Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.

PURPOSE: This study evaluates the performance of a kilovoltage x-ray image-guidance system equipped with a novel post-processing optimization algorithm on the newly introduced TAICHI linear accelerator (Linac). METHODS: A comparative study involving image quality tests and radiation dose measurements was conducted across six scanning protocols of the kV-cone beam computed tomography (CBCT) system on the TAICHI Linac. The performance assessment utilized the conventional Feldkamp-Davis-Kress (FDK) algorithm and a novel Non-Local Means denoising and adaptive scattering correction (NLM-ASC) algorithm. Image quality metrics, including spatial resolution, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were evaluated using a Catphan 604 phantom. Radiation doses for low-dose and standard protocols were measured using a computed tomography dose index (CTDI) phantom, with comparative measurements from the Halcyon Linac's iterative CBCT (iCBCT). RESULTS: The NLM-ASC algorithm significantly improved image quality, achieving a 300%-1000% increase in CNR and SNR over the FDK-only images and it also showed a 100%-200% improvement over the iCBCT images from Halcyon's head protocol. The optimized low-dose protocols yielded higher image quality than the standard FDK protocols, indicating potential for reduced radiation exposure. Clinical implementation confirmed the TAICHI system's utility for precise and adaptive radiotherapy. CONCLUSION: The kV-IGRT system on the TAICHI Linac, with its novel post-processing algorithm, demonstrated superior image quality suitable for routine clinical use, effectively reducing image noise without compromising other quality metrics.

HDConv: Heterogeneous kernel-based dilated convolutions.

Dilated convolution has been widely used in various computer vision tasks due to its ability to expand the receptive field while maintaining the resolution of feature maps. However, the critical challenge is the gridding problem caused by the isomorphic structure of the dilated convolution, where the holes filled in the dilated convolution destroy the integrity of the extracted information and cut off the relevance of neighboring pixels. In this work, a novel heterogeneous dilated convolution, called HDConv, is proposed to address this issue by setting independent dilation rates on grouped channels while keeping the general convolution operation. The heterogeneous structure can effectively avoid the gridding problem while introducing multi-scale kernels in the filters. Based on the heterogeneous structure of the proposed HDConv, we also explore the benefit of large receptive fields to feature extraction by comparing different combinations of dilated rates. Finally, a series of experiments are conducted to verify the effectiveness of some computer vision tasks, such as image segmentation and object detection. The results show the proposed HDConv can achieve a competitive performance on ADE20K, Cityscapes, COCO-Stuff10k, COCO, and a medical image dataset UESTC-COVID-19. The proposed module can readily replace conventional convolutions in existing convolutional neural networks (i.e., plug-and-play), and it is promising to further extend dilated convolution to wider scenarios in the field of image segmentation.

G protein-coupled receptor kinase 2 as a novel therapeutic target for gland fibrosis of Sjögren's syndrome.

Sjogren's syndrome (SS) is a chronic, progressive autoimmune disorder characterized by gland fibrosis. We previously found a close correlation between gland fibrosis and the expression of G protein-coupled receptor kinase 2 (GRK2). In this study we explored the pathological and therapeutic significance of GRK2 in SS. Submandibular gland (SMG) antigen-induced SS mouse model was established in WT and GRK2+/- mice. We showed that the expression levels of GRK2 were significantly up-regulated in glandular tissue and positively correlated with fibrotic morphology in SS patients and mice. Hemizygous knockout of GRK2 significantly inhibited the gland fibrosis. In mouse salivary gland epithelial cells (SGECs), we demonstrated that GRK2 interacted with Smad2/3 to positively regulate the activation of TGF-ß-Smad signaling with a TGF-ß-GRK2 positive feedback loop contributing to gland fibrosis. Hemizygous knockout of GRK2 attenuated TGF-ß-induced collagen I production in SGECs in vitro and hindered gland fibrosis in murine SS though preventing Smad2/3 nuclear translocation. Around 28 days post immunization with SMG antigen, WT SS mice were treated with a specific GRK2 inhibitor paroxetine (Par, 5 mg·kg-1·d-1, i.g. for 19 days). We found that Par administration significantly attenuated gland fibrosis and alleviated the progression of SS in mice. We conclude that genetic knockdown or pharmacological inhibition of GRK2 significantly attenuates gland fibrosis and alleviates the progression of SS. GRK2 binds to Smad2/3 and positively regulates the activation of TGF-ß-Smad signaling. A TGF-ß-GRK2 positive feedback loop contributes to gland fibrosis. Our research points out that GRK2 could be a promising therapeutic target for treating SS.

