A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer's Disease Using Structural MRI Images.

Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.

Stimulating Fungi Pleurotus ostreatus with Hydrocortisone.

Fungi cells can sense extracellular signals via reception, transduction, and response mechanisms, allowing them to communicate with their host and adapt to their environment. They feature effective regulatory protein expressions that enhance and regulate their response and adaptation to various triggers such as stress, hormones, physical stimuli such as light, and host factors. In our recent studies, we have shown that Pleurotus oyster fungi generate electrical potential impulses in the form of spike events in response to their exposure to environmental, mechanical, and chemical triggers, suggesting that the nature of stimuli may be deduced from the fungal electrical responses. In this study, we explored the communication protocols of fungi as reporters of human chemical secretions such as hormones, addressing whether fungi can sense human signals. We exposed Pleurotus oyster fungi to hydrocortisone, which was directly applied to the surface of a fungal-colonized hemp shavings substrate, and recorded the electrical activity of the fungi. Hydrocortisone is a medicinal hormone replacement that is similar to the natural stress hormone cortisol. Changes in cortisol levels released by the body indicate the presence of disease and can have a detrimental effect on physiological process regulation. The response of fungi to hydrocortisone was also explored further using X-rays to reveal changes in the fungi tissue, where receiving hydrocortisone by the substrate can inhibit the flow of calcium and, as a result, reduce its physiological changes. This research could open the way for future studies on adaptive fungal wearables capable of detecting human physiological states and biosensors built of living fungi.