Celiac disease is an autoimmune disorder represented by the ingestion of the gluten protein usually found in wheat, barley and rye. To date, ELISA has been the most accurate method for determining the presence of anti-gliadin, which is cumbersome, expensive (compared to a suspension microarray technique), and requires extensive sample preparation. In this study, in order to establish a more accurate assay to identify gliadin at lower concentrations, optical nano biosensors using an indirect immunoassay method for gliadin detection was designed and fabricated. For this, polycaprolactone (PCL) nano- to micro-beads were fabricated as a platform for the gliadin antigen which were optimized and nano functionalized with amine groups for such purposes. The gliadin antibody, which is selective to gliadin, was then added to the beads. Static light scattering tests were conducted to determine PCL particle size distribution and sizes were found from 0.1 to 30 μm, which is suitable for flowcytometry detection devices. Anti-gliadin detection was performed using an anti IgG mouse antibody conjugated with FITC in a flow cytometry device to detect the smallest particle. Fluorescence intensity was investigated at different concentrations of anti-gliadin and a standard curve used to determine gluten concentration based on fluorescence intensity. Results showed that the fluorescence intensity increased with greater concentrations of anti-gliadin providing a very effective method of detection due to selectivity at a 5 ppm detection limit. This represents a new highly sensitive and fast method for anti-gliadin detection. Further, the disuse of a cross linker and the use of a dedicated antibody at a very low level (1 μl) made this new method very economical to identify anti-gliadin concentrations at the nano level. In summary, this study provides a new, more accurate and sensitive, as well as less expensive system to detect anti-gliadin for the improved diagnosis of celiac disease.We propose a new methodology for the fabrication and evaluation of scintillating detector elements using a consumer grade fusion deposition modeling (FDM) 3D printer. In this study we performed a comprehensive investigation into both the effects of the 3D printing process on the scintillation light output of 3D printed plastic scintillation dosimeters (PSDs) and their associated dosimetric properties. click here Fabrication properties including print variability, layer thickness, anisotropy and extrusion temperature were assessed for 1 cm3 printed samples. We then examined the stability, dose linearity, dose rate proportionality, energy dependence and reproducibility of the 3D printed PSDs compared to benchmarks set by commercially available products. Experimental results indicate that the shape of the emission spectrum of the 3D printed PSDs do not show significant spectral differences when compared to the emission spectrum of the commercial sample. However, the magnitude of scintillation light output was found to be strongly dependent on the parameters of the fabrication process. Dosimetric testing indicates that the 3D printed PSDs share many desirable properties with current commercially available PSDs such as dose linearity, dose rate independence, energy independence in the MV range, repeatability, and stability. These results demonstrate that not only does 3D printing offer a new avenue for the production and manufacturing of PSDs but also allows for further investigation into the application of 3D printing in dosimetry. Such investigations could include options for 3D printed, patient-specific scintillating dosimeters that may be used as standalone dosimeters or incorporated into existing 3D printed patient devices (e.g. bolus or immobilization) used during the delivery of radiation therapy.We present an open-source platform to aid medical dosimetrists in preventing collisions between gantry head and patient or couch during photon or particle beam therapy treatment planning. This generic framework uses the native scripting interface of the particular planning software to import STL files of the treatment machine elements. These are visualized in 3D together with the contoured or scanned patient surface. A graphical dialog with sliders allows the interactive rotation of the gantry and couch, with real-time feedback. To prevent a future replanning, treatment planners can assess in advance and exclude beam angles resulting in a potential risk of collision. The software platform is publicly available on GitHub and has been validated for RayStation with actual patient plans. Furthermore, the incorporation of the complete patient geometry was tested with a 3D surface scan of a full-body phantom performed with a handheld smartphone. With this study, we aim at minimizing the risk of replanning due to collisions and thus of treatment delays and unscheduled consumption of manpower. The clinical workflow can be streamlined at no cost already at the treatment planning stage. By ensuring a real-time verification of the plan feasibility, the script might boost the use of optimal couch angles that a planner might shy away from otherwise.It has been reported that when a grounded human is exposed to an electric field at power frequency, a short-circuit current flowing from the feet to the ground is proportional to the square of his or her height. The current, however, should also vary with the body surface area, that is, body shape, even in people with the same height. In the present study, we confirmed this hypothesis using an analytical solution derived from a semi-ellipsoidal model. The short-circuit currents were calculated for various numerical human body models in which the horizontal length of a voxel was varied from 1.8 to 3.0 mm, and the results for different body shapes were compared. Finally, we derived an approximate expression for estimating the short-circuit current from the left-right width (2b), frontal thickness (2c), and height (a) of a human from the analytical solution. The short-circuit currents obtained from the approximate expression are consistent with those obtained from numerical calculations for 48 differently shaped human body models with a correlation coefficient of 0.