A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study.

BACKGROUND: Determining the optimal energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc therapy (PAT) technology. PURPOSE: This study aimed to explore the feasibility and potential clinical benefits of utilizing machine learning (ML) technique to automatically select EL for each field in PAT plans of lung cancer. METHODS: Proton Bragg peak position (BPP) was employed to characterize EL. The ground truth BPPs for each field were determined using the modified ELO-SPAT framework. Features in geometric, water-equivalent thicknesses (WET) and beamlet were defined and extracted. By analyzing the relationship between the extracted features and ground truth, a polynomial regression model with L2-norm regularization (Ridge regression) was constructed and trained. The performance of the regression model was reported as an error between the predictions and the ground truth. Besides, the predictions were used to make PAT plans (PAT_PRED). These plans were compared with those using the ground truth BPPs (PAT_TRUTH) and the mid-WET of the target volumes (PAT_MID) in terms of relative biological effectiveness-weighted dose (RWD) distributions. One hundred ten patients with lung cancer, a total of 7920 samples, were enrolled retrospectively, with 5940 cases randomly selected as the training set and the remaining 1980 cases as the testing set. Nine patients (648 samples) were collected additionally to evaluate the regression model in terms of plan quality and robustness. RESULTS: With regard to the prediction errors, the root mean squared errors and mean absolute errors between the ML-predicted and ground truth BPPs for the testing set were 9.165 and 6.572 mm, respectively, indicating differences of approximately two to three ELs. As for plan quality, the PAT_TRUTH and PAT_PRED plans performed similarly in terms of plan robustness, target coverage and organs at risk (OARs) protection, with differences smaller than 0.5 Gy(RBE). This trend was also observed for dose conformity and uniformity. The PAT_MID plans produced the lowest robustness index and lowest doses to OARs, along with the highest heterogeneity index, indicating better protection for OARs, improved plan robustness, but compromised dose homogeneity. Additionally, for relatively small tumor sizes, the PAT_MID plan demonstrated a notably poor dose conformity index. CONCLUSIONS: Within this cohort under investigation, our study demonstrated the feasibility of using ML technique to predict ELs for each field, offering a fast (within 2 s) and memory-efficient reduced way to select ELs for PAT plan.

Nanodosimetric quantity-weighted dose optimization for carbon-ion treatment planning.

Dose verification of treatment plans is an essential step in radiotherapy workflows. In this work, we propose a novel method of treatment planning based on nanodosimetric quantity-weighted dose (NQWD), which could realize biological representation using pure physical quantities for biological-oriented carbon ion-beam treatment plans and their direct verification. The relationship between nanodosimetric quantities and relative biological effectiveness (RBE) was studied with the linear least-squares method for carbon-ion radiation fields. Next, under the framework of the matRad treatment planning platform, NQWD was optimized using the existing RBE-weighted dose (RWD) optimization algorithm. The schemes of NQWD-based treatment planning were compared with the RWD treatment plans in term of the microdosimetric kinetic model (MKM). The results showed that the nanodosimetric quantity F3 - 10 had a good correlation with the radiobiological effect reflected by the relationship between RBE and F3 - 10. Moreover, the NQWD-based treatment plans reproduced the RWD plans generally. Therefore, F3 - 10 could be adopted as a radiation quality descriptor for carbon-ion treatment planning. The novel method proposed herein not only might be helpful for rapid physical verification of biological-oriented ion-beam treatment plans with the development of experimental nanodosimetry, but also makes the direct comparison of ion-beam treatment plans in different institutions possible. Thus, our proposed method might be potentially developed to be a new strategy for carbon-ion treatment planning and improve patient safety for carbon-ion radiotherapy.

Insights into the selectivity of a brain-penetrant CDK4/6 vs CDK1/2 inhibitor for glioblastoma used in multiple replica molecular dynamics simulations.

