Brain endothelial GSDMD activation mediates inflammatory BBB breakdown.

The blood-brain barrier (BBB) protects the central nervous system from infections or harmful substances1; its impairment can lead to or exacerbate various diseases of the central nervous system2-4. However, the mechanisms of BBB disruption during infection and inflammatory conditions5,6 remain poorly defined. Here we find that activation of the pore-forming protein GSDMD by the cytosolic lipopolysaccharide (LPS) sensor caspase-11 (refs. 7-9), but not by TLR4-induced cytokines, mediates BBB breakdown in response to circulating LPS or during LPS-induced sepsis. Mice deficient in the LBP-CD14 LPS transfer and internalization pathway10-12 resist BBB disruption. Single-cell RNA-sequencing analysis reveals that brain endothelial cells (bECs), which express high levels of GSDMD, have a prominent response to circulating LPS. LPS acting on bECs primes Casp11 and Cd14 expression and induces GSDMD-mediated plasma membrane permeabilization and pyroptosis in vitro and in mice. Electron microscopy shows that this features ultrastructural changes in the disrupted BBB, including pyroptotic endothelia, abnormal appearance of tight junctions and vasculature detachment from the basement membrane. Comprehensive mouse genetic analyses, combined with a bEC-targeting adeno-associated virus system, establish that GSDMD activation in bECs underlies BBB disruption by LPS. Delivery of active GSDMD into bECs bypasses LPS stimulation and opens the BBB. In CASP4-humanized mice, Gram-negative Klebsiella pneumoniae infection disrupts the BBB; this is blocked by expression of a GSDMD-neutralizing nanobody in bECs. Our findings outline a mechanism for inflammatory BBB breakdown, and suggest potential therapies for diseases of the central nervous system associated with BBB impairment.

Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma.

Purpose: Pathologic complete response (pCR) is a critical factor in determining whether patients with rectal cancer (RC) should have surgery after neoadjuvant chemoradiotherapy (nCRT). Currently, a pathologist's histological analysis of surgical specimens is necessary for a reliable assessment of pCR. Machine learning (ML) algorithms have the potential to be a non-invasive way for identifying appropriate candidates for non-operative therapy. However, these ML models' interpretability remains challenging. We propose using explainable boosting machine (EBM) to predict the pCR of RC patients following nCRT. Methods: A total of 296 features were extracted, including clinical parameters (CPs), dose-volume histogram (DVH) parameters from gross tumor volume (GTV) and organs-at-risk, and radiomics (R) and dosiomics (D) features from GTV. R and D features were subcategorized into shape (S), first-order (L1), second-order (L2), and higher-order (L3) local texture features. Multi-view analysis was employed to determine the best set of input feature categories. Boruta was used to select all-relevant features for each input dataset. ML models were trained on 180 cases from our institution, with 37 cases from RTOG 0822 clinical trial serving as the independent dataset for model validation. The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations. Results: The best input feature categories were CP+DVH+S+R_L1+R_L2 for all models, from which Boruta-selected features enabled the EBM, XGB, RF, and SVM models to attain the AUCs of 0.820, 0.828, 0.828, and 0.774, respectively. Although EBM did not achieve the best performance, it provided the best capability for identifying critical turning points in response scores at distinct feature values, revealing that the bladder with maximum dose >50 Gy, and the tumor with maximum2DDiameterColumn >80 mm, elongation <0.55, leastAxisLength >50 mm and lower variance of CT intensities were associated with unfavorable outcomes. Conclusions: EBM has the potential to enhance the physician's ability to evaluate an ML-based prediction of pCR and has implications for selecting patients for a "watchful waiting" strategy to RC therapy.

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.

BACKGROUND AND PURPOSE: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. METHODS: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. RESULTS: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. CONCLUSIONS: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

Chronic stress accelerates the process of gastric precancerous lesions in rats.

