Ruian People's Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China.
College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China.
Environ Res. 2023 Nov 1;236(Pt 1):116457. doi: 10.1016/j.envres.2023.116457. Epub 2023 Jul 15.
Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.
在过去的几十年中，由于为内耳治疗中全身药物给药建立了新的给药方法，学术和治疗领域在向内耳输送药物方面都取得了重大进展。除了传统的治疗方法外，还在研究新型技术，如纳米颗粒和水凝胶。利用微系统技术的最新发展，正在开发内耳药物输送方法，这些方法旨在将药物直接输送到内耳。这些设备用于干细胞治疗、RNA 干扰以及耳蜗植入期间神经营养因子和类固醇的输送。人工神经网络 (ANNs) 在药物研究中的深入分析可以在《药物输送、设计和处置中的人工神经网络》一书中找到。由于其在非常复杂的环境中学习和自我纠正的能力，该预测工具具有很大的潜力，可以帮助研究人员更成功地设计、开发和输送成功的药物。ANN 在准确区分疾病方面达到了 0.90 以上的高精度，同时具有 95%的灵敏度和 100%的特异性。此外，ANN 模型提供了近乎完美的 0.99%的测量值。纳米颗粒作为治疗中耳炎等难以治疗的细菌性感染的可行治疗方法具有潜力。ANN 的使用有可能增强纳米颗粒治疗的有效性，特别是在自动识别中耳炎方面。聚合物纳米颗粒已被证明在治疗儿科患者常见细菌感染方面有效，这表明它们在未来的治疗干预方面具有很大的潜力。最后，本研究基于过去十年（2012-2022 年）使用机器学习对内耳疾病治疗方法的研究。