Chronic ß-adrenergic stress contributes to cardiomyopathy in rodents with collagen-induced arthritis.

Patients with rheumatoid arthritis (RA) have a much higher incidence of cardiac dysfunction, which contributes to the high mortality rate of RA despite anti-arthritic drug therapy. In this study, we investigated dynamic changes in cardiac function in classic animal models of RA and examined the potential effectors of RA-induced heart failure (HF). Collagen-induced arthritis (CIA) models were established in rats and mice. The cardiac function of CIA animals was dynamically monitored using echocardiography and haemodynamics. We showed that cardiac diastolic and systolic dysfunction occurred in CIA animals and persisted after joint inflammation and that serum proinflammatory cytokine (IL-1ß, TNF-α) levels were decreased. We did not find evidence of atherosclerosis (AS) in arthritic animals even though cardiomyopathy was significant. We observed that an impaired cardiac ß1AR-excitation contraction coupling signal was accompanied by sustained increases in blood epinephrine levels in CIA rats. Furthermore, serum epinephrine concentrations were positively correlated with the heart failure biomarker NT-proBNP in RA patients (r2 = +0.53, P < 0.0001). In CIA mice, treatment with the nonselective ßAR blocker carvedilol (2.5 mg·kg-1·d-1, for 4 weeks) or the specific GRK2 inhibitor paroxetine (2.5 mg·kg-1·d-1, for 4 weeks) effectively rescued heart function. We conclude that chronic and persistent ß-adrenergic stress in CIA animals is a significant contributor to cardiomyopathy, which may be a potential target for protecting RA patients against HF.

Dual-domain accelerated MRI reconstruction using transformers with learning-based undersampling.

Acceleration in MRI has garnered much attention from the deep-learning community in recent years, particularly for imaging large anatomical volumes such as the abdomen or moving targets such as the heart. A variety of deep learning approaches have been investigated, with most existing works using convolutional neural network (CNN)-based architectures as the reconstruction backbone, paired with fixed, rather than learned, k-space undersampling patterns. In both image domain and k-space, CNN-based architectures may not be optimal for reconstruction due to its limited ability to capture long-range dependencies. Furthermore, fixed undersampling patterns, despite ease of implementation, may not lead to optimal reconstruction. Lastly, few deep learning models to date have leveraged temporal correlation across dynamic MRI data to improve reconstruction. To address these gaps, we present a dual-domain (image and k-space), transformer-based reconstruction network, paired with learning-based undersampling that accepts temporally correlated sequences of MRI images for dynamic reconstruction. We call our model DuDReTLU-net. We train the network end-to-end against fully sampled ground truth dataset. Human cardiac CINE images undersampled at different factors (5-100) were tested. Reconstructed images were assessed both visually and quantitatively via the structural similarity index, mean squared error, and peak signal-to-noise. Experimental results show superior performance of DuDReTLU-net over state-of-the-art methods (LOUPE, k-t SLR, BM3D-MRI) in accelerated MRI reconstruction; ablation studies show that transformer-based reconstruction outperformed CNN-based reconstruction in both image domain and k-space; dual-domain reconstruction architectures outperformed single-domain reconstruction architectures regardless of reconstruction backbone (CNN or transformer); and dynamic sequence input leads to more accurate reconstructions than single frame input. We expect our results to encourage further research in the use of dual-domain architectures, transformer-based architectures, and learning-based undersampling, in the setting of accelerated MRI reconstruction. The code for this project is made freely available at https://github.com/william2343/dual-domain-mri-recon-nets (Hong et al., 2022).