Background: Gastrointestinal cancers account for 20% of all deaths worldwide. Gastric cancer (GC) patients are susceptible to psychological change, especially depression which is commonly induced by chronic stress. Gastric precancerous lesions (GPL) is an important prodromal stage in the occurrence of gastric cancer. Chronic stress influences the prognosis of GC and may influence the process of GPL as well. Methods: Sixty SD rats were randomly divided into a control group, GPL group, and GPL+CUMS group. In the GPL group, 200µg/mL N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) free drinking method combined with intermittent fasting was applied to establish the GPL animal model. Based on this, we combined the GPL rats with chronic unpredicted mild stress (CUMS) to establish a comprehensive model. We then evaluated their behavior by open field tests and sucrose preference tests. We tested the IL-6, IL-10, TNF-α, Ghrelin, Leptin and Somatostatin (SS) levels in serum and observed the expression of Ghrelin and Gastrokine 2(GKN2) in the gastric mucosa of rats with tumors by immunofluorescence. Results: Our results showed that GPL and GPL+CUMS rats all displayed a significantly decreased total distance and mean velocity traveled in the open field test. The percentages of sucrose preference were significantly decreased in the GPL+CUMS group compared to the control group. In addition, IL-6 and TNF-α were significantly increased in both the GPL and GPL+CUMS groups. Furthermore, the GPL+CUMS group showed significantly increased TNF-α levels in serum compared to the GPL rats. Our results showed that the expression of NF-κB, p53, and BCL-2 were significantly increased while BAX was reduced in the GPL and GPL+CUMS groups. Moreover, Ghrelin and Leptin levels in serum were significantly decreased in the GPL and GPL+CUMS groups. SS levels in serum were significantly increased in the GPL+CUMS group. Additionally, we found that the GPL+CUMS rats with tumors not only had strong expression of GKN2 on the luminal side and the lamina propria of the gastric mucosa and tumor, but also had expression of Ghrelin on the luminal side of the gastric mucosa. The areas that showed strong expression of GKN2 and Ghrelin, are all located around the blood vessels in the tumor. Conclusions: GPL rats under chronic stress would aggravate the conditions of GPL, shorten the process of GPL, and increase the risk of tumorigenesis. In addition, the close monitoring of the mental health of cancer survivors and precancerous lesion patients is suggested to be of great significance in the prevention and treatment of cancer.

Comparative Effectiveness of Proton vs Photon Therapy as Part of Concurrent Chemoradiotherapy for Locally Advanced Cancer.

Importance: Concurrent chemoradiotherapy is the standard-of-care curative treatment for many cancers but is associated with substantial morbidity. Concurrent chemoradiotherapy administered with proton therapy might reduce toxicity and achieve comparable cancer control outcomes compared with conventional photon radiotherapy by reducing the radiation dose to normal tissues. Objective: To assess whether proton therapy in the setting of concurrent chemoradiotherapy is associated with fewer 90-day unplanned hospitalizations (Common Terminology Criteria for Adverse Events, version 4 [CTCAEv4], grade ≥3) or other adverse events and similar disease-free and overall survival compared with concurrent photon therapy and chemoradiotherapy. Design, Setting, and Participants: This retrospective, nonrandomized comparative effectiveness study included 1483 adult patients with nonmetastatic, locally advanced cancer treated with concurrent chemoradiotherapy with curative intent from January 1, 2011, through December 31, 2016, at a large academic health system. Three hundred ninety-one patients received proton therapy and 1092, photon therapy. Data were analyzed from October 15, 2018, through February 1, 2019. Interventions: Proton vs photon chemoradiotherapy. Main Outcomes and Measures: The primary end point was 90-day adverse events associated with unplanned hospitalizations (CTCAEv4 grade ≥3). Secondary end points included Eastern Cooperative Oncology Group (ECOG) performance status decline during treatment, 90-day adverse events of at least CTCAEv4 grade 2 that limit instrumental activities of daily living, and disease-free and overall survival. Data on adverse events and survival were gathered prospectively. Modified Poisson regression models with inverse propensity score weighting were used to model adverse event outcomes, and Cox proportional hazards regression models with weighting were used for survival outcomes. Propensity scores were estimated using an ensemble machine-learning approach. Results: Among the 1483 patients included in the analysis (935 men [63.0%]; median age, 62 [range, 18-93] years), those receiving proton therapy were significantly older (median age, 66 [range, 18-93] vs 61 [range, 19-91] years; P < .01), had less favorable Charlson-Deyo comorbidity scores (median, 3.0 vs 2.0; P < .01), and had lower integral radiation dose to tissues outside the target (mean [SD] volume, 14.1 [6.4] vs 19.1 [10.6] cGy/cc × 107; P < .01). Baseline grade ≥2 toxicity (22% vs 24%; P = .37) and ECOG performance status (mean [SD], 0.62 [0.74] vs 0.68 [0.80]; P = .16) were similar between the 2 cohorts. In propensity score weighted-analyses, proton chemoradiotherapy was associated with a significantly lower relative risk of 90-day adverse events of at least grade 3 (0.31; 95% CI, 0.15-0.66; P = .002), 90-day adverse events of at least grade 2 (0.78; 95% CI, 0.65-0.93; P = .006), and decline in performance status during treatment (0.51; 95% CI, 0.37-0.71; P < .001). There was no difference in disease-free or overall survival. Conclusions and Relevance: In this analysis, proton chemoradiotherapy was associated with significantly reduced acute adverse events that caused unplanned hospitalizations, with similar disease-free and overall survival. Prospective trials are warranted to validate these results.

