Core Viewpoint - The article emphasizes the significant role of metabolic regulation in mammalian embryogenesis, challenging the traditional view that metabolism merely supports developmental processes passively [2][4]. Group 1: Metabolic Regulation in Early Development - Metabolic needs during early embryonic development are critical, with pyruvate being essential for maintaining redox balance [4]. - The metabolic remodeling drives zygotic genome activation (ZGA), where mitochondrial TCA cycle enzymes temporarily localize to the nucleus in a pyruvate-dependent manner, closely linked to epigenetic reprogramming [4]. - Lactate accumulation in early embryos is crucial, and inhibiting lactate production or uptake can halt development at the two-cell stage [4]. Group 2: Metabolic Changes During Blastocyst Formation - As embryos progress from the two-cell stage to the blastocyst stage, significant metabolic activity changes occur, including increased mitochondrial activity and enhanced pyruvate oxidation [4]. - The dynamic changes in lipids during this stage are vital for establishing polarity in the outer cells of the morula [4]. Group 3: Metabolic Activity During Implantation - Lipids and their derivatives are essential for blastocyst implantation, with fatty acid-related inflammatory responses aiding in establishing the maternal-fetal interface and optimizing uterine receptivity [4]. Group 4: Metabolic Features in Gastrulation and Organogenesis - Metabolic gradients, such as spatially and temporally specific glucose metabolism waves, play a regulatory role in gastrulation [4]. Group 5: Metabolic Characteristics of Embryo Dormancy - Increased autophagic activity generates more nutrients, and higher levels of taurine and hypotaurine provide antioxidant protection during embryo dormancy [4]. Group 6: Advanced Methods for Assessing Embryonic Metabolism - The article discusses advanced methods for evaluating embryonic metabolism, including metabolomics for small embryos, spatial metabolomics, and Raman spectroscopy for assessing embryo quality in vitro [4].

Core Viewpoint - The emergence of DeepSeek-R1, an open-source large language model (LLM) developed by a Chinese startup, has revolutionized the deployment of AI in hospitals, significantly enhancing efficiency and reducing costs compared to existing models like ChatGPT [2][12]. Group 1: Deployment and Impact - DeepSeek-R1 was released in January 2025 and quickly became the most downloaded chatbot in the US Apple App Store, surpassing OpenAI's ChatGPT [2]. - As of May 8, 2025, DeepSeek-R1 has been deployed in over 755 hospitals across China, including top-tier hospitals and grassroots medical institutions, with more than 500 achieving local deployment [5][8]. - The model is capable of various tasks, including clinical services, hospital operations, and personal health management, providing significant support in diagnosis, treatment recommendations, and administrative tasks [13][21]. Group 2: Advantages of DeepSeek-R1 - The model's deployment cost is significantly lower than traditional AI systems, with a complete local deployment costing under $100,000, making it accessible for many smaller hospitals [21]. - DeepSeek-R1's advanced reasoning capabilities are comparable to top international models, essential for handling complex medical tasks [22]. - The open-source nature allows hospitals to customize and integrate the model into existing systems, enhancing its utility [22]. Group 3: Regulatory Challenges - The rapid deployment of DeepSeek-R1 has highlighted a regulatory "gray area," raising concerns about patient safety and the need for a robust regulatory framework [6][10]. - The lack of clear classification standards for AI applications in healthcare leads to ambiguity regarding which applications are considered high-risk [32]. - The current regulatory environment does not adequately address the unique challenges posed by large language models, necessitating immediate reforms [35]. Group 4: Recommendations for Regulation - The article calls for a risk-based classification system for AI applications in healthcare, distinguishing between high-risk and low-risk applications [35]. - High-risk applications should be regulated as medical devices, requiring stringent approval and monitoring processes [35]. - Continuous monitoring and evaluation of AI applications in real-world settings are essential to ensure safety and effectiveness [38].

Core Viewpoint - The article emphasizes the importance of selecting high-quality journals in the biomedical field for researchers, highlighting the need to avoid low-quality publications and focus on reputable ones to enhance research visibility and impact [1]. Group 1: Journal Highlights - The American Journal of Bioethics is a leading international journal in bioethics, focusing on ethical challenges in health sciences, with a 2024 impact factor of 20.8 and ranked 1st in multiple categories [3][4][5]. - The journal "Amyloid" is the official journal of the International Society of Amyloidosis, publishing research on amyloid proteins and related diseases, with a 2024 impact factor of 7.4 and high rankings in biochemistry and medicine [9][10][12]. - The Journal of Oral Microbiology explores oral microbiology and related health issues, with a 2024 impact factor of 5.5 and a strong ranking in microbiology [14][17]. - The journal "Artificial Cells, Nanomedicine, and Biotechnology" publishes interdisciplinary research on artificial cells and nanomedicine, with a 2024 impact factor of 4.5 and notable rankings in materials science and biotechnology [19][22][23].

