MedComm - Future medicine最新文献

{"title":"ImmunoCheckDB: A Comprehensive Platform for Evaluating Cancer Immunotherapy Biomarkers Through Meta-Analyses and Multiomic Profiling","authors":"Chongxuan Lu,&nbsp;Mingxiao Li,&nbsp;Hong Yang,&nbsp;Zaoqu Liu,&nbsp;Jian Zhang,&nbsp;Quan Cheng,&nbsp;Anqi Lin,&nbsp;Shixiang Wang,&nbsp;Peng Luo","doi":"10.1002/mef2.70025","DOIUrl":"https://doi.org/10.1002/mef2.70025","url":null,"abstract":"<p>Immune checkpoint inhibitors (ICIs) have transformed cancer immunotherapy, but their clinical efficacy varies significantly due to tumor heterogeneity and patient-specific factors. Existing databases lack comprehensive integration of ICI efficacy data and fail to explore biomarkers across pan-cancer contexts, limiting their utility in precision oncology. To address this gap, we developed ImmunoCheckDB, a systematic platform that curates 173 studies on cancer ICI treatment, integrating survival outcomes for traditional and network meta-analyses with multiomic data sets from public repositories, including over 93,000+ individuals across 18 cancer types and 30 ICI regimens to provide a robust resource for pan-cancer biomarker discovery. Equipped with online tools for meta-analysis, network meta-analysis, and multiomic profiling, ImmunoCheckDB enables researchers to investigate correlations between ICI efficacy and molecular biomarkers, featuring key functionalities such as real-time visualization of forest plots, funnel plots, and network diagrams, as well as association analyses linking multiomic data to clinical outcomes. Uniquely combining meta-analytical with multiomic exploration, our platform offers insights into optimal patient populations for ICI therapy, thereby bridging the gap between clinical data and molecular research to empower researchers in advancing precision immunotherapy, with access available at https://smuonco.shinyapps.io/ImmunoCheckDB/ to democratize data-driven insights for personalized cancer treatment.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transsulfuration Reprogramming: A Metabolic Driver of BRAF-V600E Resistance in Melanoma","authors":"Juntong Chen,&nbsp;Guoqing Ding,&nbsp;Jie Zhang","doi":"10.1002/mef2.70024","DOIUrl":"https://doi.org/10.1002/mef2.70024","url":null,"abstract":"&lt;p&gt;Recently, a study by Péter Nagy's team [&lt;span&gt;1&lt;/span&gt;] published in &lt;i&gt;Cell Metabolism&lt;/i&gt; identified that the upregulation of cystathionine γ-lyase (CSE) and the inhibition of cystathionine β-synthase (CBS) are key factors contributing to the development of resistance to B-Raf proto-oncogene, serine/threonine kinase (BRAF) inhibitors (BRAFi) in treatment. Coadministration of the CSE inhibitor &lt;span&gt;d&lt;/span&gt;,&lt;span&gt;l&lt;/span&gt;-propargylglycine (PAG) with BRAFi significantly improved therapeutic efficacy and delayed the onset of resistance, offering a novel therapeutic strategy for patients with BRAF V600E-mutant (a valine-to-glutamic acid substitution at position 600 in the BRAF protein) melanoma.&lt;/p&gt;&lt;p&gt;Malignant melanoma is a highly aggressive tumor originating from melanocytes, with approximately 50% of patients harboring the BRAF V600E mutation [&lt;span&gt;1&lt;/span&gt;]. This mutation leads to the activation of the downstream mitogen-activated protein kinase kinase/extracellular-signal-regulated kinase (MEK/ERK) signaling pathway, which in turn results in an increase in aerobic glycolysis, thereby supporting the proliferation of melanoma cells [&lt;span&gt;2&lt;/span&gt;]. BRAF V600E inhibitors, such as vemurafenib (V) and dabrafenib (D), have been approved by the Food and Drug Administration (FDA) for the treatment of melanoma. However, resistance to these therapies often develops. Even when combining dabrafenib with the MEK inhibitor trametinib, resistance remains an inevitable challenge [&lt;span&gt;3&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Existing research has indicated that treatment with dabrafenib-trametinib (DT) inhibits the BRAF/MEK/ERK pathway, leading to a shift in melanoma cell metabolism from aerobic glycolysis to mitochondrial respiration, which is unfavorable for melanoma cell proliferation [&lt;span&gt;4&lt;/span&gt;]. Concurrently, the increased mitochondrial oxidative phosphorylation and electron transport chain (ETC) pathways result in enhanced reactive oxygen species (ROS) production. Excessive ROS production can disrupt the cellular redox balance and trigger oxidative stress responses. Building on this, the study discovered significant expression of cytochrome P (CYP)450 enzymes in dabrafenib- and trametinib-treated cells (DTC), with upregulation of CYP1B1 