Purpose: This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness. Materials and methods: The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. We compared A-TOMO with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis, with a P-value < 0.05 indicating statistical significance. Results: A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+ < 0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V-50Gy, V-40Gy and D-mean for the bladder and rectum, as well as D-mean for the bowel bag, femoral heads, and kidneys (all P+ < 0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 min. Conclusion: We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.

This study aims to explore the measuring accuracy of an automated high-dose-rate brachytherapy (HDR BT) quality assurance system (hereinafter referred to as OriQA system) that could provide simultaneous measurements of dwell position, dwell time and source strength in a single test. Elekta-Flexitron was selected as the HDR BT afterloader, and measured the farthest transmission distance by source position simulator. Within the effective distance, we set dwell positions in two groups, specifically, plan_1 with an interval of 2.00 cm: 106.00 cm, 108.00 cm, 110.00 cm, 102.00 cm, 104.00 cm, 106.00 cm, 108.00 cm, and plan_2 with an interval of 0.20 cm: 111.40 cm, 111.60 cm, 111.80 cm, 112.00 cm, 112.20 cm, 112.40 cm, 112.60 cm. The measured values collected from OriQA device were compared with the data from traditional HDR BT QA tool (source position check ruler). Three groups of dwell time were set, specifically, plan_1: with fixed dwell time 2.00 s, plan_3 with interval 0.10 s: 3.70 s, 3.80 s, 3.90 s, 4.00 s, 4.10 s, 4.20 s, 4.30 s, and plan_4: fixed dwell time 5.00 s. Source strength was also measured continuously for a period of 10 weeks, and compared with the data collected from the well-chamber. The OriQA system showed reliable performance in the evaluation of measuring accuracy. The adjusted farthest transmission distance was 119.33 cm. And according to the time-position graph, the maximum deviation of dwell position between the measured and planned data was 0.34 mm, which was smaller than that between the ruler and planed data (0.75 mm). The maximum deviation of dwell time between the measured and planned data was 0.05 s. Fitting analysis revealed linear relationship between source strength Sk and the intensity detector response, with R2 = 0.9877, and intensity deviation fluctuated between 1.14% and 2.19%. Compared with the traditional QA tool, the OriQA device provided improved measuring accuracy and efficiency QA procedure for HDR BT.

Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative. A prototype SPI system was developed and demonstrated here in Cherenkov imaging of LINAC dose delivery to a water tank. Validation experiments were performed using four regular fields and an intensity-modulated radiotherapy (IMRT) delivery plan. The Cherenkov image-based projection percent depth dose curves (pPDDs) were compared to pPDDs simulated by the treatment planning system (TPS), with an overall average error of 0.48, 0.42, 0.65, and 1.08% for the 3, 5, 7, and 9 cm square beams, respectively. The composite image of the IMRT plan achieved a 85.9% pass rate using 3%/3 mm gamma index criteria, in comparing Cherenkov intensity and TPS dose. This study validates the feasibility of applying SPI to the Cherenkov imaging of radiotherapy dose for the first time to our knowledge.

Aquaporin 4 (AQP4) facilitates the transport of reactive oxygen species (ROS). Both cancer cells and the ionizing radiation microenvironment can induce posttranslational modifications (PTMs) in AQP4, which may affect its permeability to ROS. Because this ROS diffusion process is rapid, microscopic, and instantaneous within and outside cells, conventional experimental methods are inadequate for elucidating the molecular mechanisms involved. In this study, computational methods were employed to investigate the permeability of exogenous ROS mediated by radiation in AQP4 at a molecular scale. We constructed a simulation system incorporating AQP4 and AQP4-Cysp13 in a complex lipid environment with ROS. Long-timescale molecular dynamics simulations were conducted to assess the structural stability of both AQP4 and AQP4-Cysp13. Free energy calculations were utilized to determine the ROS transport capability of the two AQP4 proteins. Computational electrophysiology and channel structural analysis quantitatively evaluated changes in ROS transport capacity under various radiation-induced transmembrane voltage microenvironments. Our findings demonstrate the distinct transport capabilities of AQP4 channels for water molecules and various types of ROS and reveal a decrease in transport efficiency when AQP4 undergoes palmitoylation modification. In addition, we have simulated the radiation-induced alteration of cell membrane voltage, which significantly affected the ROS transport capacity. We propose that this research will enhance the understanding of the molecular mechanisms governing the transport of exogenous ROS by AQP4 and elucidate the influence of palmitoylation on ROS transport. This study will also help clarify how different structural features of AQP4 affect the transport of exogenous ROS mediated by radiotherapy, thereby providing a theoretical molecular basis for the development of new treatment strategies that combine with radiotherapy.