Core Viewpoint - Cancer-associated fibroblasts (CAF) play a critical role in supporting tumor growth and metastasis through extracellular matrix remodeling, paracrine signaling, and immune suppression, which limits the efficacy of immune checkpoint blockade (ICB) therapies [2][3]. Group 1 - The study published by researchers from the University of Chicago in the journal Nature reveals that NNMT promotes the recruitment of myeloid-derived suppressor cells (MDSC) into tumors via CAF, leading to the formation of an immunosuppressive tumor microenvironment (TME) [4]. - The research demonstrates that NNMT is expressed in all CAF subtypes and induces H3K27me3 hypomethylation, which facilitates the secretion of complement proteins that recruit MDSC into the tumor [7]. - The team developed a potent and specific NNMT inhibitor (NNMTi) that reduced tumor burden and metastasis in various mouse tumor models by decreasing CAF-mediated MDSC recruitment and reactivating CD8+ T cell activation, thereby restoring the efficacy of ICB therapy [9]. Group 2 - Overall, the study indicates that NNMT is a key regulatory factor of immunosuppression in the tumor microenvironment and represents a promising new target for alleviating immune suppression and enhancing cancer immunotherapy effectiveness [11].

Core Viewpoint - The article discusses the development of an AI-driven virtual laboratory that enables multidisciplinary research teams to tackle complex scientific problems, specifically in the context of designing new nanobodies for SARS-CoV-2 [2][4][11]. Group 1: AI-Driven Virtual Laboratory - Researchers from Stanford University and the Chan Zuckerberg Biohub have created a virtual laboratory platform powered by AI agents, which can autonomously design and execute complex research strategies [2][4]. - The virtual lab allows human scientists to pose scientific questions while AI agents, including a "Chief Scientist Agent" and various "Specialist Agents," collaborate to advance research [5][6]. Group 2: Research Outcomes - Within days, the virtual laboratory successfully designed 92 novel nanobodies, with two showing the ability to bind to the spike protein of new SARS-CoV-2 variants during laboratory validation [9][11]. - The research demonstrates that AI agents can generate creative and rational solutions to scientific challenges, enhancing human scientists' capabilities rather than replacing them [11]. Group 3: Implications and Future Applications - This study marks the first instance of autonomous AI agents effectively solving a challenging scientific research problem from start to finish, showcasing a new paradigm where AI drives the entire research process [11]. - The virtual laboratory platform is designed for biomedical research but can be adapted for broader scientific fields, indicating its potential for widespread application [11].

Core Viewpoint - The article discusses the limitations of current cancer therapies based on secreted proteins and highlights a new research study that identifies potential immunotherapy targets through a cancer immunology data platform [2][3][12]. Group 1: Research Findings - The study developed a cancer immunology data platform called CIDE, which integrates 90 multi-omics datasets covering 8,575 tumor samples across 17 types of solid tumors [8][12]. - CIDE systematically identifies genes related to immunotherapy outcomes and has revealed secreted proteins such as AOAH, CR1L, COLQ, and ADAMTS7 as new immune checkpoint blockade (ICB) regulators [9][15]. - AOAH enhances immunotherapy through dual mechanisms: increasing T cell receptor (TCR) sensitivity to weak antigens and removing inhibitory lipids that suppress dendritic cell function [10][15]. Group 2: Implications for Cancer Treatment - The findings suggest that the identified secreted proteins could serve as precise targets for a new generation of immunotherapies, overcoming the limitations of traditional therapies like IL-2 and VEGF inhibitors, which have low response rates and unstable efficacy [17]. - AOAH's validation across multiple tumor models indicates its potential as a broad-spectrum immune enhancer [18]. Group 3: Future Directions - The research aims to expand the functional map of secreted proteins, particularly focusing on the 61% of previously unreported proteins related to cancer, to discover new molecules that regulate the immune microenvironment [20]. - There is a focus on targeting lipid-immune interactions based on AOAH-related lipid metabolism pathways for developing small molecule drugs or combination therapies [20]. - The study emphasizes the need for clinical translation of molecules like AOAH as biomarkers or therapeutic targets through preclinical and clinical trials [20].

Core Viewpoint - Latent Labs has developed a groundbreaking generative AI model, Latent-X, which enables the design of functional protein binders with atomic-level precision, significantly improving the drug discovery process [6][7][26]. Group 1: Company Background - Simon Kohl, a former researcher at DeepMind, founded Latent Labs after leaving the company in late 2022, focusing on advanced protein design models to aid biopharmaceutical companies [2]. - Latent Labs secured $50 million in funding in February 2025 to further its mission in drug development [2]. Group 2: Technology and Innovation - Latent-X can design functional protein binders, including macrocyclic peptides and small protein binders, with unprecedented efficiency and accuracy [6][7]. - The model generates reliable protein binders by solving geometric challenges at the atomic level, producing high-affinity and specificity binders [7][20]. - Latent-X demonstrated a significant improvement in efficiency, requiring only 30-100 candidates per target to achieve results that typically need millions of candidates [7][18]. Group 3: Performance Validation - The research team tested Latent-X on seven benchmark target proteins related to viral infections, tumor regulation, and neurodegeneration [11][12]. - Latent-X achieved a hit rate of 91%-100% for macrocyclic peptides and 10%-64% for small protein binders across the target proteins [18]. - The best-designed macrocyclic peptides reached micromolar affinity, while small protein binders achieved picomolar affinity, surpassing other design models [19]. Group 4: Features and Usability - Latent-X allows users to generate both protein sequences and structures simultaneously, outperforming previous methods that generated them sequentially [23]. - The platform is user-friendly, requiring no coding skills, and provides a complete workflow for laboratory validation [21][29]. - Latent-X is scalable and has successfully generated various therapeutic binders, with plans for further expansion [22]. Group 5: Competitive Advantage - Latent-X excels in generating binders for previously unseen targets, achieving higher simulation hit rates with fewer samples compared to other models [24][28]. - The model adheres to atomic-level biochemical rules, creating structures that are chemically viable and suitable for drug development [28].

