, demonstrating their safety and efficacy [2]. - Achieving organ-selective delivery of LNPs containing mRNA remains challenging, particularly for extrahepatic organs [2][4]. Group 2: Advances in Organ-Selective Delivery - Recent studies have made progress in organ-selective delivery through simple binary charge modulation and lipid chemical modifications, but these strategies are limited by the rational design of the LNP-environment interface [2][4]. - The POST method utilizes specific amino acid sequences to engineer the surface of LNPs, allowing for efficient mRNA delivery to extrahepatic organs after systemic administration [4][7]. Group 3: Mechanism and Applications - The targeting mechanism of the POST system is based on the optimization of the mechanical affinity between peptide sequences and plasma proteins, forming a specific protein corona around the LNPs [4][9]. - The POST code does not rely on the charge of LNPs for organ selectivity, but rather on the unique protein corona formed, which is influenced by the amino acid sequence [9]. - The POST code is applicable to various LNP formulations and can facilitate the selective delivery of mRNA to organs such as the placenta, bone marrow, adipose tissue, and testes [9][11]. Group 4: AI and Computational Design - The research team developed an AI-based framework using a Transformer-based protein language model to generate peptide sequences with high mechanical affinity for specific proteins, demonstrating the potential of computational design in guiding LNP organ targeting [9][11]. - The peptide sequence RRRYRR was shown to enable selective delivery of mRNA to the lungs, supporting the feasibility of using computer-aided rational design for POST-LNP organ-selective delivery [9][11].

Core Viewpoint - The integration of artificial intelligence (AI) with biomaterials and biofabrication is revolutionizing the simulation of tumor extracellular matrix (ECM), enhancing physiological relevance and establishing patient-specific drug testing platforms [30]. Group 1: AI in Tumor ECM Modeling - AI methods are incorporated into three key stages of tumor ECM modeling: material formulation, optimization of biofabrication processes, and post-manufacturing analysis [4]. - AI enables the rational development of bioinks with tunable mechanical, chemical, and biological properties, improving printing accuracy and consistency [4]. - AI-enhanced in vitro tumor modeling aids in the rational design and real-time optimization of engineered tumor models, providing powerful tools for drug discovery and cancer mechanism research [4]. Group 2: Limitations of Current Methods - Existing in vitro models struggle to replicate the biochemical complexity and dynamic physical properties of the ECM, limiting their effectiveness [2][7]. - Advances in biomaterials and biofabrication technologies have allowed for the simulation of certain ECM features, but challenges remain in capturing the inherent complexity and dynamic behavior of ECM [7]. Group 3: AI's Role in Biofabrication - AI improves precision and adaptability in the three stages of ECM modeling: pre-process, in-process, and post-process [7]. - In the pre-process stage, AI facilitates the design of biomaterials through predictive modeling and exploration of initial design options [7]. - During the in-process stage, AI enables real-time monitoring and optimization of biofabrication methods, ensuring accurate replication of tumor ECM structures and properties [7]. - In the post-process stage, AI assists in high-throughput analysis of ECM datasets, linking biophysical properties to tumor behavior [7]. Group 4: Future Directions - Establishing standardized datasets, improving model interpretability, and incorporating clinical validation are crucial for bridging the gap between AI-driven ECM modeling and real-world translational impact [4]. - The framework developed for tumor ECM modeling can be extended to other diseases involving ECM dysfunction, such as fibrosis, neurodegenerative diseases, and inflammatory bowel disease [4].

Core Viewpoint - The article discusses the development of a biodegradable biohydrogel battery that addresses the limitations of traditional batteries in biomedical applications, emphasizing the need for high-performance energy sources that are compatible with biological systems [4][7]. Group 1: Biohydrogel Battery Development - Researchers from Zhejiang University have designed a biodegradable biohydrogel battery using light polymerization and 3D printing techniques, showcasing excellent mechanical properties and biocompatibility [4]. - The biohydrogel battery operates at a voltage of 1.5 V, providing a current range of 0.001-6 mA, which supports tissue regeneration and cardiac pacing applications [4][8]. Group 2: Hydrogel Characteristics and Applications - Hydrogels, as three-dimensional cross-linked polymer networks, exhibit properties similar to biological tissues, making them suitable for various biomedical applications such as drug delivery and tissue engineering [6]. - The integration of gallium-based liquid metals with hydrogels enhances their conductivity and mechanical performance, promoting their use in flexible bioelectronic devices [6]. Group 3: Challenges and Solutions in Energy Systems - Traditional batteries face significant limitations in biomedical applications due to poor biocompatibility, non-degradability, and rigidity, which can harm surrounding tissues [7]. - The development of a flexible, biodegradable power source using conductive ion hydrogels and InGa3-Cu nanoparticles addresses these challenges, maintaining stable current during degradation [7]. Group 4: Performance Metrics - The biohydrogel battery features a high printing precision of 50 micrometers, with tensile strain and compression rates of 200% and 95%, respectively, aligning with the mechanical properties of biological tissues [8]. - It operates in dual current modes, facilitating microcurrent for tissue regeneration and high current for effective cardiac pacing [8].