Cyclin dependent kinases (CDKs) play an important role in cell cycle regulation and their dysfunction is associated with many cancers. That is why CDKs have been attractive targets for the treatment of cancer. Glioblastoma is a cancer caused by the aberrant expression of CDK4/6, so exploring the mechanism of the selection of CDK4/6 toward inhibitors relative to the other family members CDK1/2 is essential. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), molecular mechanics Poisson-Boltzmann/Generalized Born surface area (MM-PB/GBSA) and other methods were integrated to decipher the selectively binding mechanism of the inhibitor N1J to CDK4/6 and CDK1/2. Molecular electrostatic potential (MESP) analysis provides an explanation for the N1J selectivity. Residue-based free energy decomposition reveals that most of the hot residues are located at the same location of CDKs proteins, but the different types of residues in different proteins cause changes in binding energy, which is considered as a potential developmental direction to improve the selectivity of inhibitors to CDK4/6. These results provide insights into the source of inhibitor and CDK4/6 selectivity for the future development of more selective inhibitors.Communicated by Ramaswamy H. Sarma.

Selection of breathing phase number in 4D scanned proton treatment planning optimization for lung tumors.

The standard four-dimensional (4D) treatment planning includes all breathing states in the optimization process, which is time-consuming. This work was aimed to optimize the number of intermediate phases needed for 4D proton treatment planning optimization to reduce the computational cost. Five 4D optimization strategies adopting different numbers of intermediate states and one three-dimensional (3D) optimization plan were studied for fifteen lung cancer patients treated with scanned protons, optimizing on all ten phases (4D_10), two extreme phases (4D_2), six phases during the exhalation stage (4D_6EX), six phases during the inhalation stage (4D_6IN), two extreme phases plus an intermediate state (4D_3) and average computed tomography image (3D), respectively. The 4D dose evaluation was conducted on all the ten phases, considering the interplay effect. The resulting doses accumulated on the reference phase were computed and compared. Compared to the 4D optimization plans, the 3D optimization plan performed inferiorly in target coverage, but superiorly in organ at risks (OARs) sparing. For the 4D optimization, all the five 4D plans showed similar performance in OARs protection. However, the 4D_6EX and 4D_6IN strategies out-performed the 4D_2 and 4D_3 plans in dose homogeneity. The computing times of the 4D_2, 4D_3, 4D_6EX and 4D_6IN approaches decreased to 32%, 41%, 66% and 67% of the 4D_10 method, respectively. Thus, our study suggested that the use of all phases during inhalation or exhalation stage might be a feasible approach substituting for the full phase strategy to reduce the calculation load while guaranteeing the plan quality for scanned proton therapy.

Molecular insights and optimization strategies for the competitive binding of engineered ACE2 proteins: a multiple replica molecular dynamics study.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally, and rapid viral evolution and the emergence of new variants pose challenges to pandemic control. During infection, the spike protein of SARS-CoV-2 interacts with the human ACE2 protein via its receptor binding domain (RBD), and it is known that engineered forms of ACE2 can compete with wild-type (WT) ACE2 for binding to inhibit infection. Here, we conducted multiple replica molecular dynamics (MRMD) simulations to study the mechanisms of the engineered ACE2 variants 3N39 and 3N94 and provide directions for optimization. Our findings reveal that engineered ACE2 is notably more efficacious in systems that show weaker binding to WT ACE2 (i.e., WT and BA.1 RBD), but also faces immune escape as the virus evolves. Moreover, by modifying residue types near the binding interface, engineered ACE2 alters the electrostatic potential distribution and reconfigures the hydrogen bonding network, which results in modified binding to the RBD. However, this structural rearrangement does not occur in all RBD variants. In addition, we identified potentially engineerable beneficial residues and potentially engineerable detrimental residues in both ACE2 and RBD. Functional conservation can thus enable the optimization of these residues and improve the binding competitiveness of engineered ACE2, which therefore provides additional immune escape prevention. Finally, we conclude that these findings have implications for understanding the mechanisms responsible for engineered ACE2 and can help us to develop engineered ACE2 proteins that show superior performance.

Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy.

BACKGROUND: Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus. PURPOSE: The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification. METHODS: Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%). RESULTS: The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results. CONCLUSION: The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.

Ferroptosis: a novel regulated cell death participating in cellular stress response, radiotherapy, and immunotherapy.

BACKGROUND: Ferroptosis is a regulated cell death mode triggered by iron-dependent toxic membrane lipid peroxidation. As a novel cell death modality that is morphologically and mechanistically different from other forms of cell death, such as apoptosis and necrosis, ferroptosis has attracted extensive attention due to its association with various diseases. Evidence on ferroptosis as a potential therapeutic strategy has accumulated with the rapid growth of research on targeting ferroptosis for tumor suppression in recent years. METHODS: We summarize the currently known characteristics and major regulatory mechanisms of ferroptosis and present the role of ferroptosis in cellular stress responses, including ER stress and autophagy. Furthermore, we elucidate the potential applications of ferroptosis in radiotherapy and immunotherapy, which will be beneficial in exploring new strategies for clinical tumor treatment. RESULT AND CONCLUSION: Based on specific biomarkers and precise patient-specific assessment, targeting ferroptosis has great potential to be translated into practical new approaches for clinical cancer therapy, significantly contributing to the prevention, diagnosis, prognosis, and treatment of cancer.

Deep learning-based internal gross target volume definition in 4D CT images of lung cancer patients.

BACKGROUND: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability. So far, few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV. PURPOSE: This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers. METHODS: Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multi-channel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively. RESULTS: In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth. CONCLUSION: Our study demonstrated the ability of the deep learning technique to predict iGTVs directly. With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved.

Deciphering the binding mechanism of inhibitors of the SARS-CoV-2 main protease through multiple replica accelerated molecular dynamics simulations and free energy landscapes.

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. The development of safe and highly effective antiviral drugs is of great significance for the treatment of COVID-19. The main protease (Mpro) of SARS-CoV-2 is a key enzyme for viral replication and transcription and has no homolog in humans. Therefore, the Mpro is an ideal target for the design of drugs against COVID-19. Insights into the inhibitor-Mpro binding mechanism and conformational changes of the Mpro are essential for the design of potent drugs that target the Mpro. In this study, we analyzed the conformational changes of the Mpro that are induced by the binding of three inhibitors, YTV, YSP and YU4, using multiple replica accelerated molecular dynamics (MR-aMD) simulations, dynamic cross-correlation map (DCCM) calculations, principal component analysis (PCA), and free energy landscape (FEL) analysis. The results from DCCM calculations and PCA show that the binding of inhibitors significantly affects the kinetic behavior of the Mpro and induces a conformational rearrangement of the Mpro. The binding ability and binding mechanism of YTV, YSP and YU4 to the Mpro were investigated using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The results indicate that substitution of the tert-butanol group by methylbenzene and trifluoromethyl groups enhances the binding ability of YSP and YU4 to the Mpro compared with YTV; moreover, massive hydrophobic interactions are detected between the inhibitors and the Mpro. Meanwhile, T25, L27, H41, M49, N142, G143, C145, M165, E166 and Q189 are identified as the key residues for inhibitor-Mpro interactions using residue-based free energy decomposition calculations, which can be employed as efficient targets in the design of drugs that inhibit the activity of the Mpro.

DR-only Carbon-ion radiotherapy treatment planning via deep learning.

