新研究表明，挖鼻孔会增加患阿尔茨海默症和痴呆的风险

诸平

Fig. 1 Bacterium   Chlamydia pneumoniae (green) inside the brain of a mouse, and surrounded with   beta amyloid peptide (red) (amyloid peptide is a hallmark problem in   Alzheimer's disease)

Fig.   2 James   A. St John

Fig. 3 Overview of the deep learning framework and performance for Alzheimer’s automatic diagnosis. (a) Deep learning framework used for automatic diagnosis. (b) Receiver operating characteristic (ROC) curves for classification of cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD), computed on the ADNI held-out test set. (c) ROC curves for classification of cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) on the NACC test set. (d) Visualization using t-SNE projections of the features computed by the proposed deep-learning model. Each point represents a scan. Green, blue, red colors indicate predicted cognitive groups. CN and AD scans are clearly clustered. (e) Visualization using t-SNE projections of the 138 volumes and thickness in the ROI-volume/thickness model. Compared to (d) the separation between CN and AD scans is less marked. The t-SNE approach is described in details in the methods section. Credit: Scientific Reports (2022). DOI: 10.1038/s41598-022-20674-x

在日常生活中经常会看见有人挖鼻孔、拔鼻毛的动作，自以为是在讲卫生，清理鼻孔垃圾。但殊不知这样有损于鼻腔内壁，会增加患阿尔茨海默症（Alzheimer's）和痴呆的风险。

据澳大利亚格里菲斯大学（Griffith University, Australia）2022年10月28日报道，新研究表明，挖鼻孔会增加患阿尔茨海默症（Alzheimer's）和痴呆的风险（New research suggests nose picking could increase risk for Alzheimer’s and dementia）。

格里菲斯大学的研究人员已经证明，一种细菌可以通过鼻子的嗅觉神经进入老鼠的大脑，在那里产生阿尔茨海默氏症的标志。相关研究结果于2022年2月17日已经在《科学报告》(Scientific Reports)杂志网站发表——Anu Chacko, Ali Delbaz, Heidi Walkden, Souptik Basu, Charles W. Armitage, Tanja Eindorf, Logan K. Trim, Edith Miller, Nicholas P. West, James A. St John, Kenneth W. Beagley, Jenny A. K. Ekberg. Chlamydia pneumoniae can infect the central nervous system via the olfactory and trigeminal nerves and contributes to Alzheimer’s disease risk. Scientific Reports, Published: 17 February 2022. Volume 12, Article number: 2759. DOI: 10.1038/s41598-022-06749-9. https://www.nature.com/articles/s41598-022-06749-9

参与此项研究的除了来自澳大利亚格里菲斯大学的研究人员之外，还有来自英国伦敦国王学院（King’s College London, UK）以及澳大利亚昆士兰科技大学（ueensland University of Technology, Brisbane, Australia）的研究人员。

研究表明，肺炎衣原体（Chlamydia pneumoniae）利用鼻腔（nasal cavity）和大脑之间的神经作为入侵路径，入侵中枢神经系统。然后，大脑中的细胞通过沉积淀粉样β蛋白（depositing amyloid beta protein）做出反应，淀粉样β蛋白是阿尔茨海默病的标志。

克莱姆·琼斯神经生物学和干细胞研究中心（Clem Jones Center for Neurobiology and Stem Cell Research）主任詹姆斯·圣约翰（James St John，Fig. 2）教授是世界上第一个研究的作者之一。詹姆斯·圣约翰教授说：“我们首次发现肺炎衣原体可以直接进入鼻子进入大脑，在那里引发类似阿尔茨海默氏病的疾病。我们在小鼠模型（mouse model）中看到了这种情况，而这一证据对人类来说可能也是可怕的。”

鼻子中的嗅觉神经直接暴露在空气中，为大脑提供了一条短路径，绕过了血脑屏障（blood-brain barrier）。病毒和细菌已经嗅到了进入大脑的一条容易的途径。

