Synthesis of novel 5-amido-2-carboxypyrazines as inhibitors of the type three secretion system of Salmonella enterica serovar Typhimurium.

A series of novel 5-amido-2-carboxypyrazine derivatives were designed, synthesized and evaluated for the inhibitory activities against the T3SS of Salmonella enterica serovar Typhimurium. Preliminary results displayed that the compounds 2f, 2g, 2h and 2i showed potent inhibitory activities against T3SS. Compound 2h was identified as the most potent T3SS inhibitor and the SPI-1 effector secretion was strongly inhibited by 2h in a dose-dependent manner. The effects of compound 2h on the SPI-1 genes transcription might be via impacting the SicA/InvF regulatory pathway.

Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images.

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images. The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement.

Time dependency of automated collateral scores in computed tomography angiography and computed tomography perfusion images in patients with intracranial arterial occlusion.

PURPOSE: The assessment of collateral status may depend on the timing of image acquisition. The purpose of this study is to investigate whether there are optimal time points in CT Perfusion (CTP) for collateral status assessment, and compare collaterals scores at these time points with collateral scores from multiphase CT angiography (mCTA). METHODS: Patients with an acute intracranial occlusion who underwent baseline non-contrast CT, mCTA and CT perfusion were selected. Collateral status was assessed using an automatically computed Collateral Ratio (CR) score in mCTA, and predefined time points in CTP acquisition. CRs extracted from CTP were correlated with CRs from mCTA. In addition, all CRs were related to baseline National Institutes of Health Stroke Scale (NIHSS) and Alberta Stoke Program Early CT Score (ASPECTS) with linear regression analysis to find the optimal CR. RESULTS: In total 58 subjects (median age 74 years; interquartile range 61-83 years; 33 male) were included. When comparing the CRs from the CTP vs. mCTA acquisition, the strongest correlations were found between CR from baseline mCTA and the CR at the maximal intensity projection of time-resolved CTP (r = 0.81) and the CR at the peak of arterial enhancement point (r = 0.78). Baseline mCTA-derived CR had the highest correlation with ASPECTS (ß = 0.36 (95%CI 0.11, 0.61)) and NIHSS (ß = - 0.48 (95%CI - 0.72, - 0.16)). CONCLUSION: Collateral status assessment strongly depends on the timing of acquisition. Collateral scores obtained from mCTA imaging is close to the optimal collateral score obtained from CTP imaging.

Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke.

Introduction: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models. Methods: The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score. Experiments: Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters. Conclusion: The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model.

Inter-rater reliability for assessing intracranial collaterals in patients with acute ischemic stroke: comparing 29 raters and an artificial intelligence-based software.

PURPOSE: Outcome of endovascular treatment in acute ischemic stroke patients is depending on the collateral circulation maintaining blood flow to the ischemic territory. We evaluated the inter-rater reliability and accuracy of raters and an automated algorithm for assessing the collateral score (CS, range: 0-3) in acute ischemic stroke patients. METHODS: Baseline CTA scans with an intracranial anterior occlusion from the MR CLEAN study (n=500) were used. For each core lab CS, ten CTA scans with sufficient quality were randomly selected. After a training session in collateral scoring, all selected CTA scans were individually evaluated for a visual CS by three groups: 7 radiologists, 13 junior and 9 senior radiology residents. Two additional radiologists scored CS to be used as reference, with a third providing a CS to produce a 2 out of 3 consensus CS in case of disagreement. An automated algorithm was also used to compute CS. Inter-rater agreement was reported with intraclass correlation coefficient (ICC). Accuracy of visual and automated CS were calculated. RESULTS: 39 CTA scans were assessed (1 corrupt CTA-scan excluded). All groups showed a moderate ICC (0.689-0.780) in comparison to the reference standard. Overall human accuracy was 65± 7% and increased to 88± 5% for dichotomized CS (0-1, 2-3). Automated CS accuracy was 62%, and 90% for dichotomized CS. No significant difference in accuracy was found between groups with different levels of expertise. CONCLUSION: After training, inter-rater reliability in collateral scoring was not influenced by experience. Automated CS performs similar to residents and radiologists in determining a collateral score.

A one-pot synthesis of AgBr/Ag3PO4 composite photocatalysts.

