Development of a Machine Learning Prediction Model for the Detection of Meniere's Disease from Cerumen Chemical Profiles

摘要/项目摘要 梅尼埃病是一种慢性、不可治愈的前庭疾病，会产生一系列反复出现的症状，如 内耳异常大量内淋巴的结果。这种疾病的表现包括 反复发作的眩晕、耳鸣、失衡、恶心和/或呕吐、饱腹感或压力感 在耳朵，和波动，进行性低频听力损失。诊断很困难，因为其他 神经系统疾病呈现出一些相同的症状。因此，梅尼埃病的诊断，即 具有挑战性、不精确和耗时的，涉及排除其他疾病的艰苦过程 症状重叠。因为它没有已知的化学或放射标记，所以诊断是基于 对临床症状概要的观察，和误诊相当普遍。如果是化学的 梅尼埃氏症和其他相关神经疾病的标志物可以被确定，更快和 可以基于对这些相关的存在(或不存在)的评估来实现准确的诊断 化合物。这里假设，Cerumen的化学特征可以作为一种报告 存在梅尼埃氏病和其他神经系统疾病，症状重叠， 可以利用这些差异配置文件的知识来准确和快速地揭示 梅尼埃病。这一假设将通过追求以下具体目标来进行研究： 具体目标一：收集和测定来自中国的鹿茸的光谱化学特征 健康捐赠者，梅尼埃病患者，以及被诊断为其他神经系统疾病的患者 症状重叠。 具体目标二：开发能够准确确定的机器学习预测模型 梅尼埃病和/或其他神经学疾病的存在， 并揭示了对区分能力很重要的化合物子集的存在 梅尼埃病的样本来自其他人。 特定目标III：通过机器学习预测揭示的化合物的结构特征 模型(S)是在特定目标II中开发的，与梅尼埃病有关。 这项工作的结果将揭示耳垢的脂质分布与 特定疾病状态的存在。将获得有关分子的结构信息 对健康患者和梅尼埃病患者的差异负责。透露的信息将 为开发一种潜在的非侵入性快速诊断方法提供机会 梅尼埃病。

ABSTRACT/PROJECT SUMMARY Ménière’s disease is a chronic, incurable vestibular disorder that produces a recurring set of symptoms as a result of abnormally large amounts of endolymph in the inner ear. Manifestations of the disease include recurrent episodes of vertigo, tinnitus, imbalance, nausea and/or vomiting, a feeling of fullness or pressure in the ear, and fluctuating, progressive low-frequency hearing loss. Diagnosis is difficult because other neurological conditions present some of the same symptoms. Thus, Ménière’s disease diagnosis, which is challenging, imprecise, and time consuming, involves the painstaking process of excluding other diseases with overlapping symptoms. Because it has no known chemical or radiographic markers, diagnosis is based on the observation of a clinical compendium of symptoms, and misdiagnosis is fairly common. If chemical markers of Ménière’s and other relevant neurological disorders could be determined, more rapid and accurate diagnosis could be achieved based on assessment of the presence (or absence) of these relevant compounds. It is hypothesized here that the chemical profile of cerumen can serve as a reporter of the presence of Ménière’s disease and other neurological disorders with overlapping symptoms, and that knowledge of these differential profiles can be leveraged to accurately and rapidly reveal the presence of Ménière’s disease. This hypothesis will be investigated through pursuit of the following specific aims: Specific Aim I: Collection and determination of the mass spectral chemical signatures of cerumen from healthy donors, Ménière’s disease patients, and patients diagnosed with other neurotological disorders with overlapping symptoms. Specific Aim II: Development of machine learning prediction models that enable accurate determination of the presence of Ménière’s disease and/or other neurotological disorders from cerumen chemical profiles, and reveal the presence of the subset of compounds that are important for the ability to distinguish Ménière’s disease samples from others. Specific Aim III: Structural characterization of compounds revealed by the machine learning prediction model(s) developed in Specific Aim II, to be associated with Ménière’s disease. The results of this work will reveal whether there is a correlation between the lipid profile of earwax and the presence of particular disease states. Structural information will be acquired on the molecules that are responsible for the differences in healthy and Ménière’s disease patients. The information revealed would provide the opportunity for development of a potential non-invasive method for the rapid diagnosis of Ménière’s disease.