Thrombosis is one of the leading causes of death worldwide. Out of four, one person is dying of thrombosis; yet, the seriousness of this disease is underappreciated. Its early prediction and prevention continue to be a dilemma that confuses researchers. Nevertheless, a light can be seen at the end of the tunnel; thanks to nanoscience which has led to the development of new generations of nanostructure with different applications in bio-medicine and bio-engineering. The key paradigm for the Internet of Nano Things (IoNT) has allowed for new medical data to be collected which potentially helps achieve more accurate disease prediction. It has enabled real-time health services and turned the physical space of a patient into a smart space. While an enabler for several applications, the artificial nature of Internet of Nano Things devices can be harmful where the implementation of Nano Things may lead to unintended health effects. To overcome this issue, researchers have suggested the novel paradigm of the IoBNT that combines nanotechnology with tools from synthetic biology to provide reengineering of biological embedded computing devices. IoBNT promises many medical applications, such as intra-body sensing and actuation networks, based on biological cells and their characteristics in the biochemical field. In this paper, a novel IoBNT-based model with an optimized Bio-Cyber communication interface that helps predict and analyze blood vessel clots is introduced. The model utilizes a bio-interface to collect information on the blood vessels and convert it into an electrical equivalent format. Furthermore, the optical or thermal responsiveness excites the release of definite nano-carrier molecules such as liposomes which may be devised across the bloodstream and enter the targeted area passively to stimulate suitable nano-devices to predict the clots. The Bio-Cyber interface is used for linking the traditional electromagnetic wave to the Bio-Signaling Network based on the bioluminescence concept. Lab-scale simulation analysis shows prominent outcomes in the prediction of blood vessel clots with 97.66% accuracy and 12.22% tolerance level in error rate.

血栓形成是全球死亡的主要原因之一。四分之一的人死于血栓形成；然而，这种疾病的严重性并未得到重视。它的早期预测和预防仍然是困扰研究人员的难题。尽管如此，在隧道尽头仍能看到光线。得益于纳米科学，纳米科学导致了在生物医学和生物工程领域具有不同应用的新一代纳米结构的发展。纳米物联网（IoNT）的关键范例允许收集新的医学数据，这可能有助于实现更准确的疾病预测。它启用了实时健康服务，并将患者的物理空间转变为智能空间。虽然可以用于多种应用，纳米物联网设备的人为性质可能有害，因为实施纳米物可能会导致意想不到的健康影响。为了克服这个问题，研究人员提出了IoBNT的新型范例，该范例将纳米技术与合成生物学的工具相结合，可以对生物嵌入式计算设备进行重新设计。基于生物细胞及其在生化领域的特性，IoBNT有望在许多医疗应用中使用，例如体内感应和驱动网络。本文介绍了一种基于IoBNT的新型模型，该模型具有经过优化的Bio-Cyber​​通信接口，可帮助预测和分析血管凝块。该模型利用生物界面收集有关血管的信息，并将其转换为等效的电子格式。此外，光学或热响应性会激发特定的纳米载体分子（例如脂质体）的释放，这些分子可能会在整个血流中形成并被动进入目标区域，以刺激合适的纳米装置来预测血凝块。Bio-Cyber​​接口用于基于生物发光概念将传统电磁波链接到Bio-Signaling网络。实验室规模的仿真分析显示，在预测血管凝块方面有显着结果，准确率达97.66％，错误率容忍度为12.22％。Bio-Cyber​​接口用于基于生物发光概念将传统电磁波链接到Bio-Signaling网络。实验室规模的仿真分析显示，在预测血管凝块方面有显着结果，准确率达97.66％，错误率容忍度为12.22％。Bio-Cyber​​接口用于基于生物发光概念将传统电磁波链接到Bio-Signaling网络。实验室规模的仿真分析显示，在预测血管凝块方面有显着结果，准确率达97.66％，错误率容忍度为12.22％。