新华社罗马1月3日电 意大利一项最新研究发现，迷走神经对维持心脏功能有重要作用，利用人工神经 导管修复实验动物被切断的右心迷走神经连接，可以避免心肌细胞过早老化、维持心脏的泵血性能。 研究人员说，切断迷走神经连接会使心脏迅速衰老，而仅部分修复这些连接就足以对抗心脏重塑的机 制。重塑是指在受损或遭受长期压力的情况下，心脏的结构和功能会相应发生变化，这样能在短期内维 持泵血能力，但长期会导致心脏功能恶化，甚至引发心力衰竭。 相关论文发表在美国《科学·转化医学》杂志上。（完） 研究人员通过手术切断雄性小型猪的右心迷走神经，然后植入人工神经导管，发现导管可促进相关神经 生长、改善其活动水平。与未经治疗的实验猪相比，接受导管治疗的实验猪右心迷走神经连接得到修 复，心脏机械性能保持较好，心肌在不同方向上的活动能力都得到改善。即使神经连接只恢复20%，也 能抑制氧化应激反应诱发的心肌细胞早衰。 迷走神经是一组混合性神经，从脑部延伸至人体多个器官，影响着循环、呼吸、消化等功能。意大利圣 安娜高等研究学院等机构的这项新研究发现，在导致右心迷走神经被切断的胸外科手术中，可以通过修 复迷走神经连接来预防心脏功能受损。 ...

肥胖世界ObesityWorld . 《肥胖世界》Obesity World - 同步传真肥胖及代谢国际新学术进展，为医学减重临床、教研人员搭建一座与国际接轨的桥梁，「每医健」旗下内容平台。 以下文章来源于肥胖世界ObesityWorld ，作者肥胖世界 一、脂肪组织重塑的全景图 研究构建了包含70名受试者、共171,247个细胞的脂肪组织单细胞图谱，包括25名极度肥胖者（手术前后）、24名健康体重者，并结合空间转 录组分析（每组4人），聚焦腹部皮下脂肪（与代谢异常息息相关）。肥胖人群中，免疫细胞（巨噬细胞、淋巴细胞）异常聚集，同时成熟脂 肪细胞数量减少（提示细胞凋亡或新生不足）；减重后这些病变现象得到明显缓解。 二、巨噬细胞的异常激活 肥胖时脂肪组织内巨噬细胞比例由14%剧增至31%，尤其是脂质相关巨噬细胞（LAMs）——其中既包含成熟型（MYE2），也有未成熟型 （MYE3），伴随大量脂质代谢及炎症激活标志物（如CD9、TREM2）表达上升，同时经典单核细胞（MYE5）也增多，提示有来自血液的招 募。肥胖巨噬细胞普遍呈现技巧性代谢激活——糖酵解、氧化磷酸化、胆固醇及脂肪酸合成/氧化全面增强。减重可使巨噬细 ...

编辑丨王多鱼 排版丨水成文 中国科学院杭州医学研究所 ：开创精准医疗的未来 中国科学院杭州医学研究所 （HIMCAS） 成立于 2019 年，是中国科学院下属的一家具有前瞻性的医学研 究机构。作为年轻且发展迅速的科技创新中心，HIMCAS 致力于通过将分子发现与临床解决方案相连接来 推动转化医学的突破。我们的使命是加快开发下一代疗法，以应对全球重大健康挑战。 HIMCAS 为处于职业生 涯各个 阶段的研究人员提供了一个充满活力且协作的环境，使他们能够在基础科 学、前沿技术和临床应用的交叉领域开展雄心勃勃的跨学科研究工作。 加入我们不断壮大的研究团队，我们面向全球招募杰出科学家，携手共塑精准医疗的未来。目前开放的职 位包括： 资深研究员 （ Senior PI） ， 申请人应目前在国际知名机构担任副教授及以上职位，并具有出色的领导能 力和有影响力的科研成果。 HIMCAS 提供极具竞争力的待遇，包括丰厚的启动资金以及全面的支持来组建 研究团队。成功入选者将有机会招募副研究员 （Co-PI） 、博士后研究员和研究助理，以建立并领导跨学 科项目。 特聘研究员 （Junior PI） ，鼓励在国际知名研究机构任职、已 ...

