Yonsei University

A new deep learning model-based tool developed in South Korea can be used to both diagnose coronary artery disease and predict major adverse cardiac events in emergency cases. HOW IT WORKS Developed by a team of researchers from Yonsei University Severance Hospital, Keimyung University Dongsan Hospital, and medical imaging AI company Phantomics, the AI tool automatically assesses coronary CT angiography (CCTA) scans and classifies stenosis as normal, non-occlusive, or occlusive. The model also utilises the YOLO architecture, which simultaneously locates and classifies objects, to quickly process images. FINDINGS In a study, the AI model was tested using CCTA data from 408 patients who presented with acute chest pain at three emergency departments from 2018 to 2022. Findings, which were published in the Radiology: Artificial Intelligence journal of the Radiology Society of North America, noted that the deep learning-driven analysis of the degree of stenosis was a better predictor of major adverse cardiac events (MACEs) than common clinical risk factors such as hyperlipidemia or the cardiac enzyme level troponin-T. Moreover, pairing the AI-driven analysis with common risk factors improved the MACE prediction by 14% points to 90%. WHY IT MATTERS CT angiography, which is used to assess artery stenosis for CAD prognosis, usually takes a long time to process results, with analyses varying depending on the reader, according to Severance Hospital. The AI tool developed by the Korean research team not only detects CAD but also predicts MACE risks in patients who present to the emergency department. "This study suggests the possibility that deep learning models can be applied to predict patient prognosis beyond simply determining the presence or absence of CAD in emergency rooms, where rapid diagnosis and treatment decisions are important," said Dr Jin Hur, professor at Severance Hospital's Department of Radiology. "The AI technology can be applied beyond simple diagnostic assistance to become a clinical decision support tool," he added. MARKET SNAPSHOT Latest research projects across Asia-Pacific have also utilised AI to improve CAD diagnosis. Singaporean startup Health BETA is developing a solution that considers genetic and lifestyle factors in providing an enhanced polygenic risk score for CAD. Meanwhile, three major heart hospitals in Singapore – the National Heart Centre Singapore, the National University Hospital and Tan Tock Seng Hospital – are set to pilot a new machine learning-driven system for rapid CAD prediction. In Australia, publicly listed medical device companies Echo IQ and Artrya have recently obtained 510(k) clearance from the United States Food and Drug Administration for their respective AI-powered software for diagnosing CAD. Echo IQ's product is specifically indicated for detecting severe aortic stenosis, while Artrya's AI-powered software, Salix, delivers a 10-minute point-of-care assessment of CCTA scans.