and CYP2F1 in dabrafenib- and trametinib-double resistant cells (DTR). These enzymes contribute to ROS production. To counteract the effects of ROS accumulation, antioxidant enzymes, such as superoxide dismutase 2 (SOD2), thioredoxin reductase 1 (TrxR1), catalase, 14-kDa human thioredoxin (Trx)-related protein (TRP14), glucose-6-phosphate dehydrogenase (G6PD), and glutathione (GSH) peroxidase 1 and 4 (GPX1, 4), are upregulated in DTC. However, this antioxidant response is limited, and the cells become more sensitive to exogenous oxidants, making them more susceptible to oxidative stress-induced damage. To support the function of antioxidant enzymes, DTC increasingly rely on the pentose phosphate pathway (PPP) to generate more nicoti","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MALT1: The Dual Domains Drive Resistance to Immune Checkpoint Inhibitors","authors":"Haoze Xie,&nbsp;Jie Zhang,&nbsp;Yicheng Chen","doi":"10.1002/mef2.70023","DOIUrl":"https://doi.org/10.1002/mef2.70023","url":null,"abstract":"&lt;p&gt;A recent research article published by Tao et al. [&lt;span&gt;1&lt;/span&gt;] in &lt;i&gt;Nature Cancer&lt;/i&gt; pointed out that two domains of Mucosa-Associated Lymphoid Tissue Lymphoma Translocation Protein 1 (MALT1) can promote tumor immune evasion, and that MALT1-targeting antisense oligonucleotides (ASOs) can effectively overcome resistance to immune checkpoint inhibitor (ICI), which are immunotherapy drugs that work by blocking inhibitory immune pathways like PD-1/PD-L1 or CTLA-4 to restore antitumor immunity. These results reveal an innovative method to overcome ICI resistance, offering fresh perspectives for developing cancer immunotherapies.&lt;/p&gt;&lt;p&gt;Cancer immunotherapy refers to therapeutic approaches that harness or enhance the body's immune system to recognize and eliminate tumor cells. With the discovery and characterization of tumor antigens, cancer immunotherapy has gained increasing attention, with research focus evolving from immune cells to tumor cells themselves and their immune microenvironment [&lt;span&gt;2&lt;/span&gt;]. Current mainstream immunotherapies, including ICIs, CAR-T cell therapy, and cancer vaccines, have demonstrated promising clinical potential in oncology. Specifically, ICIs block immune checkpoint molecules such as PD-1/PD-L1 and CTLA-4, thereby blocking the suppression of immune cells by tumors and restoring the killing power of immune cells against tumors. Despite their efficacy, ICIs suffer from high rates of primary nonresponse and secondary resistance [&lt;span&gt;3&lt;/span&gt;], restricting their widespread use. Studies have identified that the primary mechanisms underlying treatment failure and resistance involve both intrinsic tumor cell drug resistance pathways and the immunosuppressive properties of the tumor microenvironment (TME). Notably, tumor-associated macrophages (TAMs) have attracted much attention due to their unique plasticity and powerful immune regulatory function [&lt;span&gt;4&lt;/span&gt;]. By polarizing into M2-type macrophages, TAMs secrete immunosuppressive molecules that inhibit T-cell function, thereby facilitating tumor immune escape.&lt;/p&gt;&lt;p&gt;Originally identified through its chromosomal translocation in MALT lymphoma, MALT1 not only drives lymphomagenesis but also plays broad immunoregulatory roles. As the central component of the CARD11-BCL10-MALT1 (CBM) signaling complex [&lt;span&gt;5&lt;/span&gt;], MALT1 orchestrates T/B cell activation and signal transduction through its functional domains. Given its dual roles in promoting tumor immune evasion and lymphocyte dysfunction, MALT1 represents a promising target for overcoming immunotherapy resistance. Using CRISPR screening, Tao et al. created a focused library covering 810 genes in ten core oncogenic pathways. Through CD8&lt;sup&gt;+&lt;/sup&gt; T cell-mediated tumor killing experiments and mouse tumor cell line screening, they found that overexpression of MALT1 in tumor cells significantly enhanced their resistance to CD8&lt;sup&gt;+&lt;/sup&gt; T cell killing. MALT1 regulates the expression level of PD-L1 through ","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TabPFN: Shedding a New Light for Biomedicine With a Small Data Prediction Model","authors":"Menghan Li,&nbsp;Shuo Zhang,&nbsp;Cenglin Xu","doi":"10.1002/mef2.70022","DOIUrl":"https://doi.org/10.1002/mef2.70022","url":null,"abstract":"&lt;p&gt;In a recent study published in &lt;i&gt;Nature&lt;/i&gt;, the Transformer-based Tabular Prior-data Fitted Network (TabPFN) model was introduced. The important finding is that it outperforms traditional methods on small-to-medium data sets, mainly because of its in-context learning mechanism and synthetic data generation [&lt;span&gt;1&lt;/span&gt;]. This has significant translational implications for biomedicine and can efficiently analyze tabular data and make reliable predictions in resource-constrained scenarios.