In this study, an AgBr/Ag3PO4 (ABAP) photocatalyst has been prepared via a facile one-pot anion-exchange method. SEM, XRD, XPS and UV-Vis DRS characterization techniques are carried out to study the structural and physicochemical characteristics of the AgBr/Ag3PO4 composites. The ABAP photocatalyst exhibited outstanding photocatalytic capability for the photodegradation of rhodamine B (RhB) under visible light irradiation. The optimal ABAP-48% composite displayed the highest photocatalytic activity; a complete degradation was attained in 25 min under visible light irradiation. The excellent stability and reusability of ABAP catalysts were examined by five subsequent runs. A probable degradation mechanism of ABAP composites was carefully surveyed. Furthermore, radical trapping experiments confirmed that the ˙O2 - radical was the main active species in the photodegradation reaction.

Automatic Collateral Scoring From 3D CTA Images.

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.

Optimization of database for identification need of unknown spent nuclear fuel samples.

Recent years, the work of nuclear forensics has been greatly promoted in many aspects, and an important work is the establishment and application of nuclear forensic database. For now, this research is mainly based on uranium ore, because there is plenty of uranium ore information that is open and is easy to form database available for attribution. However, as the potential threat of spent nuclear fuel gets more and more attention, the number of researches on the identification of spent nuclear fuel via database is increasing. Since there is no public spent nuclear fuel database that is proper for attribution, such kind of work is mainly on methodology study. This paper focuses on the use of database for the identification of spent nuclear fuel. A database is first constructed with numerical simulation results and is used to identify samples both from simulation and experimental measurements to study the availability and applicability. Then samples from real database are used to optimize the database constructed with simulation result to better meet the need of real nuclear forensics scenarios.

Effect of amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing on the treatment of renal failure and hypertension by the PI3K/AKT pathway.

To study the effect and molecular mechanisms of amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing on the treatment methods of renal failure and hypertension. A total of 60 cases of renal failure hypertension were randomly divided into the Control group and the Treatment group. The control group was treated with amlodipine besylate, while the treatment group was treated with amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing. A rat model of renal failure hypertension was established. Rats were divided into the sham group, model group, NC group (treated with amlodipine besylate) and treatment group (treated with amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing). Rats were given drugs at 10­20 weeks of age to observe their general condition and detect changes of blood pressure, blood biochemical indices and urine index. The pathological changes of renal tissue were examined by hematoxylin and eosin staining, and the expression of vascular endothelial growth factor (VEGF) and matrix metalloproteinase (MMP)9 were detected by immunohistochemistry. Reverse transcription­quantitative polymerase chain reaction was used to determine mRNA expression of phosphoinositide 3­kinase (PI3K), protein kinase B (AKT) and endothelin (ET)­1 and western blotting was used to detect the expression of phosphorylated (p)­PI3K/PI3K, p­AKT/AKT and p­nuclear factor (NF)­κB p65/NF­κB p65 protein. Systolic and diastolic blood pressures in Treated group patients were significantly lower compared with in Control group patients. The systolic and diastolic blood pressure of rats were significantly decreased and blood urea nitrogen (BUN), carbapenem­resistant Enterobacteriaceae (CRE), N­acetyl­ß­D­glucosaminidase (NAG), urine protein (UP) and blood urea protein (BUP), contents were significantly decreased following amlodipine besylate treatment. The expression of VEGF and matrix metallopeptidase 9 protein were significantly decreased, but the expression of PI3K, AKT mRNA and p­PI3K/PI3K, p­AKT/AKT protein were significantly increased. ET­1 mRNA and p­NF­κB p65/NF­κB protein were significantly increased. The pathological alterations of renal tissue were improved and the pathological changes of glomerulus, tubule and interstitium were alleviated. Amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing can effectively reduce the systolic pressure and diastolic pressure of patients, and improve the symptoms and signs of patients, which may be associated with the regulation of the expression of PI3K/AKT pathway, so as to regulate the expression of BUN, CRE, UP, BUP and NAG.

Modes of Antiviral Action of Chemical Portions and Constituents from Woad Root Extract against Influenza Virus A FM1.

Woad root has been used for the prevention of influenza for hundreds of years in many Asian countries. In this study, the antiviral modes of clemastanin B (CB), epigoitrin, phenylpropanoid portion (PEP), and the mixture of phenylpropanoids, alkaloids, and organic acid portions (PEP + ALK + OA) from wood root extract against influenza virus A FM1 were investigated. The results revealed that CB, epigoitrin, PEP, and PEP + ALK + OA exert their anti-influenza activity via inhibiting the virus multiplication, prophylaxis, and blocking the virus attachment. The primary mode of action of PEP and PEP + ALK + OA is the inhibition of virus replication. The inhibitory effect on virus attachment and multiplication is the main modes for epigoitrin. All the compounds or chemical portions from woad root extract tested in this study do not have direct virucidal activity. Our results provided the comprehensive analysis of the antiviral mechanism of wood root extract.

Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach.

Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.