编者按：根据全球疾病负担估计，全球有超过680万人受到炎症性肠病（Inflammatory Bowel Disease, 简称IBD）困扰，症状包括反复发作的腹痛、腹泻、直肠出血、疲劳和体重下降等，长期炎症还可引发纤维化等并发症。由于消化道黏膜反复损伤与修复，IBD甚至可能发展为肠癌。由于发病机制复杂，IBD至今没有治愈手段。这类疾病往往会反复发作，给患者带来长期的痛苦，因此也有“绿色癌症”之称。但对于再复杂的疾病，科学界都不缺攻克难关的勇气与坚持。自从上世纪40年代IBD疗法首次出现突破以来，我们已经能够通过多种糖皮质激素、免疫抑制剂以及近些年的新型小分子药物缓解IBD症状；与此同时，对新型疗法的追求也在继续。在一项近期研究中，药明康德DEL平台也助力研究者发现具有IBD治疗潜力的新型BET抑制剂。时值世界炎症性肠病日之际，本文将分享IBD疗法的研发历程与前沿进展。作为一类自身免疫相关的慢性胃肠道疾病，IBD表现为溃疡性结肠炎和克罗恩病两个类型。其中，前者主要影响结肠，炎症发生在浅表部位；后者则可累及消化道的任何部位，炎症也可抵达更深层的浆膜层等区域。目前，IBD的发病机制尚未明确，可能与遗传因素、环境因素、免疫反应异常和肠道微生物群失调等有关，不健康的高脂饮食、环境压力及抗生素滥用等都可能是导致触发或加重肠道炎症反应的因素。图片来源：123RF其中，免疫细胞分泌的多种细胞因子和生长因子能够推动肠道纤维化的进展。例如，转化生长因子β（TGF-β）是肠道和其他器官纤维化的主要调节因子，可以通过不同途径诱导肌成纤维细胞的聚集，促进肠道纤维化；而血小板衍生生长因子（PDGF）主要通过刺激肌成纤维细胞增殖来促进纤维化。此外，肿瘤坏死因子α（TNF-α）、多种白细胞介素（例如IL-1β、IL-17、IL-33和IL-36）和干扰素γ（IFN-γ）也深度参与了IBD的发病机制，促进炎症反应，导致肠道纤维化。IBD疗法的近百年探索IBD复杂的病理生理学机制，加上容易复发的特点，向临床治疗提出了巨大挑战。最近几十年里，随着研究者对炎症性肠病分子机制的理解不断加深，IBD的治疗方法有了长足发展。目前，已获批的临床治疗药物主要包括了传统治疗药物、生物制剂和新型小分子药物。IBD治疗最早的突破，要追溯到20世纪40年代。首次合成了柳氮磺吡啶的瑞典女性科学家Nanna Svartz教授观察到，当类风湿性关节炎患者在接受这种药物治疗时，溃疡性结肠炎症状竟然也得到了缓解。此后，柳氮磺吡啶的分解产物5-氨基水杨酸被确定为主要活性成分，这个带着些阴差阳错的发现也成为了IBD疗法研究的首个突破。自20世纪50年代起，糖皮质激素、免疫抑制剂等传统药物开始为IBD的治疗带来进展。1955年，一项临床试验显示，糖皮质激素能够显著改善IBD患者的疗效并降低死亡率。如今科学家们知道，这类药物通过激活受体抑制NF-κB等促炎转录因子，快速控制IBD的急性发作。不过，糖皮质激素的长期使用有引发不良事件的风险。同样在20世纪50年代，经典抗肿瘤药甲氨蝶呤被应用于克罗恩病治疗，拉开了免疫抑制剂治疗IBD的序幕。此后，多款免疫抑制剂也陆续实现了“老药新用”，用于维持缓解或降低免疫原性。不过，这类药物往往起效缓慢，效果也不及后续登场的生物制剂和新型小分子。图片来源：123RF进入20世纪80年代末，靶向促炎细胞因子——肿瘤坏死因子（TNF）的单克隆抗体首次获批用于治疗克罗恩病，开启了靶向生物制剂的治疗新时代，也改写了IBD的治疗格局。目前，用于IBD治疗的大分子生物制剂主要包含TNF抑制剂、整合素抑制剂以及白细胞介素（IL）抑制剂。例如，整合素抑制剂可以限制整合素（例如α4β7）与相应的内皮细胞粘附分子结合，阻断淋巴细胞向肠道黏膜的定向迁移。最近十年，新型小分子药物不断涌现，为IBD治疗提供了新的选择。其中，Janus激酶（JAK）抑制剂和鞘氨醇-1-磷酸（S1P）受体调节剂是发展较为迅速的两类小分子药物。JAK家族包括JAK1、JAK2、JAK3和TYK2，是与细胞因子受体胞内结构域连接的酶。当促炎细胞因子与其受体结合时，就会激活这些酶，引发免疫细胞内的促炎信号级联反应。而JAK抑制剂可以破坏细胞因子依赖的促炎途径，目前已有泛JAK抑制剂和选择性JAK1抑制剂获批，用于溃疡性结肠炎或克罗恩病的治疗。S1P受体调节剂是另一类得到应用的小分子IBD疗法。炎症期间，淋巴管中生成大量的S1P。在S1P梯度的驱动下，淋巴细胞离开淋巴结，移动至炎症部位，持续引发炎症反应。而S1P受体调节剂能够内化免疫细胞中的受体，阻止淋巴细胞离开淋巴结。目前，已获批的S1P受体调节剂正是通过这一机制切断淋巴细胞前往炎症部位的道路，有效缓解溃疡性结肠炎。蓬勃发展的新兴疗法基于上述原理，多款大分子生物制剂和新型小分子候选新药正在临床试验阶段。与此同时，近期研究揭示了多个新靶点与通路在IBD治疗中的潜力，一系列新型疗法正在研发之中。例如，TNF样配体1A（TL1A）是肿瘤坏死因子家族的一员，已有研究发现TL1A在包括IBD在内的自身免疫性疾病中异常表达。TL1A抑制剂不仅促进淋巴细胞分化为辅助性T细胞17 （TH17），起到抗炎作用，还兼具抗纤维化的作用。目前，已有一款TL1A抗体疗法正在接受溃疡性结肠炎和克罗恩病的III期临床评估。此外，NLRX1激动剂和GATA3拮抗剂同样通过阻止淋巴细胞向促炎亚群的分化起到治疗作用，相关疗法正在开发之中。另一类思路则是靶向免疫细胞内的促炎信号通路，包括受体相互作用的丝氨酸/苏氨酸蛋白激酶1（AKT1）、磷酸二酯酶4（PDE4）和SIK2/3抑制剂。不久前，另一项研究揭示了BET蛋白抑制剂在IBD治疗中的潜力。BET家族全称为溴结构域和超末端家族蛋白，包含BRD2、BRD3、BRD4和BRDT这4种蛋白，它们能够结合特定的表观遗传修饰，调控包括炎症因子在内的多种基因表达。此前有研究揭示了BET抑制剂在癌症治疗中的潜力，也提出了开发选择性BET抑制剂的迫切需求。而在这项近期研究中，韩国延世大学的研究团队基于药明康德提供的DEL技术，筛选出对BRD2和BRD4的特定结构域表现出高选择性的新型BET抑制剂——KB-0118。在体外实验中，KB-0118抑制了T细胞和巨噬细胞中多种促炎因子的表达，同时上调了抗炎因子IL-10，显示出双向免疫调节潜力。尤其值得注意的是，KB-0118在减少促炎性Th17细胞的同时，促进了Th2细胞向着抗炎的趋势分化，因此在IBD等慢性炎症性疾病中具有控制过度免疫激活的潜力。随后的动物实验证实， KB-0118能显著减轻溃疡性结肠炎和克罗恩病模型的疾病严重程度，同时表现出良好的安全性。接下来，研究团队仍需进一步验证KB-0118的长期安全性、人体药代动力学特征及疗效，为临床试验奠定基础。在DEL兴起的同时，药明康德建立并完善了DEL技术能力。2018年以来，药明康德持续向工业界和学术界客户提供多种基于DEL的筛选服务，以满足不同合作伙伴的需求，加速新药发现。图片来源：123RF药明康德的DEL平台作为全球创新药物研发的有力工具，在过去几年中已助力来自世界各地的科学家成功发现了一系列具有突破性治疗潜力的创新分子。在该团队此前发布的两篇研究论文中，药明康德DEL平台先后赋能研究团队，发现了对白血病和实体瘤具有治疗潜力的泛BET抑制剂BBC1115，以及具有缓解骨关节炎潜力的选择性BET抑制剂BBC0906。此次赋能延世大学研究团队筛选出新型BET抑制剂，是药明康德DEL平台助力候选药物研发的又一个案例，再次彰显了DEL技术平台在药物发现领域的价值与潜力。如今，在全球科学界的努力下，多款具有不同机制的药物正帮助IBD患者实现疾病控制与症状缓解。与此同时，人们对IBD发病机制的探索也不曾止步，相信这些进展将为IBD患者带来治愈的曙光。药明康德也期待持续携手全球合作伙伴，以科技创新驱动药物研发，让更多突破性疗法早日惠及全球患者。参考资料：[1] Vieujean, S., Jairath, V., Peyrin-Biroulet, L. et al. Understanding the therapeutic toolkit for inflammatory bowel disease. Nat Rev Gastroenterol Hepatol (2025). https://doi-org.libproxy1.nus.edu.sg/10.1038/s41575-024-01035-7[2] Molecular Basis of Intestinal Fibrosis in Inflammatory Bowel Disease Open Access. Akira Andoh et al., Inflamm Intest Dis (2022) 7 (3-4): 119–127. https://doi-org.libproxy1.nus.edu.sg/10.1159/000528312欢迎转发到朋友圈，谢绝转载到其他平台。如有开设白名单需求，请在“学术经纬”公众号主页回复“转载”获取转载须知。其他合作需求，请联系wuxi_media@wuxiapptec.com。免责声明：本文仅作信息交流之目的，文中观点不代表药明康德立场，亦不代表药明康德支持或反对文中观点。本文也不是治疗方案推荐。如需获得治疗方案指导，请前往正规医院就诊。更多推荐点个“在看”再走吧~