&lt;/p&gt;&lt;p&gt;The TabPFN model capitalizes on the in-context learning (ICL) mechanism, commencing with a methodology for generating diverse tabular datasets. And the target values of a subset of samples are masked to mimic supervised prediction scenarios. Then a transformer-based neural network (PFN) is trained to predict these masked targets, acquiring a generalized learning algorithm. TabPFN fundamentally differs from conventional supervised deep learning through three innovations. First, it employs cross-dataset training that exposes the model to diverse datasets, enabling universal pattern recognition beyond single-task limitations. Second, it performs whole-dataset inference by processing complete datasets simultaneously during prediction rather than individual samples. Third, its two-way attention mechanism operates bidirectionally: horizontally through intra-sample attention (analyzing feature interactions within each row) and vertically through inter-sample attention (identifying feature distribution patterns across columns). This architecture achieves inherent invariance to permutations in both sample and feature ordering while allowing efficient scaling to datasets exceeding the training size, effectively balancing model generalization with computational practicality. Additionally, it generates synthetic data using structural causal models (SCMs), sampling high-level parameters to fabricate a directed acyclic graph with a predefined causal structure, propagating random noise through root nodes, applying computational mappings (e.g., small neural networks, discretization, decision trees), and using post-processing techniques (e.g., Kumaraswamy distribution warping and quantization) to enhance realism and complexity. During inference, the model separates training and test samples. It performs ICL on the training set once, then reuses the learned state for multiple test set inferences, significantly enhancing inference speed. Memory optimization techniques (e.g., half-precision layer norms, flash attention, activation checkpointing, sequential state computation) reduce memory usage to under 1000 bytes per cell, enabling processing of data sets up to 50 million cells on a single H100 GPU. In performance, TabPFN surpasses traditional machine learning methods with three key advantages. Compared with CatBoost, XGBoost, and random forest, in the end-to-end process (training and inference), TabPFN is 5140 times faster than CatBoost (2.8 s vs. 4 h of hyperparamet","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical Role of Skin in Pathogenesis: Bidirectional Crosstalk Between Skin and Multiple Organs","authors":"Wende Deng,&nbsp;Junyi Liu,&nbsp;Changheng Tang,&nbsp;Zhenghao Li,&nbsp;Ying Qiu,&nbsp;Han Zhou,&nbsp;Lanxuan Yang,&nbsp;Ting Li","doi":"10.1002/mef2.70020","DOIUrl":"https://doi.org/10.1002/mef2.70020","url":null,"abstract":"<p>The skin, the largest organ in the human body, serves both as a mechanical barrier and an autonomous lymphoid organ, protecting against various environmental threats while maintaining the balance and functionality of multiple bodily systems. This relationship extends beyond the skin itself, involving other organs closely linked to skin homeostasis and related diseases. However, systematic reviews in this area are still lacking. This review seeks to explore this bidirectional communication, with a particular focus on the critical role of the immune system. We present a comprehensive review of the latest evidence, highlighting the fundamental roles of immune cells and cytokines within the skin–organ axis, particularly IL-17A, which appears to interact with nearly all organs, illustrating their interplay and impact on skin health. Additionally, we discuss the implications of these interactions for the design and application of skin-on-a-chip and organ-on-a-chip technologies, emphasizing the importance of understanding these relationships for developing physiologically relevant in vitro models. By providing a comprehensive analysis of these complex interactions, this review establishes a solid theoretical foundation for the prevention, diagnosis, and treatment of diseases associated with the skin–organ axis, particularly regarding immune cells, cytokines, microorganisms, and their metabolites, ultimately aiming to advance research in related fields and offer new insights for clinical applications.