Core Viewpoint - The article discusses the safety and effectiveness of two types of influenza vaccines, "subunit" vaccines and "split virus" vaccines, based on a large clinical trial conducted in India, highlighting their comparable safety profiles for healthy adults and the elderly [2][19]. Group 1: Introduction and Background - Influenza poses a significant public health threat globally, particularly to high-risk groups such as children, the elderly, pregnant women, and those with chronic illnesses, making vaccination the most effective preventive measure [3]. - The market offers trivalent (TIV) and quadrivalent (QIV) vaccines, with QIV being preferred in many countries due to its broader protection against two B virus lineages [3]. - India faces unique challenges in influenza control, with a reported 28,798 cases of H1N1 and 1,218 deaths in 2019, indicating a potentially higher actual infection burden due to underreporting [5]. Group 2: Vaccine Types and Research Methodology - Subunit vaccines retain only immunogenic surface proteins, while split virus vaccines preserve the complete viral structure, leading to differences in protein content [6]. - The study was a randomized, observer-blind, active-controlled Phase III clinical trial conducted in March-April 2018 across 12 centers in India, involving 480 healthy participants aged 18-60 and elderly individuals aged 61 and above [8]. Group 3: Safety Comparison - Both vaccine types exhibited low adverse reaction rates, with 0.8% in the test group and 0.4% in the control group, and no serious adverse events or deaths reported [10]. - Local and systemic reactions were mild, with incidences below 5% in both groups, showing no significant differences [12]. - Local reactions included pain at the injection site (2.5% for subunit and 3.0% for split virus), while systemic reactions were most commonly fever (2.9% for subunit and 1.6% for split virus) [14]. Group 4: Discussion and Future Directions - The study confirms that both vaccine types are safe for healthy adults and the elderly in India, providing critical data for influenza prevention strategies [16]. - Limitations include the exclusion of immunocompromised or high-risk populations, a small sample size, and the absence of data on children and adolescents [17]. - Future research should focus on high-risk groups and consider regional differences in influenza prevalence to optimize vaccination strategies [18].

Core Viewpoint - The research highlights the development of an efficient method to differentiate human pluripotent stem cells (hPSCs) into A10 subtype midbrain dopaminergic neurons (A10 mDA), which can integrate into mouse brain circuits and improve depression-like behaviors [3][6][9]. Group 1: Research Background - A10 mDA neurons play a crucial role in reward-related and goal-directed behaviors and are considered target cells for treating various mental disorders, including depression [3][5]. - Previous studies have successfully induced differentiation of midbrain dopaminergic neurons (mDA) from hPSCs, but efficient differentiation of A10 mDA has been challenging [5][6]. Group 2: Research Methodology - The study reports a method for efficiently differentiating A10 mDA from hPSCs using Notch inhibitors, glial cell-derived neurotrophic factor (GDNF), and ascorbic acid (AA) [6][7]. - The differentiated A10 mDA neurons exhibited characteristics specific to the A10 subtype, including gene expression profiles and electrophysiological properties [6][7]. Group 3: Research Findings - Transplantation of A10 mDA into the nucleus accumbens (NAc) resulted in specific projections to endogenous target brain regions, inducing significant anti-anxiety phenotypes in normal mice and effectively alleviating depression-like phenotypes in depression model mice [6][7]. - The study emphasizes the potential of hPSC-derived neuron subtypes in treating neuropsychiatric disorders by functionally repairing damaged circuits [9].

撰文丨王聪 编辑丨王多鱼 排版丨水成文 血小板 的分子特征及其在成年哺乳动物体内的多种作用已得到充分描述，但对于其在早期发育阶段的情况，我们却知之甚少。 2025 年 8 月 11 日，中国医学科学院血液病医院 （中国医学科学院血液学研究所） 周家喜 、 刘翠翠 、 王洪涛 等人 在 Cell 子刊 Cell Reports Medicine 上发 表了题为： Enhanced regenerative and developmental potential of embryonal and stem cell-derived platelets compared to adult platelets 的研究论文。 该研究表明 ，相比于 成人血小板， 胚胎和干细胞来源的血小板具有更强的再生和发育潜能 ，这一发现 为针对特定临床需求开发定制化血小板疗法提供了新方 向。 在这项最新研究中，研究团队对小鼠胚胎血小板进行了转录组和蛋白质组分析，发现与成年血小板相比，胚胎血小板表现出经典的免疫调节和促凝血特征减弱， 但支持发育的特性增强。尤为重要的是，胚胎血小板与成纤维细胞等多种细胞类型的相互作用更活跃，可显著加速难愈 ...