该研究中心的团队已经在计划下一阶段的研究，目的是证明人类也存在同样的途径。

“我们需要在人类身上进行这项研究，并确认相同的途径是否以同样的方式发挥作用。这项研究已经由许多人提出，但尚未完成。我们知道的是，人类体内也存在这些细菌，但我们还没有弄清它们是如何到达那里的。”

詹姆斯·圣约翰教授建议，如果人们想降低患上晚发性阿尔茨海默氏病的风险，现在就可以采取一些简单的措施来护理鼻腔。

他说：“挖鼻孔然后拔掉鼻毛不是个好主意。我们不想损伤鼻子内部，而抠鼻孔和拔鼻毛可以做到这一点。如果你损伤了鼻腔内壁，那么进入大脑的细菌数量就会增加。”

詹姆斯·圣约翰教授说，嗅觉测试也可能作为阿尔茨海默氏病和痴呆症的检测器，因为嗅觉丧失是阿尔茨海默氏病的早期指标。他建议，当一个人到了60岁时，进行嗅觉测试可以作为早期检测手段。

“一旦你超过65岁，你的患病风险就会上升，但我们也在研究其他原因，因为这不仅仅是年龄的原因，还有暴露在环境中的原因。我们认为细菌和病毒至关重要。”

此研究得到了戈达基金会（Goda Foundation）、克莱姆·琼斯基金会（Clem Jones Foundation）、ARC发现资助(ARC Discovery Grant DP150104495)、孟席斯健康研究所昆士兰能力资助(Menzies Health Institute Queensland Capacity Grant)、以及格里菲斯大学国际研究生研究奖学金(Griffith University International postgraduate research scholarship简称GUIPRS)的资助或支持。

上述介绍，仅供参考。欲了解更多信息，敬请注意浏览原文或者相关报道。

鼻子中的细菌可能增加患老年痴呆症的风险(Bacteria in the nose may increase risk of Alzheimer's disease)

Abstract (DOI: 10.1038/s41598-022-06749-9)

Chlamydia pneumoniae is a respiratory tract pathogen but can also infect the central nervous system (CNS). Recently, the link between C. pneumoniae CNS infection and late-onset dementia has become increasingly evident. In mice, CNS infection has been shown to occur weeks to months after intranasal inoculation. By isolating live C. pneumoniae from tissues and using immunohistochemistry, we show that C. pneumoniae can infect the olfactory and trigeminal nerves, olfactory bulb and brain within 72 h in mice. C. pneumoniae infection also resulted in dysregulation of key pathways involved in Alzheimer's disease pathogenesis at 7 and 28 days after inoculation. Interestingly, amyloid beta accumulations were also detected adjacent to the C. pneumoniae inclusions in the olfactory system. Furthermore, injury to the nasal epithelium resulted in increased peripheral nerve and olfactory bulb infection, but did not alter general CNS infection. In vitro, C. pneumoniae was able to infect peripheral nerve and CNS glia. In summary, the nerves extending between the nasal cavity and the brain constitute invasion paths by which C. pneumoniae can rapidly invade the CNS likely by surviving in glia and leading to Aβ deposition.

也可以参看美国纽约大学数据科学中心（Center for Data Science, NYU, USA）、纽约大学格罗斯曼医学院（NYU Grossman School of Medicine）以及纽约大学库朗数学科学研究所（Courant Institute of Mathematical Sciences, NYU）的研究人员的相关研究——Sheng Liu, Arjun V. Masurkar, Henry Rusinek, Jingyun Chen, Ben Zhang, Weicheng Zhu, Carlos Fernandez-Granda, Narges Razavian. Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Scientific Reports, 2022, 12, Article number: 17106. DOI: 10.1038/s41598-022-20674-x. Published: 17 October 2022. https://www.nature.com/articles/s41598-022-20674-x

Abstract (DOI: 10.1038/s41598-022-20674-x)

Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.