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Summary: The Chlamydia pneumoniae bacterium can travel directly from the olfactory nerve to the nose and brain, forcing brain cells to deposit amyloid beta and causing Alzheimer’s disease. Researchers say that protecting the nasal lining by not picking or plucking nasal hairs may help reduce the risk of Alzheimer’s.
Source: Griffith University
Researchers at Griffith University have shown that a bacterium in mice can travel through the olfactory nerve in the nose and into the brain, where it produces markers that are a telltale sign of Alzheimer’s disease.
The study published in the journal Scientific Reports, showed that Chlamydia pneumoniae used the nerve that stretches between the nasal cavity and the brain as an invasion route to invade the central nervous system. The cells in the brain then responded by depositing amyloid-beta protein, which is a hallmark of Alzheimer’s disease.
Professor James St. John, director of the Clem Jones Center for Neurobiology and Stem Cell Research, is co-author of the world’s first research paper.
“We are the first to show that Chlamydia pneumoniae can go straight through the nose to the brain, where it can trigger pathologies that resemble Alzheimer’s disease,” said Professor St. John. “We’ve seen this in a mouse model, and the evidence may be frightening for humans, too.”
The olfactory nerve in the nose is directly exposed to the air and provides a short route to the brain that bypasses the blood-brain barrier. It’s a route that viruses and bacteria have sniffed out as an easy way into the brain.
The center’s team is already planning the next phase of research and wants to prove that the same pathway exists in humans.
“We need to do this human study and confirm if the same way works the same way. It is research that has been suggested by many people but not yet completed. What we do know is that the same bacteria are present in humans, but we haven’t figured out how they get there.”
There are some simple steps to take care of the nasal lining that Professor St. John suggests people can take now if they want to lower their risk of potentially developing late-onset Alzheimer’s disease.
“Picking your nose and picking hair out of your nose is not a good idea,” he said.
“We don’t want to damage the inside of our noses, and picking and plucking can do that. If you damage the lining of your nose, you can increase the number of bacteria that can get into your brain.”
Smell tests could also have potential as detectors for Alzheimer’s and dementia, says Professor St. John, since the loss of the sense of smell is an early indicator of Alzheimer’s disease. He suggests that smell testing from the age of 60 could be helpful as early detection.
“Once you are over 65, your risk factor goes up, but we look at other causes as well, because it’s not just age, it’s environmental exposure. And we think bacteria and viruses are critical.”
About this news from Alzheimer’s research
Author: press office
Source: Griffith University
Contact: Press Office – Griffith University
Picture: The image is in the public domain
Original research: Open access.
“Generalizable deep learning model for early detection of Alzheimer’s disease from structural MRIs” by Sheng Liu et al. Scientific Reports
See also
abstract
Generalizable deep learning model for early detection of Alzheimer’s disease from structural MRIs
Early detection of Alzheimer’s disease plays a central role in patient care and clinical trials. In this study, we developed a new approach based on 3D Deep Convolutional Neural Networks to accurately distinguish mild Alzheimer’s dementia from mild cognitive impairment and from cognitively normal individuals using structural MRIs.
For comparison, we created a reference model based on the volumes and thicknesses of previously reported brain regions known to be involved in disease progression.
We validate both models on an internal, restrained Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and on an external, independent National Alzheimer’s Coordinating Center (NACC) cohort.
The deep learning model is accurate, achieving an area under the curve (AUC) of 85.12 in distinguishing between cognitively 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, which requires volume and thickness to be extracted beforehand.
The model can also be used to predict progression: people with mild cognitive impairment, who the model misclassified as dementia with mild Alzheimer’s disease, progressed to dementia more rapidly over time. An analysis of the features learned from the proposed model shows that it relies on a variety of regions implicated in Alzheimer’s disease.
These results suggest that deep neural networks can automatically learn to identify imaging biomarkers that predict Alzheimer’s disease and use them to achieve accurate early detection of the disease.
