RESUMO
Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.
RESUMO
Disease treatment with advanced biological therapies such as adalimumab (ADM), although largely beneficial, is still costly and suffers from loss of response. To tackle these aspects, therapeutic drug monitoring (TDM) is proposed to improve treatment dosing and efficacy, but is often associated with long sampling-to-result workflows. Here, we present an in-house constructed ADM-sensor, allowing TDM of ADM at the doctor's office. This biosensor brings fiber optic surface plasmon resonance (FO-SPR), combined with self-powered microfluidics, to a point of care (POC) setting for the first time. After developing a rapid FO-SPR sandwich bioassay for ADM detection on a commercial FO-SPR device, this bioassay was implemented on the fully-integrated ADM-sensor. For the latter, we combined (I) a gold coated fiber optic (FO) probe for bioassay implementation and (II) an FO-SPR readout system with (III) the self-powered iSIMPLE microfluidic technology empowering plasma sample and reagent mixing on the-cartridge as well as connection to the FO-SPR readout system. With a calculated limit of detection (LOD) of 0.35 µg/mL in undiluted plasma, and a total time-to-result (TTR) within 12 min, this innovative biosensor demonstrated a comparable performance to existing POC biosensors for ADM quantification in patient plasma samples, while requiring only 1 µL of plasma. Whereas this study demonstrates great potential for FO-SPR biosensing at the POC using ADM as a model case, it also shows huge potential for bedside TDM of other drugs (e.g. other immunosuppressants, anti-epileptics and antibiotics), as the bioassay is highly amenable to adaptation.