PURPOSE: To evaluate the feasibility of patient-specific digital radiography (DR)-only treatment planning for carbon ion radiotherapy in anthropomorphic thorax-and-abdomen phantom and head-and-neck patients. METHODS: The study was conducted on the anthropomorphic phantom and head-and-neck patients. We collected computed tomography (CT) and DR images of the phantom and cone beam CT (CBCT) and DR images of the patients, respectively. Two different deep neural networks were established to correlate the relationships between DR and digitally reconstructed radiograph (DRR) images, as well as DRR and CT images. The similarity between CT and predicted CT images was evaluated by computing the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Dose calculations on the predicted CT images were compared against the true CT-based dose distributions for carbon-ion radiotherapy treatment planning with intensity-modulated pencil-beam spot scanning. Relative dose differences in the target volumes and organ-at-risks were computed and three-dimensional gamma analyses (3 mm, 3%) were performed. RESULTS: The average MAE, RMSE, PSNR and SSIM of the framework were 0.007, 0.144, 37.496 and 0.973, respectively. The average relative dose differences between the predicted CT- and CT-based dose distributions at the same carbon-ion irradiation settings for the phantom and the patients were <2% and ≤4%, respectively. The average gamma pass-rates were >98% for the predicted CT-based versus CT-based carbon ion plans of the phantom and the patients. CONCLUSION: We have demonstrated the feasibility of a patient-specific DR-only treatment planning workflow for heavy ion radiotherapy by using deep learning approach.

Towards an uncertainty evaluation model for marine track considering local sampling cloud and sample information.

To evaluate the practical ability of crews during the navigation of an inward-port single ship, a track evaluation model was developed on a planar forward normal cloud chart under sample information based on the forward normal and the backward normal cloud generator. Since the track sampling cloud may be too divergent, a track belt division method based on the contributions of normal cloud drops was proposed. Combining the track evaluation model with the track belt division method, a comprehensive track evaluation scheme of the local sampling cloud based on sampling information was established. The results of an example of M.V. DAQING 257 unloaded into Dalian Port demonstrated the effectiveness of the model and showed its consistency with expert evaluation results based on subjective information. The proposed uncertainty evaluation model provides a new approach for intelligent evaluation under sample information.

Validation and testing of a novel pencil-beam model derived from Monte Carlo simulations in carbon-ion treatment planning for different scenarios.

PURPOSE: The calculation ability of the newly-proposed accurate beam model, the double Gaussian-logistic (DG-L) model, was validated in both homogeneous and heterogeneous phantoms to provide helpful information for its future application in clinical carbon-ion treatment planning system (TPS). METHODS: MatRad was used as the new algorithm test platform. Based on Monte Carlo (MC) method, the basic database in matRad was generated, then comparative dosimetric analyses between the single Gaussian (SG), double Gaussian (DG) and DG-L models against the MC recalculations were performed on the treatment plans of a cubic water phantom, a TG119 phantom and a liver patient scenario. Absolute dose differences, dose-volume histograms (DVHs) and global γ-index analyses derived from the treatment plans were evaluated. RESULTS: Calculated with the DG-L model, the deviations of the target dose coverage (D95) for the cubic water phantom, the TG119 phantom and the liver patient case against the MC recalculations could be reduced from -2.5%, -4.6% and -6.4% to -0.3%, -2.0% and -4.5% respectively compared to the SG model, while the γ pass rates (3%/3mm) could be enhanced from 98.0%, 90.6% and 90.1% to 99.8%, 95.7% and 91.6%, respectively. The novel beam model also shows improved performance compared with the DG model, without substantially increasing the computation time. CONCLUSIONS: The DG-L model could effectively improve the dose calculation accuracy and mitigate the delivered dose deficiency in target volumes compared to the SG and DG models. The lateral heterogeneities should be considered for its future implementation in a clinical TPS.

Free Energy Profiles Relating With Conformational Transition of the Switch Domains Induced by G12 Mutations in GTP-Bound KRAS.

