Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Effects of substrate stiffness on the viscoelasticity and migration of prostate cancer cells examined by atomic force microscopy.

The stiffness of the extracellular matrix of tumour cells plays a key role in tumour cell metastasis. However, it is unclear how mechanical properties regulate the cellular response to the environmental matrix. In this study, atomic force microscopy (AFM) and laser confocal imaging were used to qualitatively evaluate the relationship between substrate stiffness and migration of prostate cancer (PCa) cells. Cells cultured on stiff substrates (35 kPa) undergone several interesting phenomena compared to those on soft substrates (3 kPa). Here, the stimulation generated by the stiff substrates triggered the F-actin skeleton to bundle its filaments, increasing the polarity index of the external contour of PCa cells. Analysis of AFM force-distance curves indicated that the elasticity of the cells cultured on 35 kPa substrates increased while the viscosity decreased. Wound-healing experiments showed that PCa cells cultured on 35 kPa substrates have higher migration potential. These phenomena suggested that the mechanical properties may be correlated with the migration of PCa cells. After actin depolymerisation, the elasticity of the PCa cells decreased while the viscosity increased, and the migration ability was correspondingly decreased. In conclusion, this study clearly demonstrated the relationship between substrate stiffness and the mechanical properties of cells in prostate tumour metastasis, providing a basis for understanding the changes in the biomechanical properties at a single-cell level.