Human perpetual exposure to several pollutants such as smoke, radiation, and toxic chemical substances, causes an excessive production of reactive oxygen species (ROS), that leads to the onset of neurodegeneration diseases. This knowledge has highlighted the importance of frequent monitoring people health with innovative biosensors able to detect pathological conditions at the initial stage. Indeed, it is mainly stated that neurodegenerative diseases can be effectively treated only if diagnosed very early. In this context, the structural aggregation of biomolecules in different districts of the brain, seem to play a key role in the neurodegeneration development mechanism becoming eligible targets for an early diagnosis. Hereby, we propose an innovative technique for detecting such biomolecules, e.g. Tau, by exploiting a pyro-electrohydrodynamic effect that is able to generate and accumulate tiny droplets of analyte on the surface of a reactive glass slide. We call the technique p-jet and we tested it in case of serial dilutions of Tau protein to demonstrate the consistency of the procedure under an immunodetection-based protocol.
The presence of microgravity and ionizing radiation during spaceflight missions causes excessive Reactive Oxygen Species (ROS) production that contributes to oxidative cellular stress and multifunctional damage in astronauts. This knowledge has underlined the importance of frequent monitoring of astronaut’s health to have early diagnoses. In this scenario, the biosensor diagnostic devices could offer the necessary analytical performance to study pathological astronaut conditions. Herein, we propose an innovative biosensor for detecting highly diluted biomarkers at picogram level by using the pyro-electrohydrodynamic jet (p-jet) system. The detection limit of the system was confirmed using a model protein as the Bovine Serum Albumin (BSA) by optimizing its deposition on different functionalized glass substrates through different chemical reactions starting with a manual procedure. Based on these results, the epoxy glass activated surface was chosen as the best slide for p-jet experiments. The characterization of the processes was performed through different spectroscopic techniques such as infrared-spectroscopy (IR) or confocal fluorescence. In the context of long-term human missions, our revolutionary approach could be extremely useful to monitor the astronaut health.
Human health and disease prevention are among the priorities to safeguard astronauts and, in the next future, space tourists. There is a great demand of new reliable biotechnologies that would be eventually implemented on spacecrafts to observe the space-induced effect on humans. One of the main risks is related to the radiation exposure, that is significantly higher than on Earth. For this reason, space agencies are pushing to develop strategies to quantify, oversee and limiting such risks. Here we present an approach based on the combination of microfluidics and stain-free imaging also aided by artificial intelligence to monitor the effect on ionizing radiation on blood cells. The system is based on the Holographic Image Flow Cytometry system where Quantitative Phase Contrast images are retrieved for cell flowing and rotating into a microfluidics circuits. Proof of concept is demonstrated where morphological parameters are identified able to distinguish cell population irradiated at different radiation doses and at different time from the radiation exposure. Blood cell will be analyzed. The presented approach has main advantages respect to standard and already existent technologies for single cell analysis. The first one is the no-need of fluorescence staining thus opening to faster and easier operation steps. The second one is related to cell rotation into the field of view, allowing to acquire images at different rotation angle and thus collecting a broader dataset useful for the application of artificial intelligence network. Furthermore, the system can be miniaturized to a scale portable out of the laboratory environment.
We present the multimodal characterization of thin polymeric membrane by digital holography-based methods. Herein, two microscope techniques had been chosen to reveal the morphology of membranes, which are conventional off-axis Digital Holography (DH) and Space-Time Digital Holography (STDH). The complementary features of the different methods allow for a bottom-up analysis of the related membranes. Meanwhile, the dynamic forming process of polymeric membrane at the air-water interface is revealed in real-time by CDH. By comparing the imaging results of different methods, the application range of different imaging methods is analyzed in detail.
G. Mettivier, R. Ricciarci, A. Sarno, F. Maddaloni, M. Porzio, M. Staffa, S. Minelli, A. Santoro, E. Antignani, M. Masi, V. Landoni, P. Ordonez, F. Ferranti, L. Greco, S. Clemente, P. Russo
The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.
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