Core Insights - Artificial intelligence is becoming a key force driving transformation in the healthcare sector, focusing on precision in disease diagnosis, personalized treatment plans, efficient drug development, and intelligent health management [1][4]. Group 1: AI in Healthcare - AI is reconstructing the healthcare service paradigm by breaking down geographical barriers in medical resources, enhancing grassroots diagnosis, and ensuring precise health knowledge dissemination [4]. - Experts emphasize the importance of integrating AI in early identification and personalized intervention for neurological diseases, showcasing significant advancements in stroke prevention and treatment [4][5]. - The integration of AI with traditional Chinese medicine is highlighted as a means to innovate while preserving the essence of traditional practices [8]. Group 2: Medical Talent Development - High-quality discipline construction in public hospitals is essential for cultivating exceptional medical talent and improving healthcare service quality [6]. - A differentiated evaluation system is proposed to adapt to the "Healthy China" strategy, focusing on both innovative and clinical medical talents [7]. - Emphasis is placed on interdisciplinary collaboration and practical-oriented training to enhance the capabilities of medical professionals [7]. Group 3: Health Management Innovations - The "People's Good Doctor Family Health Care Service" project aims to create an intelligent, inclusive, and reliable health protection system for the public, utilizing AI technology [20]. - The launch of the "People's Good Doctor Corpus" aims to provide reliable information and verification services for healthcare professionals and the public [15]. - The establishment of the "People's Good Doctor·Zhong Nanshan Science Popularization Joint Laboratory" is intended to build a robust health science communication system [12]. Group 4: Future Directions - The conference highlighted the need for continuous optimization of medical talent training systems and the integration of digital technologies in healthcare [5][6]. - Discussions on the coexistence of medical humanities and AI suggest that AI can enhance the quality of medical services while promoting humanistic care [25].

Core Insights - A groundbreaking meta-analysis published in *The Lancet Diabetes & Endocrinology* reveals a strong dose-response relationship between weight loss and diabetes remission in overweight or obese patients with type 2 diabetes [6][9] Summary by Sections Weight Loss and Diabetes Remission - The study found that weight loss is crucial for controlling type 2 diabetes and reducing the risk of related complications, providing clear treatment goals and expected outcomes for clinicians and patients [9] - A systematic review of randomized controlled trials identified 22 high-quality studies, focusing on complete and partial remission metrics [7] Remission Rates Based on Weight Loss - Dramatic differences in complete remission rates were observed based on weight loss: only 0.7% of patients losing less than 10% of their body weight achieved complete remission, while nearly half (49.6%) of those losing 20%-29% and 79.1% of those losing over 30% achieved complete remission [7][8] - Partial remission rates also showed a clear ascending trend: 5.4% for less than 10% weight loss, 48.4% for 10%-19%, 69.3% for 20%-29%, and 89.5% for over 30% [7] Statistical Findings - Each 1% reduction in body weight increases the probability of complete remission by 2.17% and partial remission by 2.74% [8] - The study did not find significant associations between remission outcomes and factors such as age, gender, race, duration of diabetes, baseline body mass index, hemoglobin A1c levels, insulin use, or weight loss intervention methods [8]

Core Viewpoint - Obesity is a significant risk factor for cancer, being the second leading preventable cause after smoking, with over 13 types of cancer closely linked to obesity [6]. Group 1: Obesity and Cancer - Obesity accelerates the occurrence and progression of cancer, yet obese patients often show a "protective effect" during immunotherapy, responding better to treatments and having improved survival rates [7]. - A recent study from Vanderbilt University published in *Nature* reveals that inflammatory cytokines induced by obesity stimulate the expression of PD-1 on tumor-associated macrophages (TAM), weakening the immune surveillance against tumors while simultaneously enhancing the efficacy of anti-PD-1 immunotherapy [8][12]. Group 2: Research Findings - In experiments with mice, those on a high-fat diet (HFD) exhibited significant weight gain and metabolic abnormalities, leading to accelerated tumor growth when injected with cancer cells. However, only the HFD group showed notable anti-tumor effects when treated with anti-PD-1 antibodies [11]. - Analysis of immune cells from HFD mice indicated a decrease in specific CD8+ T cells and an increase in macrophages, with significant changes in TAM, including elevated PD-1 expression and altered metabolic states [14][16]. Group 3: Mechanisms and Implications - The study highlights that obesity-related inflammatory factors like INF-γ and TNF-α upregulate PD-1 expression in macrophages through signaling pathways, which in turn suppresses TAM functionality and reduces T cell activation [17]. - This mechanism suggests that anti-PD-1 inhibitors could effectively counteract the suppressive effects of obesity on TAM, thereby enhancing T cell anti-tumor activity in high BMI populations [18]. Group 4: Future Research Directions - Further investigation is needed to explore the roles of other innate immune cells and different dietary structures in the context of tumor immunity and the effects of obesity [18].