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS.

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dimensional feature representations that ultimately leads to accurate prediction of patient survival, outperforming conventional methods under comparison.

Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients.

This retrospective study was to investigate whether radiomics feature come from radiotherapy treatment planning CT can predict prognosis in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery. Four-hundred-eleven locally advanced rectal cancer patients which were treated with neoadjuvant chemoradiation enrolled in this study. All patients' radiotherapy treatment planning CTs were collected. Tumor was delineated on these CTs by physicians. An in-house radiomics software was used to calculate 271 radiomics features. The results of test-retest and contour-recontour studies were used to filter stable radiomics (Spearman correlation coefficient > 0.7). Twenty-one radiomics features were final enrolled. The performance of prediction model with the radiomics or clinical features were calculated. The clinical outcomes include local control, distant control, disease-free survival (DFS) and overall survival (OS). Model performance C-index was evaluated by C-index. Patients are divided into two groups by cluster results. The results of chi-square test revealed that the radiomics feature cluster is independent of clinical features. Patients have significant differences in OS (p = 0.032, log rank test) for these two groups. By supervised modeling, radiomics features can improve the prediction power of OS from 0.672 [0.617 0.728] with clinical features only to 0.730 [0.658 0.801]. In conclusion, the radiomics features from radiotherapy CT can potentially predict OS for locally advanced rectal cancer patients with neoadjuvant chemoradiation treatment.

The Impact of Clinical Trial Quality Assurance on Outcome in Head and Neck Radiotherapy Treatment.

Purpose: To investigate the impact of radiation treatment quality assurance (RTQA) on treatment outcomes in a phase III trial for advanced head and neck cancer. Materials and Methods: A total of 767 patients from NRG/RTOG 0522 were included in this study. The contours of target volume (TV) and organ at risk (OAR), and dose-volume coverage of targets were reviewed and scored (per-protocol, variation-acceptable and deviation-unacceptable) according to the protocol. We performed log-rank tests for RTQA scores with patients' outcomes, including local control (LC), distant control (DC) and overall survival (OS). Cox models with and without RTQA score data were established. To obtain a more reasonable model, per-protocol and variation acceptable were combined into a single acceptable score. Results: The log-rank test showed that all RTQA scores correlated with LC, which was significantly different between the per-protocol and variation-acceptable patients in target and OAR contouring (p-value = 0.004 and 0.043). For dose-volume score, the per-protocol and variation-acceptable patients were significantly different from unacceptable patients in the LC, with a p-value = 0.020 and 0.006, respectively. The DC of patients with variation-acceptable was significantly different than that of the unacceptable patients (p-value = 0.043). There were no correlations between RTQA scores with other outcomes. By incorporating RTQA scores into outcome modeling, the performance of LC model can be improved from 0.62 to 0.63 (c-index). The RTQA scores had no impact on DC and OS. Conclusion: RTQA scores are related to patients' local control rates in head and neck cancer radiotherapy.

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. METHODS AND MATERIALS: A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade ≥2 occurring in the 12th month of radiation therapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. RESULTS: The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows: accuracy: 0.76, 0.74, 0.73, 0.65, 0.64, and 0.56; sensitivity: 0.76, 0.72, 0.77, 0.59, 0.72, and 0.75; specificity: 0.76, 0.76, 0.71, 0.69, 0.59, and 0.43; F-score: 0.70, 0.68, 0.69, 0.56, 0.60, and 0.57; and area under the receiver operator characteristic curve: 0.84, 0.82, 0.78, 0.70, 0.74, and 0.68, respectively. CONCLUSIONS: The proposed model uses 3D rCNN filters to extract low- and high-level spatial features and to achieve promising performance. This is a potentially effective model for predicting objective toxicity for supporting clinical decision making.

Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades.

PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks (CNN) Cascades. METHODS: CNN Cascades was a two-step, coarse-to-fine approach that consisted of a simple region detector (SRD) and a fine segmentation unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. RESULTS: The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. CONCLUSIONS: The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN. METHODS: Data of 100 patients (50 in supine and 50 in prone) with rectal cancer were collected for this study. We designed three sets of experiments for comparison: (a) segmentation using the model trained with data from the same orientation; (b) segmentation using the model trained with data from the opposite orientation; (c) segmentation using the model trained with data from both orientations. We performed fivefold cross-validation. The performance was evaluated on segmentation of the clinical target volume (CTV), bladder, and femurs with Dice similarity coefficient (DSC) and Hausdorff distance (HD). RESULTS: Compared with models trained on cases positioned in the same orientation, the models trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) on CTV and bladder segmentation, but had comparable accuracy for femurs (P > 0.05). The average DSC values were 0.74 vs 0.84, 0.85 vs 0.88, and 0.91 vs 0.91 for CTV, bladder, and femurs, respectively. The corresponding HD values (mm) were 16.6 vs 14.6, 8.4 vs 8.1, and 6.3 vs 6.3, respectively. The models trained with data from both orientations have comparable accuracy (P > 0.05), with average DSC of 0.84, 0.88, and 0.91 and HD of 14.4, 8.1, and 6.3, respectively. CONCLUSIONS: Orientation affects the accuracy for CTV and bladder, but has negligible effect on the femurs. The model trained from data combining both orientations performs as well as a model trained with data from the same orientation for all the organs. These observations can offer guidance on the choice of training data for accurate segmentation.

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGING DATA BASED SURVIVAL ANALYSIS OF RECTAL CANCER.

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations. To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model. To make the CNNs applicable to tumors with varied sizes, a spatial pyramid pooling strategy is adopted. Our method has been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of rectal cancer patients treated for locally advanced rectal cancer. Compared with survival prediction models built upon hand-crafted radiomic features using Cox proportional hazards model and random survival forests, our method achieved competitive prediction performance.

Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction with improved discriminative power and robustness to noisy radiomic features, we develop an adaptive sparsity regularization based collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively. Our method is built on adaptive sparsity regularized matrix tri-factorization for simultaneous feature denoising and dimension reduction so that the noise is adaptively isolated from the features, and grouping information of patients with distinctive features provides latent supervision information to guide feature dimension reduction. The sparsity regularization is grounded on distribution modeling of transform-domain coefficients in a Bayesian framework. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and empirical results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.

Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of different patients are of different sizes. Thus, small tumors may be ignored while big tumors may exceed the receptive fields of convolutions. The purpose of this study is to further improve the segmentation accuracy using a novel CNN (named CAC-SPP) with cascaded atrous convolution (CAC) and a spatial pyramid pooling (SPP) module. This work is the first attempt at applying SPP for segmentation in radiotherapy. We improved the network based on ResNet-101 yielding accuracy gains from a greatly increased depth. We added CAC to extract a high-resolution feature map while maintaining large receptive fields. We also adopted a parallel SPP module with different atrous rates to capture the multi-scale features. The performance was compared with the widely adopted U-Net and ResNet-101 with independent segmentation of rectal tumors for two image sets, separately: (1) 70 T2-weighted MR images and (2) 100 planning CT images. The results show that the proposed CAC-SPP outperformed the U-Net and ResNet-101 for both image sets. The Dice similarity coefficient values of CAC-SPP were 0.78 ± 0.08 and 0.85 ± 0.03, respectively, which were higher than those of U-Net (0.70 ± 0.11 and 0.82 ± 0.04) and ResNet-101 (0.76 ± 0.10 and 0.84 ± 0.03). The segmentation speed of CAC-SPP was comparable with ResNet-101, but about 36% faster than U-Net. In conclusion, the proposed CAC-SPP, which could extract high-resolution features with large receptive fields and capture multi-scale context yields, improves the accuracy of segmentation performance for rectal tumors.

Nε-fatty acylation of multiple membrane-associated proteins by Shigella IcsB effector to modulate host function.

Shigella flexneri, an intracellular Gram-negative bacterium causative for shigellosis, employs a type III secretion system to deliver virulence effectors into host cells. One such effector, IcsB, is critical for S. flexneri intracellular survival and pathogenesis, but its mechanism of action is unknown. Here, we discover that IcsB is an 18-carbon fatty acyltransferase catalysing lysine Nε-fatty acylation. IcsB disrupted the actin cytoskeleton in eukaryotes, resulting from Nε-fatty acylation of RhoGTPases on lysine residues in their polybasic region. Chemical proteomic profiling identified about 60 additional targets modified by IcsB during infection, which were validated by biochemical assays. Most IcsB targets are membrane-associated proteins bearing a lysine-rich polybasic region, including members of the Ras, Rho and Rab families of small GTPases. IcsB also modifies SNARE proteins and other non-GTPase substrates, suggesting an extensive interplay between S. flexneri and host membrane trafficking. IcsB is localized on the Shigella-containing vacuole to fatty-acylate its targets. Knockout of CHMP5-one of the IcsB targets and a component of the ESCRT-III complex-specifically affected S. flexneri escape from host autophagy. The unique Nε-fatty acyltransferase activity of IcsB and its altering of the fatty acylation landscape of host membrane proteomes represent an unprecedented mechanism in bacterial pathogenesis.