Introduction:Accurate delineation of tumor targets is crucial for stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC). This study aims to develop a deep learning-based segmentation approach to accurately and efficiently delineate NSCLC targets using diagnostic PET-CT and SBRT planning CT (pCT).Methods:The diagnostic PET was registered to pCT using the transform matrix from registering diagnostic CT to the pCT. We proposed a 3D-UNet-based segmentation method to segment NSCLC tumor targets on dual-modality PET-pCT images. This network contained squeeze-and-excitation and Residual blocks in each convolutional block to perform dynamic channel-wise feature recalibration. Furthermore, up-sampling paths were added to supplement low-resolution features to the model and also to compute the overall loss function. The dice similarity coefficient (DSC), precision, recall, and the average symmetric surface distances were used to assess the performance of the proposed approach on 86 pairs of diagnostic PET and pCT images. The proposed model using dual-modality images was compared with both conventional 3D-UNet architecture and single-modality image input.Results:The average DSC of the proposed model with both PET and pCT images was 0.844, compared to 0.795 and 0.827, when using 3D-UNet and nnUnet. It also outperformed using either pCT or PET alone with the same network, which had DSC of 0.823 and 0.732, respectively.Discussion:Therefore, our proposed segmentation approach is able to outperform the current 3D-UNet network with diagnostic PET and pCT images. The integration of two image modalities helps improve segmentation accuracy.

The M protein of the novel coronavirus 2019 (SARS-CoV-2) is the major structural component of the viral envelope and is also the minimum requirement for virus particle budding. M proteins generally exist as dimers. In virus assembly, they are the main driving force for envelope formation through lateral interactions and interactions with other viral structural proteins that play a central role. We built 100 candidate models and finally analyzed the six most convincing structural features of the SARS-CoV-2 M protein dimer based on long-timescale molecular dynamics (MD) simulations, multiple free energy analyses (potential mean force (PMF) and molecular mechanics Poisson-Boltzmann surface area (MMPBSA)) and principal component analysis (PCA) to obtain the most reasonable structure. The dimer stability was found to depend on the Leu-Ile zipper motif and aromatic amino acids in the transmembrane domain (TMD). Furthermore, the C-terminal domain (CTD) effects were relatively small. These results highlight a model in which there is sufficient binding affinity between the TMDs of M proteins to form dimers through the residues at the interface of the three transmembrane helices (TMHs). This study aims to help find more effective inhibitors of SARS-CoV-2 M dimers and to develop vaccines based on structural information.

Protein-lipid interactions are an essential element of the function of many membrane ion-channel proteins. These potential interactions should be considered alongside the diversity and complexity of membrane lipid composition. Phospholamban (PLN) is an inhibitor of sarcoplasmic reticulum Ca2+ ATPase (SERCA). PLN is a 52-residue transmembrane protein encoded by lncRNA, and PLN monomers form stable pentamers of biological function in a lipid bilayer membrane. Some earlier studies suggest that it can form a cationic selective channel, while others suggest that it can only store ions. Here, we report the distribution of different lipids in the membrane and the structural dynamics and conductance properties of PLN pentamers after coarse-grained (CG) and all-atom (AA) molecular dynamics simulations of different systems. The results show that cholesterol is highly enriched around the protein and stabilizes the structure of the PLN pentamer. The absence of cholesterol increases the flexibility of the protein backbone. The conductance properties of monovalent ions and water suggest that they cannot spontaneously permeate through the PLN pentamer channel pore. However, the energy barrier to overcome is much lower in the absence of cholesterol, underlining the need to fully consider multiple lipid species when investigating small transmembrane protein oligomer ion-channel structure and conductance.

Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy.Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (CTVcw), supra- and infra-clavicular region (CTVsc), and internal mammary lymphatic region (CTVim). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency.Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches.Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.

PURPOSE:Explore the longitudinal CT-based radiomics to demonstrate the changing trend of radiotherapy response and to determine at which point after the onset of treatment radiomics exhibit the greatest change for stage III NSCLC patients.METHODS AND MATERIALS:Ten stage III NSCLC patients in line with inclusion criteria were enrolled retrospectively, each of whom received radiotherapy or concurrent chemo-radiotherapy and performed eight series of follow-up CT imaging. Longitudinal radiomics were extracted on region of interest from the eight registered images, then two steps were conducted to select significant features as indicators of tumor change: 1) stable features were selected by Kendall rank correlation; 2) texture feature types with a steadily changing trend were retained and intensity features with stable change trends were selected to represent the large number of them. Next, the trend and rate of tumor change were analyzed using the Delta method and Curve-fitting method. Finally, the statistics in the distribution of stable features in patients were calculated.RESULTS:675 stable features were selected from a total number of 1371 radiomics features, then 12 texture features types were retained and three intensity features were chosen to represent their own category. Among the final selected feature types, it was found that the two time points were weeks 1 and 3 with the higher rate of change. One patient had very few stable tumor features out of a total of 101 features, and the rate of change of features of another patient was conspicuously higher than the average level with number of 301 features.CONCLUSION:The longitudinal CT radiomics could demonstrate the change trend of tumor and at which point exhibit the greatest change during radiotherapy, and potentially be used for treatment decisions concerning adaptive radiotherapy.

