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[1] arXiv:2507.07604 (交叉列表自 cs.LG) [中文pdf, pdf, html, 其他]

标题： 通过生物神经递质的合成MC：肠道-脑轴的治疗性调节 显示英文标题

标题： Synthetic MC via Biological Transmitters: Therapeutic Modulation of the Gut-Brain Axis

Sebastian Lotter, Elisabeth Mohr, Andrina Rutsch, Lukas Brand, Francesca Ronchi, Laura Díaz-Marugán

主题： 机器学习 (cs.LG) ; 定量方法 (q-bio.QM) ; 组织与器官 (q-bio.TO)

合成分子通信（SMC）是未来医疗系统的关键使能技术，在该系统中，生物纳米事物互联网（IoBNT）设备有助于持续监测患者的生化信号。 为了在传感和执行之间形成闭环，检测和生成体内的分子通信（MC）信号是关键。 然而，通过合成纳米设备在人体内生成信号在SMC中存在挑战，这是由于技术障碍以及法律、安全和伦理问题。 因此，本文考虑一种SMC系统，其中信号是通过调节自然体内MC系统——即肠脑轴（GBA）间接生成的。 治疗性GBA调节已被确立为神经系统疾病（如药物难治性癫痫（DRE））的治疗方法，并通过给予营养补充剂或特定饮食来实现。 然而，介导这些治疗效果的分子信号通路大多未知。 因此，现有的治疗方法是标准化的或基于经验设计的，只能帮助部分患者而无法帮助其他患者。 在本文中，我们提出利用个人健康数据，例如由体内IoBNT设备收集的数据，以设计比现有方法更通用和稳健的基于GBA调节的治疗方法。 为了展示我们方法的可行性，我们定义了治疗性GBA调节的理论要求清单。 然后，我们提出一种机器学习模型，以在仅存在有限GBA调节数据的情况下，验证实际场景中的这些要求。 通过对几个数据集进行评估，我们确认了该模型在识别GBA不同调节因子方面的出色准确性。 最后，我们利用所提出的模型来识别对治疗性GBA调节起重要作用的具体调节通路。 显示英文摘要

Synthetic molecular communication (SMC) is a key enabler for future healthcare systems in which Internet of Bio-Nano-Things (IoBNT) devices facilitate the continuous monitoring of a patient's biochemical signals. To close the loop between sensing and actuation, both the detection and the generation of in-body molecular communication (MC) signals is key. However, generating signals inside the human body, e.g., via synthetic nanodevices, poses a challenge in SMC, due to technological obstacles as well as legal, safety, and ethical issues. Hence, this paper considers an SMC system in which signals are generated indirectly via the modulation of a natural in-body MC system, namely the gut-brain axis (GBA). Therapeutic GBA modulation is already established as treatment for neurological diseases, e.g., drug refractory epilepsy (DRE), and performed via the administration of nutritional supplements or specific diets. However, the molecular signaling pathways that mediate the effect of such treatments are mostly unknown. Consequently, existing treatments are standardized or designed heuristically and able to help only some patients while failing to help others. In this paper, we propose to leverage personal health data, e.g., gathered by in-body IoBNT devices, to design more versatile and robust GBA modulation-based treatments as compared to the existing ones. To show the feasibility of our approach, we define a catalog of theoretical requirements for therapeutic GBA modulation. Then, we propose a machine learning model to verify these requirements for practical scenarios when only limited data on the GBA modulation exists. By evaluating the proposed model on several datasets, we confirm its excellent accuracy in identifying different modulators of the GBA. Finally, we utilize the proposed model to identify specific modulatory pathways that play an important role for therapeutic GBA modulation.

替换提交 (展示 1 之 1 条目 )

[2] arXiv:2507.06337 (替换) [中文pdf, pdf, html, 其他]

标题： 用于原发右心室流出道经导管肺动脉瓣模拟的集成开源框架 显示英文标题

标题： Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts

Christopher N. Zelonis, Jalaj Maheshwari, Wensi Wu, Steve A. Maas, Seda Aslan, Kyle Sunderland, Stephen Ching, Ashley Koluda, Yuval Barak-Corren, Nicolas Mangine, Patricia M. Sabin, Andras Lasso, Devin W. Laurence, Christian Herz, Matthew J. Gillespie, Jeffrey A. Weiss, Matthew A. Jolley

评论： 25页，7图，2视频，预印本已提交至《计算机方法与生物医学程序》

主题： 组织与器官 (q-bio.TO) ; 医学物理 (physics.med-ph)

背景 - 法洛四联症（ToF）的跨环修补术会导致肺动脉瓣功能不全，从而引起晚期发病率和死亡率。经导管原发性流出道肺动脉瓣置换术（TPVR）已成为常见手术，但评估患者是否适合手术以及选择最佳器械仍具有挑战性。我们展示了一种集成的开源工作流程，用于在图像衍生模型中模拟TPVR，以指导器械选择。方法 - 实施了基于机器学习的CT扫描分割，以定义右心室流出道（RVOT）。在SlicerHeart中实现了设备定位和预压缩的自定义工作流程。生成的几何结构被导出到FEBio进行模拟。使用在SlicerHeart和FEBio中实现的自定义指标进行结果可视化和量化。结果 - RVOT模型创建和设备放置可在1分钟内完成。使用FE模拟的虚拟设备放置在视觉上模仿实际设备放置，并允许量化血管应变、应力和接触面积。在TPVs与RVOT壁接触的近端和远端位置观察到较高应变和应力区域。在模拟中未观察到其他一致趋势。RVOT、支架和RVOT内不同位置的机械指标的观察到的差异表明，没有一种器械在所有解剖结构中都能表现最佳，从而强化了基于模拟的个性化患者评估的必要性。结论 - 本研究证明了一种新型开源工作流程在快速模拟TPVR中的可行性，经过进一步改进，可能有助于评估患者是否适合手术以及选择最佳器械。 显示英文摘要

Background - Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated open-source workflow for simulation of TPVR in image-derived models to inform device selection. Methods - Machine learning-based segmentation of CT scans was implemented to define the right ventricular outflow tract (RVOT). A custom workflow for device positioning and pre-compression was implemented in SlicerHeart. Resulting geometries were exported to FEBio for simulation. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Results - RVOT model creation and device placement could be completed in under 1 minute. Virtual device placement using FE simulations visually mimicked actual device placement and allowed quantification of vessel strain, stress, and contact area. Regions of higher strain and stress were observed at the proximal and distal end locations of the TPVs where the devices impinge the RVOT wall. No other consistent trends were observed across simulations. The observed variability in mechanical metrics across RVOTS, stents, and locations in the RVOT highlights that no single device performs optimally in all anatomies, thereby reinforcing the need for simulation-based patient-specific assessment. Conclusions - This study demonstrates the feasibility of a novel open-source workflow for the rapid simulation of TPVR which with further refinement may inform assessment of patient candidacy and optimal device selection.