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models for Transforming Healthcare: A Perspective on DeepSeek-R1","authors":"Jinsong Zhou,&nbsp;Yuhan Cheng,&nbsp;Sixu He,&nbsp;Yingcong Chen,&nbsp;Hao Chen","doi":"10.1002/mef2.70021","DOIUrl":"https://doi.org/10.1002/mef2.70021","url":null,"abstract":"<p>DeepSeek-R1 is an open-source Large Language Model (LLM) with advanced reasoning capabilities. It has gained significant attention for its impressive advantages including low costs and visualized reasoning steps. Recent advancements in reasoning LLMs like ChatGPT-o1 have significantly exhibited their considerable reasoning potential, but the closed-source nature of existing models limits customization and transparency, presenting substantial barriers to their integration into healthcare systems. This gap motivates the exploration of DeepSeek-R1 in the medical field. Thus, we comprehensively review the transformative potential, applications, and challenges of DeepSeek-R1 in healthcare. Specifically, we investigate how DeepSeek-R1 can enhance clinical decision support, patient engagement, and medical education to help for clinic, outpatient and medical research. Furthermore, we critically evaluate challenges including modality limitations (text-only), hallucination risks, and ethical issues, particularly related to patient autonomy and safety-focused recommendations. By assessing DeepSeek-R1′s integration potential, this perspective highlights promising opportunities for advancing medical AI while emphasizing necessary improvements to maximize clinical reliability and ethical compliance. This paper provides valuable guidance for future research directions and elucidates practical application scenarios for DeepSeek-R1′s successful integration into healthcare settings.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Large Language Models and Their Association With Enhanced Impact in Biomedical Research and Beyond","authors":"Huzi Cheng,&nbsp;Wen Shi,&nbsp;Bin Sheng,&nbsp;Aaron Y. Lee,&nbsp;Josip Car,&nbsp;Varun Chaudhary,&nbsp;Atanas G. Atanasov,&nbsp;Nan Liu,&nbsp;Yue Qiu,&nbsp;Qingyu Chen,&nbsp;Tien Yin Wong,&nbsp;Yih-Chung Tham,&nbsp;Ying-Feng Zheng","doi":"10.1002/mef2.70019","DOIUrl":"https://doi.org/10.1002/mef2.70019","url":null,"abstract":"<p>The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Medical Education: A Practical Guide for Educators","authors":"Nivritti Gajanan Patil,&nbsp;Nga Lok Kou,&nbsp;Daniel T. Baptista-Hon,&nbsp;Olivia Monteiro","doi":"10.1002/mef2.70018","DOIUrl":"https://doi.org/10.1002/mef2.70018","url":null,"abstract":"<p>Artificial intelligence (AI)-driven learning is transforming education, requiring educators to quickly develop the skills to integrate AI tools effectively so they complement rather than replace traditional teaching practices. The fast pace of generative AI development poses challenges, particularly for less tech-savvy teachers or those who delay learning about these tools, leaving them at risk of falling behind. This is further compounded by students' quick adaptation to widely available models such as ChatGPT-3.5 and Deepseek R1, which they increasingly use for learning, assignments, and assessments. Despite existing discussions on AI in education, there is a lack of practical guidance on how medical educators can effectively and responsibly implement AI tools in teaching. This perspective provides a practical guide for medical educators to effectively incorporate AI tools to complement their teaching strategies, generate student assessments and to adapt assignments suitable for the AI era. We address challenges such as data bias, accuracy, and ethics, ensuring AI enhances rather than undermines medical training when aligned with sound pedagogical principles. This review provides a practical, structured approach for educators, offering clear recommendations to help bridge the gap between AI advancements and effective teaching methodologies in medical education.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lithocholic Acid Activates TULP3-Sirtuin-v-ATPase-AMPK Axis to Enhance Longevity","authors":"Yiran Wu,&nbsp;Zhengyu Gao,&nbsp;Long Zhang","doi":"10.1002/mef2.70017","DOIUrl":"https://doi.org/10.1002/mef2.70017","url":null,"abstract":"&lt;p&gt;A recent study published in Nature by Lin et al. [&lt;span&gt;1, 2&lt;/span&gt;], conducted at Xiamen University identified that lithocholic acid (LCA), a bile acid metabolite, mimics the anti-aging effects of caloric restriction (CR) by activating the TULP3-sirtuin-v-ATPase-AMPK axis. LCA binds to the receptor TUB-like protein 3 (TULP3), triggering allosteric activation of sirtuins (SIRT1-7), which deacetylate lysine residues (K52, K99, K191) on the V1E1 subunit of lysosomal v-ATPase. This deacetylation inhibits v-ATPase activity, activating AMPK through the lysosomal glucose-sensing pathway, fostering muscle rejuvenation in aged mice.