撰文丨王聪 编辑丨王多鱼 排版丨水成文 线粒体 作为细胞内的"发电厂"，发挥着多种重要功能，它们每天需要"进口"上千种蛋白质，这些线粒体蛋白质的正确定位是线粒体发挥功能的基 础。 传统观点认为，这些线粒体蛋白质是在细胞质中完全组装好 （翻译后） 才被转运到线粒体的，而加州理工学院的一项最新研究显示， 约 20% 的线 粒体蛋白质是在翻译过程中就开始了向线粒体的转运 。这种"边生产边进口"的模式，相当于给线粒体蛋白开设了"VIP 通道"，让它们在翻译过程中就 提前插队。 2025 年 8 月 11 日，美国国家科学院院士、加州理工学院 单舒瓯 教授团队在国际顶尖学术期刊 Cell 上发表了题为： Principles of cotranslational mitochondrial protein import 的研究论文。 该研究为线粒体蛋白质向线粒体的 共翻译输入 提供了直接证据，明确了这一过程的时间和特异性，并解析了共翻译输入的原理。 研究团队发现，在人类细胞中近 20% 的线粒体蛋白可通过共翻译过程被输入线粒体。共翻译输入需要新生多肽链的 N 端前导序列，并能促进线粒体 表面的局部化翻译。该途径并不偏向 ...

Core Viewpoint - The article emphasizes the importance of NFAT (Nuclear Factor of Activated T-cells) reporter gene cell lines as essential tools for studying immune signaling pathways and drug screening, highlighting the challenges in developing high specificity and sensitivity systems for practical applications [4][5]. Group 1: NFAT Reporter Gene Cell Lines - NFAT reporter gene cell lines are crucial for real-time monitoring of pathway activity, reflecting the functional state of immune cells [4]. - The challenges faced in constructing effective NFAT reporter systems include insufficient signal sensitivity, background noise interference, expression heterogeneity, and limitations in application scenarios [5]. Group 2: Online Course Announcement - An online course titled "NFAT Reporter Gene Cell Lines: Decoding Immune Signals and Empowering Drug Development" is scheduled for August 14, featuring advanced scientific insights from a senior scientist at the company [6]. - The course will cover the core mechanisms of NFAT-mediated signaling pathways, innovative construction strategies for NFAT reporter gene cell lines, practical case studies from CAR-T optimization to antibody drug screening, and a Q&A session [6]. Group 3: Instructor Background - The instructor, Dr. Wu Qiang, is a senior scientist specializing in in vitro pharmacology with extensive experience in target peptide and antibody drug development, particularly in the fields of endocrine and autoimmune diseases [10].

撰文丨王聪 编辑丨王多鱼 排版丨水成文 不同尺度的生物结构无缝整合以执行基本功能。尽管已开发出多种组织学方法来揭示这些复杂的结构，但在保持大体积架构完整性的同时以高分辨率揭示微观结 构，仍是一项重大挑战。 2025 年 8 月 11 日，清华大学生物医学工程学院 苑克鑫 教授团队在国际顶尖学术期刊 Cell 上发表了题为： VIVIT: Resolving trans-scale volumetric biological architectures via ionic glassy tissue 的研究论文。 该研究首次提出并验证了一种全新的生物组织处理方法—— VIVIT ， 实现了组织在玻璃态下的高保真三维成像 。VIVIT 同时突破了组织透明化领域的三个技术瓶 颈：透明与无形变不能同时实现、荧光信号衰减、不兼容无损冷冻保存与切片，为包括脑科学在内的基础研究、病理分析、AI 辅助诊断等应用打开了新的空间。 在这项最新研究中，研究团队开发了 基于玻璃态离子液体溶剂的全尺度生物结构三维成像技术 （VIVIT） ，这是一种利用离子液体化学特性的三维组织学方法， 首次实现了将不透光的生物 组织 在低温下转变为 ...

Core Viewpoint - The study published in Nature Neuroscience reveals significant differences in the expression patterns of immature dentate granule cells (imGC) across various mammalian species, while highlighting the conserved biological processes involved in neurogenesis [3][4][7]. Group 1: Research Findings - The research systematically maps the cross-species molecular profiles of hippocampal neurogenesis, showing extensive differences in transcriptional expression among mammals, yet a high degree of conservation in biological processes [4][7]. - A specific subclass of ATPase enriched in human hippocampal imGC was identified, emphasizing its necessity in the development of human neurons [4][7]. - The study utilized machine learning-assisted analysis of published single-nucleus RNA sequencing datasets to identify imGC characteristics at the transcriptomic level in the hippocampus of macaques [7]. Group 2: Comparative Analysis - Cross-species comparisons (humans, macaques, pigs, and mice) revealed that, aside from a few shared genes (e.g., DPYSL5), imGC predominantly exhibits species-specific gene expression, converging on common biological pathways regulating neuronal development [7]. - The research highlights the importance of conducting independent molecular and functional analyses of adult neurogenesis across different species due to the observed gene expression heterogeneity and biological process convergence [7].

