Alpha particle microdosimetry calculations using a shallow neural network

A shallow neural network was trained to accurately calculate the microdosimetric parameters, <z1> and <z1 2> (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 µm), cell radii (2.5–20 µm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71×10-7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80×10-7. The 95th percentile testing data errors were within ±1.4% for <z1> outputs and ±2.8% for <z1 2> outputs, respectively. Cell survival was also predicted using actual vs. neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.

A Physiologically Based Pharmacokinetic Model for In Vivo Alpha Particle Generators Targeting Neuroendocrine Tumors in Mice

In vivo alpha particle generators have great potential for the treatment of neuroendocrine tumors in alpha-emitter-based peptide receptor radionuclide therapy (α-PRRT). Quantitative pharmacokinetic analyses of the in vivo alpha particle generator and its radioactive decay products are required to address concerns about the efficacy and safety of α-PRRT. A murine whole-body physiologically based pharmacokinetic (PBPK) model was developed for 212Pb-labeled somatostatin analogs (212Pb-SSTA). The model describes pharmacokinetics of 212Pb-SSTA and its decay products, including specific and non-specific glomerular and tubular uptake. Absorbed dose coefficients (ADC) were calculated for bound and unbound radiolabeled SSTA and its decay products. Kidneys received the highest ADC (134 Gy/MBq) among non-target tissues. The alpha-emitting 212Po contributes more than 50% to absorbed doses in most tissues. Using this model, it is demonstrated that α-PRRT based on 212Pb-SSTA results in lower absorbed doses in non-target tissue than α-PRRT based on 212Bi-SSTA for a given kidneys absorbed dose. In both approaches, the energies released in the glomeruli and proximal tubules account for 54% and 46%, respectively, of the total energy absorbed in kidneys. The 212Pb-SSTA-PBPK model accelerates the translation from bench to bedside by enabling better experimental design and by improving the understanding of the underlying mechanisms.

Proton Beam Abundance Variations and Their Relation to Alpha Particle Properties

Less abundant but still dynamically important solar wind components are the proton beam and alpha particles, which usually contribute similarly to the total ion momentum. The main characteristics of alpha particles are determined by the solar wind source region, but the origin of the proton beam and its properties are still not fully explained. We use the plasma data measured in situ on the path from 0.3 to 1 au (Helios 1 and 2) and focus on the proton beam development with an increasing radial distance as well as on the connection between the proton beam and alpha particle properties. We found that the proton beam relative abundance increases with increasing distance from the Sun in the collisionally young streams. Among the mechanisms suggested for beam creation, we have identified the wave–particle interactions with obliquely propagating Alfvén modes being consistent with observations. As the solar wind streams get collisionally older, the proton beam decay gradually dominates and the beam abundance is reduced. In search for responsible mechanisms, we found that the content of alpha particles is correlated with the proton beam abundance, and this effect is more pronounced in the fast solar wind streams during the solar maximum. We suggest that Coulomb collisions are the main agent leading to merging of the proton beam and core. We are also showing that the variations of the proton beam abundance are correlated with a decrease of the alpha particle velocity in order to maintain the total momentum balance in the solar wind frame.

isks of incidence of breast cancer in a cohort of females occupationally exposed to ionizing radiation

Breast cancer (BrCa) holds the first rank place in morbidity and mortality due to malignant neoplasms among Russian women. BrCa is a multifactorial disease and ionizing radiation is among factors that cause elevated risks of developing BrCa. Our research aim was to assess relative risk (RR) of incidence of BrCa among women who were occupationally exposed to chronic ionizing radiation taking into account radiation and non-radiation factors. RR of incidence of BrCa was analyzed in a cohort of women employed at a nuclear production enterprise, namely Mayak PA, in 1948–1982. 95 % of women started working at the enterprise at their reproductive age. All those women were chronically exposed to ionizing radiation at their workplaces. A mean cumulative breast absorbed dose of external gamma-ray exposure amounted to 0.45 (standard deviation was 0.68) Gy; an average cumulative muscle absorbed dose of internal alpha-particle exposure amounted to 0.003 (0.01) Gy. According to data taken from “Clinic” medical-dosimetric database, 165 BrCa cases were detected in 157 women of the analyzed cohort (8 women had BrCa in both breasts). Our analysis involved calculating RR of incidence of BrCa in relation to known non-radiation and radiation factors. Categorical data analysis was performed without age-related and calendar period-related stratification and with them. RR was analyzed based on Poisson regression with AMFIT module in EPICURE software package. Incidence of BrCa was revealed to be associated with attained age, age of menarche, age of menopause, number of abortions, age of concomitant diseases prior to cancer diagnosis, height, body mass index, age of hiring at the Mayak PA. There was no relationship between BrCa incidence and cumulative doses of occupational chronic external gamma-ray, internal alpha-particle and neutron exposure.