Mutations of G12 in KRAS have been involved in different cancers. Multiple replica-Gaussian accelerated molecular dynamics (MR-GaMD) simulations are applied to investigate conformational changes of the switch domains caused by G12C, G12D and G12R. Free energy landscapes suggest that G12C, G12D and G12R induce more energetic states compared to the GTP-bound WT KRAS and make the conformations of the switch domains more disordered, which disturbs bindings of KRAS to effectors. Dynamics analyses based on MR-GaMD trajectory show that G12C, G12D and G12R not only change structural flexibility of the switch domains but also affect their motion behavior, indicating that these three mutations can be used to tune the activity of KRAS. The analyses of interaction networks verify that the instability in interactions of the GTP with the switch SⅠ plays an important role in the high disorder states of the switch domain. This work is expected to provide useful information for deeply understanding the function of KRAS.

Binding mechanism of inhibitors to SARS-CoV-2 main protease deciphered by multiple replica molecular dynamics simulations.

The outbreak caused by SARS-CoV-2 has received extensive worldwide attention. As the main protease (Mpro) in SARS-CoV-2 has no human homologues, it is feasible to reduce the possibility of targeting the host protein by accidental drugs. Thus, Mpro has been an attractive target of efficient drug design for anti-SARS-CoV-2 treatment. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), and the molecular mechanics-generalized Born surface area (MM-GBSA) method were integrated together to decipher the binding mechanism of four inhibitors masitinib, O6K, FJC and GQU to Mpro. The results indicate that the binding of four inhibitors clearly affects the structural flexibility and internal dynamics of Mpro along with dihedral angle changes of key residues. The analysis of FELs unveils that the stability in the relative orientation and geometric position of inhibitors to Mpro is favorable for inhibitor binding. Residue-based free energy decomposition reveals that the inhibitor-Mpro interaction networks involving hydrogen bonding interactions and hydrophobic interactions provide significant information for the design of potent inhibitors against Mpro. The hot spot residues including H41, M49, F140, N142, G143, C145, H163, H164, M165, E166 and Q189 identified by computational alanine scanning are considered as reliable targets of clinically available inhibitors inhibiting the activities of Mpro.

Acupuncture versus Lornoxicam in the Treatment of Acute Renal Colic: A Randomized Controlled Trial.

OBJECTIVE: To compare the analgesic efficacy and safety of acupuncture and lornoxicam in acute renal colic (ARC). DESIGN SETTING PARTICIPANT: A randomized, double-blind, parallel-controlled, single-centered trial was conducted at Susong County People's Hospital from October 2019 to November 2020. Eighty-four patients with ARC were randomly divided into lornoxicam group (Group L) and acupuncture group (Group A). Group A was treated with acupuncture at Sanyinjiao (SP6), Yinlingquan (SP9) and normal saline, and Group L was treated with sham acupuncture at SP6, SP9 and lornoxicam. MAIN OUTCOME MEASURES: Visual analogue scale (VAS) scores and adverse reactions such as nausea and dizziness were recorded within 5, 10, 15, 20 and 40 minutes after treatment. The main outcome of this study was the short-term effective (STE) rate, the secondary outcome was the onset time, and the safety index was incidence of adverse reactions. RESULTS: A total of 80 patients completed this study, including 41 patients (21 males and 20 females) in Group L and 39 patients (21 males and 18 females) in Group A. Group A exhibited lower scores versus group L after treatment (P < 0.05). The overall STE of group L was 61.00% (25/41), significantly lower than group A [84.62% (33/39)] (P < 0.001). There was no difference in the incidence of adverse reactions between group A [2.6% (1/39)] and group L [7.3% (3/41)] (P = 0.616). The ordered logistic regression analysis showed patients receiving acupuncture therapy are more likely to be cured [OR = 2.887, 95% CI: (1.190, 7.000), P = 0.019]. CONCLUSION: Acupuncture at SP6, SP9 and intramuscular injection of lornoxicam can effectively and safely relieve ARC, but the former has faster and better analgesic effect. Moreover, the incidence of adverse reactions was similar between the two treatments. This acupuncture therapy is recommended as a complementary therapy for ARC.

Carbon Ion Radiotherapy Acts as the Optimal Treatment Strategy for Unresectable Liver Cancer During the Coronavirus Disease 2019 Crisis.

