Abstract Background Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease’s complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell’s physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification. Methods We analyzed plasma-derived EVs of ALS patients (n= 106) and controls (n=96), and SOD1 G93A and TDP-43 Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis. Results Our procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. There were more particles in the plasma of ALS patients and the two mouse models of ALS while their average diameter was smaller. HSP90 was differentially represented in ALS patients and mice compared to the controls. In terms of disease progression, the levels of cyclophilin A, with the EV size distribution, distinguished fast and slow disease progressors, suggesting a new means for patient stratification. We also measured the levels of phosphorylated TDP-43 and showed that is not an intravesicular cargo of plasma-derived EVs. Conclusions Our analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.
This work deals with the design, fabrication, and thermal characterization of a disposable miniaturized Polymerase Chain Reaction (PCR) module that will be integrated in a portable and fast DNA analysis system. It is composed of two independent parts: a silicon substrate with embedded heater and thermometers and a PDMS (PolyDiMethylSiloxane) chamber reactor as disposable element; the contact between the two parts is assured by a mechanical clamping obtained using a Plastic Leaded Chip Carrier (PLCC). This PLCC is also useful, avoid the PCR mix evaporation during the thermal cycles. Finite Element Analysis was used to evaluate the thermal requirements of the device. The thermal behaviour of the device was characterized revealing that the temperature can be controlled with a precision of ±0.5°C. Different concentrations of carbon nanopowder were mixed to the PDMS curing agent in order to increase the PDMS thermal conductivity and so the temperature control accuracy.
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