No Arabic abstract
Low-dose ionizing radiation may induce far-reaching consequences in human, especially regarding intrauterine development. Many studies have documented that the risks of in utero irradiation remain controversial and no effect is reported at doses below 50 mGy. Animal models are often used to clarify the non-fully understood impact of intrauterine irradiation and allow the manipulation of several experimental setups, making possible the analysis of a wide range of end points. We investigated the impact of in utero low-dose X-ray irradiation on postnatal development in rat offspring through a set of well-established behavioral parameters and weight gain. To investigate the hypothesis of postnatal behavioral and physiological alterations due to prenatal low-dose ionizing radiation we exposed pregnant Wistar to 15 mGy of X-rays on gestational days 8 and 15 and control mothers. This low-dose value into diagnostic range can be achieved in a single radiological exam. Four male animals were select from each litter. At infant age, eye-opening test and negative geotaxis tests were performed. Animals were tested at postnatal ages 30 and 70 days in open field, elevated plus-maze, and hole board tests. We evaluated the weight gain of all animals throughout the experiment. The results presented differences between irradiated and non-irradiated animals. Exposed animals presented lower weight gain in adult life, impairment in central nervous system since infant phase, behavioral alterations persisting into later life, and motor coordination impairment. Effects at doses under 100 mGy have not been reported, however, the present study demonstrate that 15 mGy intrauterine exposure was able to generate deleterious effects.
In embryonic development, programmed cell shape changes are essential for building functional organs, but in many cases the mechanisms that precisely regulate these changes remain unknown. We propose that fluid-like drag forces generated by the motion of an organ through surrounding tissue could generate changes to its structure that are important for its function. To test this hypothesis, we study the zebrafish left-right organizer, Kupffers vesicle (KV), using experiments and mathematical modeling. During development, monociliated cells that comprise the KV undergo region-specific shape changes along the anterior-posterior axis that are critical for KV function: anterior cells become long and thin, while posterior cells become short and squat. Here, we develop a mathematical vertex-like model for cell shapes, which incorporates both tissue rheology and cell motility, and constrain the model parameters using previously published rheological data for the zebrafish tailbud [Serwane et al.] as well as our own measurements of the KV speed. We find that drag forces due to dynamics of cells surrounding the KV could be sufficient to drive KV cell shape changes during KV development. More broadly, these results suggest that cell shape changes could be driven by dynamic forces not typically considered in models or experiments.
In this paper we present the results of a $sim$5 hour airborne gamma-ray survey carried out over the Tyrrhenian sea in which the height range (77-3066) m has been investigated. Gamma-ray spectroscopy measurements have been performed by using the AGRS_16L detector, a module of four 4L NaI(Tl) crystals. The experimental setup was mounted on the Radgyro, a prototype aircraft designed for multisensorial acquisitions in the field of proximal remote sensing. By acquiring high-statistics spectra over the sea (i.e. in the absence of signals having geological origin) and by spanning a wide spectrum of altitudes it has been possible to split the measured count rate into a constant aircraft component and a cosmic component exponentially increasing with increasing height. The monitoring of the count rate having pure cosmic origin in the >3 MeV energy region allowed to infer the background count rates in the $^{40}$K, $^{214}$Bi and $^{208}$Tl photopeaks, which need to be subtracted in processing airborne gamma-ray data in order to estimate the potassium, uranium and thorium abundances in the ground. Moreover, a calibration procedure has been carried out by implementing the CARI-6P and EXPACS dosimetry tools, according to which the annual cosmic effective dose to human population has been linearly related to the measured cosmic count rates.
We develop a kinetic reaction model for cells having irradiated DNA molecules due to ionizing radiation exposure. Our theory simultaneously accounts for the time-dependent reactions of the DNA damage, the DNA mutation, the DNA repair, and the proliferation and apoptosis of cells in a tissue with a minimal set of model parameters. In contrast to existing theories for radiation exposition, we do not assume the relationships between the total dose and the induced mutation frequency. Our theory provides a universal scaling function that reasonably explains the mega-mouse experiments in Ref.[W. L. Russell and E. M. Kelly, Proc. Natl. Acad. Sci. USA. {bf 79} (1982) 542.] with different dose rates. Furthermore, we have estimated the effective dose rate, which is biologically equivalent to the ionizing effects other than those caused by artificial irradiation. This value is $ 1.11 times 10^{-3} ~rm{[Gy/hr]}$, which is significantly larger than the effect caused by natural background radiation.
As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect. In this article, we investigate the possibility of denoising low dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets. Two cycle GANs were trained: 1) from paired data, by simulating low dose CTs (i.e., introducing noise) from high dose CTs; and 2) from unpaired real low dose CTs. To accelerate convergence, during GAN training, a slice-paired training strategy was introduced. The trained GANs were applied to three scenarios: 1) improving radiomics reproducibility in simulated low dose CT images and 2) same-day repeat low dose CTs (RIDER dataset) and 3) improving radiomics performance in survival prediction. Cycle GAN results were compared with a conditional GAN (CGAN) and an encoder-decoder network (EDN) trained on simulated paired data.The cycle GAN trained on simulated data improved concordance correlation coefficients (CCC) of radiomic features from 0.87 to 0.93 on simulated noise CT and from 0.89 to 0.92 on RIDER dataset, as well improving the AUC of survival prediction from 0.52 to 0.59. The cycle GAN trained on real data increased the CCCs of features in RIDER to 0.95 and the AUC of survival prediction to 0.58. The results show that cycle GANs trained on both simulated and real data can improve radiomics reproducibility and performance in low dose CT and achieve similar results compared to CGANs and EDNs.
Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (X_{L_c}) and latent texture features (X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF part. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with three encoders and decoders to obtain well-refined HF texture features. After that, we combine these well-refined HF texture features with the pre-extracted X_{L_c} to encourage the restoration of high-quality LDCT images with the assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT dataset show that our method produces superior results and outperforms other methods.