The coronavirus disease 2019 (COVID-19) pandemic has greatly disrupted the normal treatment of patients with liver cancer and increased their risk of death. The weight of therapeutic safety was significantly amplified for decision-making to minimize the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Herein, the safety and effectiveness of carbon ion radiotherapy (CIRT) for unresectable liver cancer (ULC) were evaluated, and Chinese experiences were shared to solve the predicament of ULC treatment caused by SARS-CoV-2. Worldwide studies were collected to evaluate CIRT for ULC as the world has become a community due to the COVID-19 pandemic. We not only searched five international databases including the Cochrane Library, Web of Science, PubMed, Embase, and Scopus but also performed supplementary retrieval with other sources. Chinese experiences of fighting against COVID-19 were introduced based on the advancements of CIRT in China and a prospective clinical trial of CIRT for treating ULC. A total of 19 studies involving 813 patients with ULC were included in the systematic review. The qualitative synthetic evaluation showed that compared with transarterial chemoembolization (TACE), CIRT could achieve superior overall survival, local control, and relative hepatic protection. The systematic results indicated that non-invasive CIRT could significantly minimize harms to patients with ULC and concurrently obtain superior anti-cancer effectiveness. According to the Chinese experience, CIRT allows telemedicine within the hospital (TMIH) to keep a sufficient person-to-person physical distance in the whole process of treatment for ULC, which is significant for cutting off the transmission route of SARS-CoV-2. Additionally, CIRT could maximize the utilization rate of hospitalization and outpatient care (UHO). Collectively, CIRT for ULC patients not only allows TMIH and the maximized UHO but also has the compatible advantages of safety and effectiveness. Therefore, CIRT should be identified as the optimal strategy for treating appropriate ULC when we need to minimize the risk of SARS-CoV-2 infection and to improve the capacity of medical service in the context of the unprecedented COVID-19 crisis.

Hyperthermia ablation combined with transarterial chemoembolization versus monotherapy for hepatocellular carcinoma: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The existing evidence has indicated that hyperthermia ablation (HA) and HA combined with transarterial chemoembolization (HATACE) are the optimal alternative to surgical resection for patients with hepatocellular carcinoma (HCC) in the COVID-19 crisis. However, the evidence for decision-making is lacking in terms of comparison between HA and HATACE. Herein, a comprehensive evaluation was performed to compare the efficacy and safety of HATACE with monotherapy. MATERIALS AND METHODS: Worldwide studies were collected to evaluate the HATACE regimen for HCC due to the practical need for global extrapolation of applicative population. Meta-analyses were performed using the RevMan 5.3 software (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark). RESULTS: Thirty-six studies involving a large sample of 5036 patients were included finally. Compared with HA alone, HATACE produced the advantage of 5-year overall survival (OS) rate (OR:1.90; 95%CI:1.46,2.46; p < 0.05) without increasing toxicity (p ≥ 0.05). Compared with TACE alone, HATACE was associated with superior 5-year OS rate (OR:3.54; 95%CI:1.96,6.37; p < 0.05) and significantly reduced the incidences of severe liver damage (OR:0.32; 95%CI:0.11,0.96; p < 0.05) and ascites (OR:0.42; 95%CI:0.20,0.88; p < 0.05). Subgroup analysis results of small (≤3 cm) HCC revealed that there were no significant differences between the HATACE group and HA monotherapy group in regard to the OS rates (p ≥ 0.05). CONCLUSIONS: Compared with TACE alone, HATACE was more effective and safe for HCC. Compared with HA alone, HATACE was more effective for non-small-sized (>3 cm) HCC with comparable safety. However, the survival benefit of adjuvant TACE in HATACE regimen was not found for the patients with small (≤3 cm) HCC.

Binding of Inhibitors to BACE1 Affected by pH-Dependent Protonation: An Exploration from Multiple Replica Gaussian Accelerated Molecular Dynamics and MM-GBSA Calculations.