&lt;/p&gt;&lt;p&gt;CR has long been associated with enhanced health and longevity in various species including yeasts, worms, flies, and mammals [&lt;span&gt;3&lt;/span&gt;]. Although the precise underlying mechanisms of these benefits remain unclear, they potentially involve alterations in multiple metabolic, hormonal, and cellular signaling pathways. AMPK is at the core of this process as a crucial regulator that senses energy levels within cells. Despite the well-documented advantages of CR, long-term adherence to such a dietary regimens is often impractical for individuals [&lt;span&gt;4&lt;/span&gt;]. Therefore, identifying pharmacological agents that could effectively mimic CR effects has emerged as a significant research area within the field of aging science. The presented study positions LCA as an effective analog for CR, offering a promising opportunity for therapeutic intervention.&lt;/p&gt;&lt;p&gt;Based on conventional wisdom, AMPK is primarily activated when energy stores are depleted and glucose levels are decreased [&lt;span&gt;5&lt;/span&gt;]. However, the authors discovered that in CR mice, blood glucose did not fall to the levels that would reportedly trigger AMPK activation. This observation indicated that AMPK activation during CR was not directly driven by reduced glucose levels. To further explore this phenomenon, the authors added serum from CR mice to cell cultures and observed sustained AMPK activation even under high glucose concentrations (Figure 1A). This result suggests that a factor in the serum of CR mice can activate AMPK glucose level-independently. The authors conducted a comprehensive metabolomic analysis of the serum from CR mice using various mass spectrometric techniques, and registered significant changes in the abundance of 695 metabolites during CR. Notably, only LCA could recapitulate AMPK activation at physiological concentrations (compared to the control serum) (Figure 1B,C). Furthermore, AMPK activation has been observed in mice as well as in other model organisms, such as nematodes and Drosophila, upon LCA supplementation.&lt;/p&gt;&lt;p&gt;The authors further investigated the signaling pathway by which LCA activates AMPK. They observed that AMPK activation via either LCA or CR did not lead to an increase in AMP or cytosolic calcium levels. This finding ruled out traditional AMP- or calcium-dependent activation mechanisms. The authors explored the ","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stem Cell-Derived Exosomes as Nanotherapeutics for Inflammatory Diseases","authors":"Xinyu Wei,&nbsp;Qingyi Wang,&nbsp;Wen Wen,&nbsp;Lingxiao Yang,&nbsp;Hao Chen,&nbsp;Gang Xu,&nbsp;Yongjie Zhou,&nbsp;Jiayin Yang,&nbsp;Zhenyu Duan","doi":"10.1002/mef2.70016","DOIUrl":"https://doi.org/10.1002/mef2.70016","url":null,"abstract":"<p>Inflammation, as a complex biological response, can lead to tissue damage and pathological physiological changes, forming the basis for many chronic diseases. Stem cell-derived exosomes (SC-Exos), a type of nanoscale extracellular vesicle, possess advantages such as small volume, low immunogenicity, and drug-carrying capacity, demonstrating immense potential in the field of disease diagnostics and therapeutics. Current studies indicate that SC-Exos can not only alleviate inflammatory diseases by suppressing inflammatory cytokines and modulating the activation of macrophages through their immunomodulatory and regenerative properties but also show significant potential as carriers for anti-inflammatory drugs, presenting a promising therapeutic approach for inflammatory conditions. However, the current lack of systematic summaries of SC-Exos in the treatment of inflammatory diseases has impeded the development of standardized therapies and clinical applications. This review elucidates the methods of SC-Exo sourcing, isolation, characterization, and engineering, as well as their application, mechanisms of action, and efficacy in the treatment of inflammatory diseases such as periodontitis, osteoarthritis (OA), and inflammatory bowel disease. Integrating these findings, this review highlights that SC-Exos can attenuate a variety of inflammatory diseases by transporting a diverse range of molecules to modulate immune responses, thereby providing foundations for subsequent standardization of production and clinical trials.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}