Department of Otolaryngology, Pingyang Hospital Affiliated to Wenzhou Medical University, Pingyang, Zhejiang, 325400, China.
Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
Chemosphere. 2023 Aug;331:138458. doi: 10.1016/j.chemosphere.2023.138458. Epub 2023 Mar 24.
Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.
纳米粒子 (NPs) 是一种有前途的抗生素替代品，可用于靶向微生物，尤其是在治疗困难的细菌疾病方面。纳米技术在医疗设备的抗菌涂层、感染预防和治疗材料、医疗诊断的细菌检测系统以及抗菌免疫接种方面具有潜在的应用。耳部感染会导致听力损失，而且极难治愈。使用纳米粒子来增强抗菌药物的疗效是一种潜在的选择。已经制备了各种类型的无机、基于脂质的和聚合纳米粒子，并显示出对药物的控制给药有益。本文重点介绍了使用聚合纳米粒子治疗人体常见细菌疾病。通过使用机器学习模型（如人工神经网络 (ANNs) 和卷积神经网络 (CNNs)），这项 28 天的研究评估了纳米粒子治疗的疗效。报道了一种先进的 CNN （如 Dense Net）在自动检测中耳感染方面的创新应用。将 3000 张耳内镜图像 (OEIs) 分类为正常、慢性中耳炎 (COM) 和分泌性中耳炎 (OME)。将中耳积液与 OEIs 进行比较，CNN 模型的分类准确率达到 95%，表明对自动识别中耳感染具有很大的应用前景。混合 CNN-ANN 模型的整体准确率超过 0.90%，在区分耳垢和疾病方面的灵敏度为 95%，特异性为 100%，并且提供了近乎完美的 0.99%的措施。纳米粒子是治疗困难的细菌疾病（如耳部感染）的一种有前途的治疗方法。机器学习模型（如 ANNs 和 CNNs）的应用可以提高纳米粒子治疗的疗效，特别是对中耳感染的自动检测。特别是聚合纳米粒子，在治疗儿童常见细菌感染方面显示出疗效，为未来的治疗方法提供了很大的希望。