Group 1 - The core viewpoint emphasizes the importance of integrating artificial intelligence (AI) tools in the medical field to advance medical science [1] - The expert, Zhong Nanshan, encourages continuous learning of AI among medical professionals, sharing his own experience of struggling initially but persisting in learning [1] - There is a call for collaboration among professionals in virus research, including clinical, laboratory, and basic research personnel, to enhance the application of AI in medicine [1]

Core Viewpoint - The article emphasizes the importance of fat distribution over total fat amount in determining health risks, highlighting a new AI-based research approach that provides more accurate health risk assessments [5][7][10]. Research Highlights - Traditional health risk assessment tools like BMI are inadequate as they do not differentiate between fat and muscle or show fat distribution, leading to varying health risks among individuals with the same BMI [7]. - A study utilizing AI technology analyzed over 33,000 MRI scans from the UK Biobank, revealing that visceral fat around organs and intramuscular fat are closely linked to higher risks of diabetes and cardiovascular diseases, even when considering BMI and waist circumference [7][10]. - The study found that low muscle mass in men increases the risk of related health issues, a trend not observed in women, indicating potential gender differences in the impact of fat distribution and muscle quality on health risks [9]. Significance of the Research - The research demonstrates that AI can enhance the precision of health risk assessments by analyzing body scan images, identifying that fat around organs and within muscles poses greater health threats compared to fat in other areas [10]. - The findings suggest that widespread adoption of this AI technology could enable earlier identification of high-risk individuals and facilitate personalized prevention and management strategies for chronic diseases like diabetes and heart disease [10]. - This advancement not only offers a new tool for the medical community but also holds promise for improving public health management through more accurate risk assessments [10].

Core Insights - A new study from Turku University in Finland indicates a correlation between gut microbiome status during pregnancy and postpartum body fat content and weight changes [1][2] - Women with lower gut microbiome diversity during pregnancy have a higher probability of postpartum obesity [1] Group 1: Research Findings - The study involved follow-up data from over 250 pregnant women, revealing multiple associations between gut microbiome during pregnancy and postpartum overweight and obesity, particularly evident one to two years after childbirth [1] - Women who maintained normal weight within a year postpartum had higher levels of certain bacteria, such as the genus of Actinobacteria, while the presence of Ruminococcus and Clostridium was linked to weight status two years postpartum [1] - The gut microbiome "functional profile" inferred from metagenomic data showed predictive capabilities for body mass index and body fat percentage in women one year postpartum [1] Group 2: Broader Context - The global obesity rate continues to rise, affecting an increasing number of women of childbearing age, with estimates suggesting that nearly one in two pregnant women is overweight or obese [2] - Maternal obesity during pregnancy increases health risks for both mothers and fetuses during and after pregnancy [2] - The research findings have been published in the American academic journal "Microbiology (Open)" [2]

Core Insights - The article discusses a recent study published by South China Normal University, highlighting the relationship between environmental exposure and the abundance and transferability of antibiotic resistance genes (ARGs) in the respiratory tract [2][4]. Group 1: Study Findings - Exposure to environmental pollutants is linked to an increase in respiratory antibiotic resistance genes (ARGs) [5]. - The abundance and mobility of antibiotic resistance genes are negatively correlated with lung function [5]. - Enhanced mobility of antibiotic resistance genes is observed in early chronic obstructive pulmonary disease (COPD) [5]. - Environmental pollutant exposure is associated with increased antibiotic-resistant phenotypes in mouse lungs [5]. Group 2: Implications - The study elucidates a pathway through which environmental pollutants contribute to the increase of the respiratory resistance gene pool, indicating the need for action to mitigate the burden of antibiotic resistance by addressing environmental pollution [6].