Prompt gamma ray (PG) imaging based on Compton camera (CC) is promising to realize in vivo verification during the proton therapy. However, the finite spatial and energy resolution of current CC, as well as the Doppler broaden effect, degrade the quality and resolution of PG images. In addition, due to the inherent geometrical complexity of Compton camera data, PG imaging can be time-consuming and difficult to reconstruct in real-time, while using standard techniques such as filtered back-projection or maximum likelihood-expectation maximization. In this paper, we propose three modifications of origin ensembles with resolution recovery (OE-RR) algorithm based on Markov chains to accelerate the convergence to equilibrium of OE-RR algorithm and improve the image quality. For evaluation, we performed a Monte Carlo simulation of a three-stage CZT Compton camera with resolution loss to detect the PG produced by a proton beam in a water phantom, and evaluate image quality of the gamma rays emitted during proton irradiation. The results show that our ordered OE-RR algorithm realized a good resolution recovery and accurate estimation of the position, including the peak and the distal falloff of the PG emission with remarkably faster reconstruction, thus demonstrating the feasibility of this new method in non-idealized PG-based proton range verification.

Traumatic brain injury (TBI) can induce neuronal apoptosis and neuroinflammation, resulting in substantial neuronal damage and behavioral disorders. Fibroblast growth factors (FGFs) have been shown to be critical mediators in tissue repair. However, the role of FGF10 in experimental TBI remains unknown. In this study, mice with TBI were established via weight-loss model and validated by increase of modified neurological severity scores (mNSS) and brain water content. Secondly, FGF10 levels were elevated in mice after TBI, whereas intraventricular injection of Ad-FGF10 decreased mNSS score and brain water content, indicating the remittance of neurological deficit and cerebral edema in TBI mice. In addition, neuronal damage could also be ameliorated by stereotactic injection of Ad-FGF10. Overexpression of FGF10 increased protein expression of Bcl-2, while it decreased Bax and cleaved caspase-3/PARP, and improved neuronal apoptosis in TBI mice. In addition, Ad-FGF10 relieved neuroinflammation induced by TBI and significantly reduced the level of interleukin 1β/6, tumor necrosis factor α, and monocyte chemoattractant protein-1. Moreover, Ad-FGF10 injection decreased the protein expression level of Toll-like receptor 4 (TLR4), MyD88, and phosphorylation of NF-κB (p-NF-κB), suggesting the inactivation of the TLR4/MyD88/NF-κB pathway. In conclusion, overexpression of FGF10 could ameliorate neurological deficit, neuronal apoptosis, and neuroinflammation through inhibition of the TLR4/MyD88/NF-κB pathway, providing a potential therapeutic strategy for brain injury in the future.

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Airplane engines are vital aircraft components, so regular inspections of the engines are required to ensure their stable operation. A dynamic computed tomography (CT) system has been proposed by our group for in situ nondestructive testing of airplane engines, which takes advantage of the rotor's self-rotation. However, static parts of the engines cause blocked artifacts in the reconstructed image, leading to misinterpretations of the condition of engines. In this paper, in order to remove the artifacts produced by the projection of the static parts in CT reconstruction, two deep-learning-based methods are proposed, which use U-Net to perform correction in the projection domain. The projection of static parts can be estimated by a well-trained U-Net and subsequently can be subtracted from the projections of the engine. Finally, the rotor can be reconstructed from the corrected projections. The results shown in this paper indicate that the proposed methods are practical and effective for removing those blocked artifacts and recovering the details of rotating parts, which will, in turn, maximize the utilization of the dynamic CT system for in situ engine tests.

EGFR G724S mutation in exon 18 has been shown to be resistant to both first- and third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). However, we found a rare mutation of EGFR Ex19del/G724S in two patients with lung cancer who demonstrated a favorable response to the combination of afatinib and chemotherapy. Identified by next-generation sequencing (NGS), EGFR G724S was found from a primary and a secondary tumor biopsy, respectively. Treated with afatinib combined with chemotherapy, both patients responded well and achieved progression-free survival. Analysis of acquired mutations developed during treatment using afatinib revealed that the emergence of EGFR T790M or ALK fusion was the potential mechanism of afatinib resistance. Our study lends credence to treatment using afatinib combined with chemotherapy as a viable option for patients with Ex19del/G724S.