目的:研究一种基于CT图像层间插值的方法,用于放射治疗过程中的患者摆位验证,从而提高放疗精度.方法:采用一种基于3D卷积和膨胀卷积神经网络(3D CNN-DCNN)算法,利用相邻图像层之间的关联信息重建中间层图像.采用U-Net网络架构,通过编码部分的卷积层、膨胀卷积层、池化层和解码部分的上采样层、卷积层、膨胀卷积层,对CT进行端到端的学习.采集20例患者图像数据,采用留一交叉验证的方法训练验证模型,分别对神经网络和线性插值的预测CT与原始薄层CT进行对照比较.结果:3D CNN-DCNN的平均绝对误差(MAE)为34 HU,远小于线性插值的55 HU.除此之外,骨骼的Dice相似系数(DSC)为0.95,大于线性插值方法的0.89.结论:与传统线性插值方法相比,3D CNN-DCNN算法可以更准确的重建薄层CT,明显改善了插值伪影、图像失真和锯齿状现象.

Single-pixel imaging (SPI) enables the use of advanced detector technologies to provide a potentially low-cost solution for sensing beyond the visible spectrum and has received increasing attentions recently. However, when it comes to sub-Nyquist sampling, the spectrum truncation and spectrum discretization effects significantly challenge the traditional SPI pipeline due to the lack of sufficient sparsity. In this work, a deep compressive sensing (CS) framework is built to conduct image reconstructions in classical SPIs, where a novel compression network is proposed to enable collaborative sparsity in discretized feature space while remaining excellent coherence with the sensing basis as per CS conditions. To alleviate the underlying limitations in an end-to-end supervised training, for example, the network typically needs to be re-trained as the basis patterns, sampling ratios and so on. change, the network is trained in an unsupervised fashion with no sensing physics involved. Validation experiments are performed both numerically and physically by comparing with traditional and cutting-edge SPI reconstruction methods. Particularly, fluorescence imaging is pioneered to preliminarily examine the in vivo biodistributions. Results show that the proposed method maintains comparable image fidelity to a sCMOS camera even at a sampling ratio down to 4%, while remaining the advantages inherent in SPI. The proposed technique maintains the unsupervised and self-contained properties that highly facilitate the downstream applications in the field of compressive imaging.

As a useful tool, artificial intelligence has surpassed human beings in many fields. Artificial intelligence-based automated radiotherapy planning strategies have been proposed in lots of cancer sites and are the future of treatment planning. Postmastectomy radiotherapy (PMRT) decreases local recurrence probability and improves overall survival, and volumetric modulated arc therapy (VMAT) has gradually become the mainstream technique of radiotherapy. However, there are few customized effective automated treatment planning schemes for postmastectomy VMAT so far. This study investigated an artificial intelligence based automated planning using the MD Anderson Cancer Center AutoPlan (MDAP) system and Pinnacle treatment planning system (TPS), to effectively generate high-quality postmastectomy VMAT plans. In this study, 20 patients treated with PMRT were retrospectively investigated, including 10 left- and 10 right-sided postmastectomy patients. Chest wall and the supraclavicular, subclavicular, and internal mammary regions were delineated as target volume by radiation oncologists, and 50 Gy in 25 fractions was prescribed. Organs at risk including heart, spinal cord, left lung, right lung, and lungs were also contoured. All patients were planned with VMAT using 2 arcs. An optimization objective template was summarized based on the dose of clinical plans and requirements from oncologists. Several treatment planning parameters were investigated using an artificial intelligence algorithm, including collimation angle, jaw collimator mode, gantry spacing resolution (GSR), and number of start optimization times. The treatment planning parameters with the best performance or that were most preferred were applied to the automated treatment planning method. Dosimetric indexes of automated treatment plans (autoplans) and manual clinical plans were compared by the paired t-test. The jaw tracking mode, 2-degree GSR, and 3 rounds of optimization were selected in all the PMRT autoplans. Additionally, the 350- and 10-degree collimation angles were selected in the left- and right-sided PMRT autoplans, respectively. The uniformity index and conformity index of the planning target volume, mean heart dose, spinal cord D0.03cc, mean lung dose, and V5Gy and V20Gy of the lung of autoplans were significantly better compared with the manual clinical plans. An artificial intelligence-based automated treatment planning method for postmastectomy VMAT has been developed to ensure plan quality and improve clinical efficiency.