A consortium led by South Korea's top universities, Yonsei University and Seoul National University, will embark on a national project promoting the early detection of autism spectrum disorder in the country. Backed by the National Center for Mental Health and the Ministry of Health and Welfare, the project received 9.2 billion won ($6.6 million) in government funding. According to a media release, the project aims to develop AI-powered digital and medical devices for screening autism, which are targeted for release by 2028. The devices will be run by algorithms based on data from a cohort of 1,200 infants and toddlers below 48 months. Additionally, applications for an innovative medical device designation from the Ministry of Food and Drug Safety will be pursued. WHY IT MATTERS A recent article noted an overall increase in cases of ASD among Korean children and adolescents from 2011 to 2021. It has been estimated that the prevalence of DSM-5-defined ASD is at 2.20% . This trend may necessitate calibrating strategies to enable timelier and more effective prevention and care of children with autism. However, current approaches in ASD diagnosis, according to the Yonsei-SNU-led consortium, are largely dependent on specialists' findings and subjective observations of guardians, which often render them inconsistent and short of objectivity. "The AI-based [ASD] screening assistive medical device to be developed through this project will be an opportunity to shift the paradigm of early autism diagnosis and treatment," explained Chun Geun-ah, professor at Yonsei University Severance Hospital and principal project investigator. "The consortium will establish a foundation to overcome the limitations of diagnosing [ASD] and lead to better treatment outcomes," Kim Boong-nyeon, SNU Hospital (SNUH) professor and co-principal investigator, also said. The consortium involves several major hospitals, such as Yonsei's Gangnam Severance Hospital, Ewha Womans University Hospital in Seoul and Mokdong, and Seoul St. Mary’s Hospital, and digital health companies, including HurayPositive and Adotcure. THE LARGER TREND In 2023, SNUH opened a living laboratory to collect data that will be used to develop algorithm-driven models for detecting and personalising the treatment of ASD. The lab, also supported by the NCMH, serves as a bedrock for new digital therapeutics and the discovery of autism biomarkers. AI has seen increasing applications in ASD screening and treatment. EarliTec Diagnostics in the United States is a major developer in this space. It received regulatory approval for its autism decision support tool in 2022. Meanwhile, in Japan, new research utilised eye-tracking technology to demonstrate how predictable movement stimuli can be potentially used as a behavioural marker for autism .