NRG Oncology medical physicists' manpower survey quantifying support demands for multi-institutional clinical trials.

PURPOSE: A survey was created by NRG to assess a medical physicists' percent full time equivalent (FTE) contribution to multi-institutional clinical trials. A 2012 American Society for Radiation Oncology report, "Safety Is No Accident," quantified medical physics staffing contributions in FTE factors for clinical departments. No quantification of FTE effort associated with clinical trials was included. METHODS: To address this lack of information, the NRG Medical Physics Subcommittee decided to obtain manpower data from the medical physics community to quantify the amount of time medical physicists spent supporting clinical trials. A survey, consisting of 16 questions, was designed to obtain information regarding physicists' time spent supporting clinical trials. The survey was distributed to medical physicists at 1996 radiation therapy institutions included on the membership rosters of the 5 National Clinical Trials Network clinical trial groups. RESULTS: Of the 451 institutions who responded, 50% (226) reported currently participating in radiation therapy trials. On average, the designated physicist at each institution spent 2.4 hours (standard deviation [SD], 5.5) per week supervising or interacting with clinical trial staff. On average, 1.2 hours (SD, 3.1), 1.8 hours (SD, 3.9), and 0.6 hours (SD, 1.1) per week were spent on trial patient simulations, treatment plan reviews, and maintaining a Digital Imaging and Communications in Medicine server, respectively. For all trial credentialing activities, physicists spent an average of 32 hours (SD, 57.2) yearly. Reading protocols and supporting dosimetrists, clinicians, and therapists took an average of 2.1 hours (SD, 3.4) per week. Physicists also attended clinical trial meetings, on average, 1.2 hours (SD, 1.9) per month. CONCLUSION: On average, physicist spent a nontrivial total of 9 hours per week (0.21 FTE) supporting an average of 10 active clinical trials. This time commitment indicates the complexity of radiation therapy clinical trials and should be taken into account when staffing radiation therapy institutions.

Angiopoietin receptor Tie2 is required for vein specification and maintenance via regulating COUP-TFII.

Mechanisms underlying the vein development remain largely unknown. Tie2 signaling mediates endothelial cell (EC) survival and vascular maturation and its activating mutations are linked to venous malformations. Here we show that vein formation are disrupted in mouse skin and mesentery when Tie2 signals are diminished by targeted deletion of Tek either ubiquitously or specifically in embryonic ECs. Postnatal Tie2 attenuation resulted in the degeneration of newly formed veins followed by the formation of haemangioma-like vascular tufts in retina and venous tortuosity. Mechanistically, Tie2 insufficiency compromised venous EC identity, as indicated by a significant decrease of COUP-TFII protein level, a key regulator in venogenesis. Consistently, angiopoietin-1 stimulation increased COUP-TFII in cultured ECs, while Tie2 knockdown or blockade of Tie2 downstream PI3K/Akt pathway reduced COUP-TFII which could be reverted by the proteasome inhibition. Together, our results imply that Tie2 is essential for venous specification and maintenance via Akt mediated stabilization of COUP-TFII.

Reproducibility with repeat CT in radiomics study for rectal cancer.

PURPOSE: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer. RESULTS: Volume normalized features are much more reproducible than unnormalized features. The average value of all slices is the most reproducible feature type in rectal cancer. Different filters have little effect for the reproducibility of radiomics features. For the average type features, 496 out of 775 features showed high reproducibility (ICC ≥ 0.8), 225 out of 775 features showed medium reproducibility (0.8 > ICC ≥ 0.5) and 54 out of 775 features showed low reproducibility (ICC < 0.5). METHODS: 40 rectal cancer patients with stage II were enrolled in this study, each of whom underwent two CT scans within average 8.7 days. 775 radiomics features were defined in this study. For each features, five different values (value from the largest slice, maximum value, minimum value, average value of all slices and value from superposed intermediate matrix) were extracted. Meanwhile a LOG filter with different parameters was applied to these images to find stable filter value. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) of two CT scans were calculated to assess the reproducibility, based on original features and volume normalized features. CONCLUSIONS: Features are recommended to be normalized to volume in radiomics analysis. The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.to modulate two anticancer compounds in well-defined sets of GBM patients.