To date, inhibiting the activity of ß-amyloid cleaving enzyme 1 (BACE1) has been considered an efficient approach for treating Alzheimer's disease (AD). In the current work, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics general Born surface area (MM-GBSA) method were combined to investigate the effect of pH-dependent protonation on the binding of the inhibitors CS9, C6U, and 6WE to BACE1. Dynamic analyses based on the MR-GaMD trajectory show that pH-dependent protonation strongly affects the structural flexibility, correlated motions, and dynamic behavior of inhibitor-bound BACE1. According to the constructed free energy profiles, in the protonated state at low pH, inhibitor-bound BACE1 tends to populate at more conformations than in high pH. The binding free energies calculated by MM-GBSA suggest that inhibitors possess stronger binding abilities under the protonation conditions at high pH than under the protonation conditions at low pH. Moreover, pH-dependent protonation exerts a significant effect on the hydrogen bonding interactions of CS9, C6U, and 6WE to BACE1, which correspondingly alters the binding abilities of the three inhibitors to BACE1. Furthermore, in different protonated environments, three inhibitors share common interaction clusters and similar binding sites in BACE1, which are reliably used as efficient targets for the design of potent inhibitors of BACE1.

The influence of beam delivery uncertainty on dose uniformity and penumbra for pencil beam scanning in carbon-ion radiotherapy.

The dose uniformity and penumbra in the treatment field are important factors in radiotherapy, which affects the outcomes of radiotherapy. In this study, the integrated depth-dose-distributions (IDDDs) of 190 MeV/u and 260 MeV/u carbon beams in the active spot-scanning delivery system were measured and calculated by FLUKA Monte Carlo simulation based on the Heavy Ion Medical Machine (HIMM). Considering the dose distributions caused by secondary particles and scattering, we also used different types of pencil beam (PB) models to fit and compare the spatial distributions of PB. We superposed a bunch of PB to form a 20×20 cm2 treatment field with the double Gaussian and double Gaussian logistic beam models and calculated the influence of beam delivery error on the field flatness and penumbra, respectively. The simulated IDDDs showed good agreement with the measured values. The triple Gaussian and double Gaussian logistic beam models have good fitness to the simulated dose distributions. There are different influences on dose uniformity and penumbra resulting from beam uncertainties. These results would be helpful for understanding carbon ion therapy, and physical therapists are more familiar with beam characteristics for active scanning therapy, which provides a reference for commissioning and optimization of treatment plans in radiotherapy.

Mutation-Induced Impacts on the Switch Transformations of the GDP- and GTP-Bound K-Ras: Insights from Multiple Replica Gaussian Accelerated Molecular Dynamics and Free Energy Analysis.

Mutations yield significant effect on the structural flexibility of two switch domains, SW1 and SW2, in K-Ras, which is considered as an important target of anticancer drug design. To unveil a molecular mechanism with regard to mutation-mediated tuning on the activity of K-Ras, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations followed by analysis of free energy landscapes (FELs) are performed on the GDP- and GTP-bound wild-type (WT), G12V, and D33E K-Ras. The results suggest that G12V and D33E not only evidently change the flexibility of SW1 and SW2 but also greatly affect correlated motions of SW1 and SW2 separately relative to the P-loop and SW1, which exerts a certain tuning on the activity of K-Ras. The information stemming from the analyses of FELs reveals that the conformations of SW1 and SW2 are in high disorders in the GDP- and GTP-associated WT and mutated K-Ras, possibly producing significant effect on binding of guanine nucleotide exchange factors or effectors to K-Ras. The interaction networks of GDP and GTP with K-Ras are identified and the results uncover that the instability in hydrogen-bonding interactions of SW1 with GDP and GTP is mostly responsible for conformational disorder of SW1 and SW2 as well as tunes the activity of oncogenic K-Ras.