Glioma is a lethal malignant brain cancer, and many reports have shown that abnormalities in the behavior of water and ion channels play an important role in regulating tumor proliferation, migration, apoptosis, and differentiation. Recently, new studies have suggested that some long noncoding RNAs containing small open reading frames can encode small peptides and form oligomers for water or ion regulation. However, because the peptides are difficult to identify, their functional mechanisms are far from being clearly understood. In this study, we used bioinformatics methods to identify and evaluate lncRNAs, which may encode small transmembrane peptides in gliomas. Combining ab initio homology modeling, molecular dynamics simulations, and free energy calculations, we constructed a predictive model and predicted the oligomer channel activity of peptides by identifying the lncRNA ORFs. We found that one key hub lncRNA, namely, DLEU1, which contains two smORFs (ORF1 and ORF8), encodes small peptides that form pentameric channels. The mechanics of water and ion (Na+ and Cl-) transport through this pentameric channel were simulated. The potential mean force of the H2O molecules along the two ORF-encoded peptide channels indicated that the energy barrier was different between ORF1 and ORF8. The ORF1-encoded peptide pentamer acted as a self-assembled water channel but not as an ion channel, and the ORF8 permeated neither ions nor water. This work provides new methods and theoretical support for further elucidation of the function of lncRNA-encoded small peptides and their role in cancer. Additionally, this study provides a theoretical basis for drug development.

The COVID-19 epidemic is one of the most influential epidemics in history. Understanding the impact of coronaviruses (CoVs) on host cells is very important for disease treatment. The SARS-CoV-2 envelope (E) protein is a small structural protein involved in many aspects of the viral life cycle. The E protein promotes the packaging and reproduction of the virus, and deletion of this protein weakens or even abolishes the virulence. This review aims to establish new knowledge by combining recent advances in the study of the SARS-CoV-2 E protein and by comparing it with the SARS-CoV E protein. The E protein amino acid sequence, structure, self-assembly characteristics, viroporin mechanisms and inhibitors are summarized and analyzed herein. Although the mechanisms of the SARS-CoV-2 and SARS-CoV E proteins are similar in many respects, specific studies on the SARS-CoV-2 E protein, for both monomers and oligomers, are still lacking. A comprehensive understanding of this protein should prompt further studies on the design and characterization of effective targeted therapeutic measures.

Sarcolipin (SLN) is an important transmembrane (TM) protein encoded by long noncoding RNA. SLN is expressed in the sarcoplasmic reticulum and regulates cardiac and skeletal muscle contractions. SLN forms a pentameric hydrophobic ligand-gated ion channel. The protonation of Glu7 (protonated SLN, pSLN) and mutation of Thr18 to Ala18 (T18A) have been reported to exert a significant influence on the permeability of the channel. In this study, the altered permeability of both the pSLN and T18A pentameric channels was simulated. Combined with molecular dynamics simulation, the free-energy landscape for single ions, computational electrophysiology, diffusion coefficient, and pore geometrical characteristic analyses were performed to further understand the properties of amino acid modifications in the SLN pentameric channel. The results suggest that both the pSLN and T18A pentameric channels form stable hydrophobic ligand-gated channels. The TM voltage has a positive effect on the permeability of water molecules and ions. By using pSLN and T18A, our study provides helpful information on the pore-forming mechanism of SLN and furthers our understanding of the regulatory mechanisms underlying the permeation of ions and water molecules in the pentameric SLN channel.

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus (SARS-CoV-2) and represents the causative agent of a potentially fatal disease that is a public health emergency of international concern. Coronaviruses, including SARS-CoV-2, encode an envelope (E) protein, which is a small, hydrophobic membrane protein; the E protein of SARS-CoV-2 shares a high level of homology with severe acute respiratory syndrome coronavirus (SARS-CoV). In this study, we provide insights into the function of the SARS-CoV-2 E protein channel and the ion and water permeation mechanisms using a combination of in silico methods. Based on our results, the pentameric E protein promotes the penetration of cation ions through the channel. An analysis of the potential mean force (PMF), pore radius and diffusion coefficient reveals that Leu10 and Phe19 are the hydrophobic gates of the channel. In addition, the pore exhibits a clear wetting/dewetting transition with cation selectivity under transmembrane voltage, indicating that it is a hydrophobic voltage-dependent channel. Overall, these results provide structure-based insights and molecular dynamic information that are needed to understand the regulatory mechanisms of ion permeability in the pentameric SARS-CoV-2 E protein channel.