Core Viewpoint - The research highlights the structural differences between TTX-resistant (TTXr) and TTX-sensitive (TTXs) sodium channels, providing insights for the development of subtype-selective sodium channel-targeted drugs [4][5]. Group 1: Research Findings - The study published in PNAS by researchers from Shenzhen Medical Academy and Tsinghua University reveals that the differences in affinity for tetrodotoxin (TTX) and the β1 subunit between TTXr and TTXs sodium channels stem from several key structural elements [4][5]. - The research reported a 3.4 Å resolution structure of the wild-type human Na v 1.5 channel co-expressed with the β1 subunit after treatment with high concentrations of TTX, elucidating the molecular mechanisms behind the response differences to TTX and the β subunit [5][6]. - The study identified four critical sites on the extracellular loop (ECL) that may determine the differences in β1 subunit binding capabilities between Na v 1.5 and other sodium channels [6][7]. Group 2: Structural Insights - In TTXs sodium channels, the guanidinium group of TTX interacts with conserved cation-π interactions, which are lost in TTXr channels due to substitutions at corresponding sites, explaining the sensitivity differences [6][7]. - The absence of key cysteine residues in the ECL II domain of TTXr channels, which are necessary for forming disulfide bonds with β2 or β4 subunits, emphasizes the critical role of the extracellular loop in the mechanistic differences between TTXs and TTXr sodium channels [7].

Core Viewpoint - Baichuan Intelligent has launched the Baichuan-M2 model, which has surpassed OpenAI's models in medical capabilities and cost-effectiveness, establishing itself as a leading open-source medical AI model [2][4][12]. Group 1: Model Performance and Evaluation - Baichuan-M2 achieved a score of 60.1 on the HealthBench evaluation, outperforming OpenAI's latest model, GPT-OSS-120B, which scored 57.6 [4][11]. - The model has demonstrated superior performance in various benchmarks, including AIME24 (83.4), CFBench (77.6), and HealthBench (60.7) compared to its competitors [11]. - Baichuan-M2 is the second model globally to exceed a score of 32 on the HealthBench Hard evaluation, indicating its capability to handle complex medical questions [14][17]. Group 2: Cost and Deployment - Baichuan-M2 has been optimized for lightweight deployment, allowing it to run on a single RTX4090 card, reducing costs to nearly 1/60 compared to other models like DeepSeek-R1 [7][10]. - The model's design caters to the needs of medical institutions, enabling rapid deployment using existing hardware [7]. Group 3: Innovation in Training and Data Utilization - The development of Baichuan-M2 involved innovations such as the AI patient simulator and end-to-end reinforcement learning, which significantly enhanced its medical capabilities [19][22]. - The model utilizes a large verification system to ensure the accuracy and safety of its outputs, simulating real-world medical scenarios [19][20]. Group 4: Adaptation to Local Medical Practices - Baichuan-M2 has been specifically optimized to align with Chinese medical guidelines and practices, providing a tailored solution for local healthcare needs [24][26]. - The model's recommendations are based on local patient demographics and treatment protocols, distinguishing it from Western models [26][28]. Group 5: Real-World Application and Validation - Baichuan-M2 has shown exceptional performance in real clinical cases, accurately diagnosing conditions such as hypothyroidism and bronchial obstruction [32][33]. - The model's ability to integrate patient history and symptoms into its diagnostic process has been recognized by medical experts as comparable to that of high-level specialists [30][32].

Core Viewpoint - The article discusses the development of PromoterAI, an AI model by Illumina researchers, which accurately predicts expression-altering mutations in non-coding promoter regions, highlighting its significance in understanding genetic mutations and their impact on human health and rare diseases [3][4][6]. Group 1: PromoterAI Development and Functionality - PromoterAI is a deep learning model designed to predict the effects of promoter mutations on gene expression by evaluating genomic sequences in promoter regions [6]. - The model was trained at single nucleotide resolution to predict histone modifications, DNA accessibility, transcription factor binding, and gene expression around transcription start sites [6][9]. - The research team constructed a training dataset containing thousands of rare promoter mutations associated with abnormal gene expression across various tissues, controlling for confounding variables [6][9]. Group 2: Research Findings and Implications - The study found that predicted expression-altering promoter mutations were significantly enriched in clinically relevant genes of rare disease patients, contributing to 6% of the genetic burden associated with rare diseases [4][9]. - Analysis of population allele frequency spectra showed a significant depletion of predicted harmful mutations, indicating natural selection's role in removing deleterious mutations [7]. - PromoterAI's predictions were strongly correlated with protein abundance and quantitative trait measurements, enhancing the understanding of genetic contributions to rare diseases [7][9]. Group 3: Clinical Applications and Future Directions - The model was applied to undiagnosed rare disease patients in the Genomics England cohort, revealing a specific enrichment of predicted mutations in the promoter regions of Mendelian disease genes [7][9]. - PromoterAI fills a critical gap in genomic interpretation by accurately detecting promoter mutations that affect gene expression, which is often overlooked in current clinical genomic analyses focused on coding region mutations [9].