Core Viewpoint - The article discusses the increasing global plastic consumption and its environmental impact, particularly focusing on microplastics and nanoplastics, which pose potential biological risks to human health [2][3][6]. Group 1: Plastic Consumption and Environmental Impact - In 2022, global plastic production was approximately 390 million tons, with only about 9% being recycled, leading to significant accumulation of microplastics and nanoplastics (MNP) [3]. - An individual may be exposed to between 74,000 and 121,000 MNP particles annually, with these particles detected in various human tissues, including kidneys, liver, lungs, and spleen [3]. Group 2: Research Findings on Reproductive Health - A study published by researchers from Zhejiang University found polyethylene (PE) and polyvinyl chloride (PVC) nanoplastics in human follicular fluid and seminal plasma, which are associated with decreased fertilization success rates and reduced sperm quality [4][5]. - The study utilized liquid phase extraction combined with pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) to analyze samples from 51 couples undergoing in vitro fertilization (IVF) [7]. - Average concentrations of PE and PVC in follicular fluid were 1.21 µg/g and 1.85 µg/g, respectively, while in seminal plasma, they were 3.02 µg/g and 2.67 µg/g [8]. - Higher concentrations of PE and PVC in follicular fluid were linked to significantly lower fertilization success rates, while PVC concentration in seminal plasma was associated with reduced sperm motility [10].

Core Viewpoint - The article emphasizes the importance of understanding common clinical symptoms in laboratory mice and rats to ensure reliable research data and efficient experimental progress. It introduces a guide that helps researchers quickly identify potential health issues in these animals, thereby reducing misdiagnosis and experimental delays [1]. Summary by Sections Clinical Symptoms Guide - The guide includes over 40 high-frequency symptoms with diagnostic illustrations for quick identification [1]. - It provides emergency handling procedures, such as wound disinfection and determining whether to euthanize an animal [1]. - The guide helps differentiate between environmental and pathological factors affecting the animals' health [1]. Observation Process - A complete animal observation process should encompass various aspects, including external and internal examination, dynamic and static observation, and individual versus overall assessment [6]. - The guide aids in accurately identifying health issues, from weight fluctuations to behavioral changes, with reference images and detailed descriptions [6]. Multi-Dimensional Analysis - The guide encourages a multi-faceted approach to identify the underlying causes of health issues by considering environmental factors, dietary conditions, and genetic backgrounds [7]. - It provides tailored recommendations for addressing health problems, considering the animals' health status, experimental needs, and environmental conditions [7]. Common Symptoms and Management - The article lists various symptoms, their common causes, and recommended handling measures, such as addressing issues like hair loss, weight changes, and abnormal behaviors [9]. - Specific examples include managing weight loss due to dietary insufficiencies or health conditions, and recognizing signs of distress or illness in the animals [9].

Core Viewpoint - Colorectal cancer (CRC) is the third leading cause of cancer-related deaths globally, with over 30% of patients diagnosed at an advanced stage. Neoadjuvant radiotherapy plays a crucial role in both curative and palliative treatments, yet only 15%-30% of rectal cancer patients achieve pathological complete response (pCR), highlighting the need to understand mechanisms behind radiotherapy failure [2][5]. Group 1 - A new study published in Cell Reports Medicine identifies a subtype of cancer-associated fibroblasts (CAFs) called ilCAF, which responds positively to radiotherapy. Activation of the IFN-γ/STING signaling pathway in ilCAF enhances the effectiveness of radiotherapy and overcomes resistance [3][6]. - The study utilized single-cell RNA sequencing to discover ilCAF characterized by high expression of interferon regulatory factor-1 (IRF1), which is enriched in tumors that respond well to radiotherapy [6][9]. - The activation of the IFN-γ/STING signaling pathway reprograms the tumor microenvironment, enhancing anti-tumor immunity by attracting T cells and dendritic cells through the secretion of chemokines CCL4 and CCL5 [6][12]. Group 2 - The increase in ilCAF numbers enhances endogenous immune responses, contributing to improved overall survival rates in rectal cancer patients. The combination of STING agonists with radiotherapy shows significant translational potential for cancer treatment [9][12]. - Early initiation of the combined strategy of STING agonists and radiotherapy may enhance initial anti-tumor responses and mitigate the emergence of treatment resistance [9].