Adaptively Customizing Activation Functions for Various Layers.

To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and rectified linear unit (ReLU). To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VggNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC, and COCO). To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta, and ADAM) and different recognition tasks such as classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision, and generalization, and it can surpass other popular methods such as ReLU and adaptive functions such as Swish in almost all experiments in terms of overall performance.

Neoadjuvant Chemo-Radiation Using IGRT in Patients with Locally Advanced Gastric Cancer.

The goal of this study was to see how effective and safe neoadjuvant chemoradiation with image-guided IMRT was in patients with locally advanced resectable gastric cancer. Between January 2013 and June 2019, patients with locally advanced (cT3/cT4 or N+) gastric cancer treated with neoadjuvant chemoradiotherapy at PUMCH (Peking Union Medical College Hospital) were retrospectively studied. Using concurrent chemotherapy (Capecitabine alone or XELOX*2 cycles), radiotherapy (IMRT (intensity-modulated radiation therapy) 45 Gy, 25#, 5 weeks) was delivered with IGRT (image-guided radiotherapy) before the start of each weeks therapy to ensure accuracy and repeatability. A total of 95 patients were enrolled in the study, 93 (97.9%) stage cT3/T4 and 85 (89.5%) stage N+. Of these, 85 patients (89.5%) had a tumor located in the upper 1/3 of the stomach, and 93/95 patients (97.9%) completed neoadjuvant chemoradiation, with 80 patients (84.2%) undergoing stomach resection (58 D2 and 22 D1 gastrostomies). Pathology downstaging was found in 68 patients (85.0%), with 66 patients (82.5%) receiving T downstaging and 56 patients (70.0%) receiving N downstaging. There were 11 individuals (13.8%) who had a pathological complete response (PCR). The average period of follow-up was 44.7 months (19-96 months). The 5-year OS (overall survival), LRFS (local recurrence-free survival), and DMFS (distant metastasis free survival) rates of patients were 47.0% (95% CI: 38.6-55.4), 86.55% (95% CI: 79.1-93.99) and 60.71% (95% CI: 51.49-69.93%), respectively. Thirteen (13.7%) patients had grade 3-4 leukopenia, anemia, and thrombocytopenia, while 9 (9.5%) patients had grade 3-4 anemia, and 5 (5.3%) patients had grade 3-4 thrombocytopenia. PCR was found to be a significant predictive factor for OS in multivariate analysis (HR = 11.211, 95% CI: 1.500-83.813, p = 0.024). The method of using IGRT image-guided IMRT (45 Gy, 25 fractions, 5 weeks) combined with concurrent chemotherapy in patients with locally advanced resectable gastric cancer was equally effective when compared to the clinical efficacy of neoadjuvant chemoradiotherapy, with clinical outcomes achieving equal efficacy, with similar PCR rates and high rates of OS, LRFS, and DMFS, as well as good tolerances of concurrent chemoradiotherapy with acceptable side effects.

Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes.

BACKGROUND: The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. METHODS: Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. RESULTS: The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. CONCLUSION: This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.

Medical image augmentation for lesion detection using a texture-constrained multichannel progressive GAN.

Lesion detectors based on deep learning can assist doctors in diagnosing diseases. However, the performance of current detectors is likely to be unsatisfactory due to the scarcity of training samples. Therefore, it is beneficial to use image generation to augment the training set of a detector. However, when the imaging texture of the medical image is relatively delicate, the synthesized image generated by an existing method may be too poor in quality to meet the training requirements of the detectors. In this regard, a medical image augmentation method, namely, a texture-constrained multichannel progressive generative adversarial network (TMP-GAN), is proposed in this work. TMP-GAN uses joint training of multiple channels to effectively avoid the typical shortcomings of the current generation methods. It also uses an adversarial learning-based texture discrimination loss to further improve the fidelity of the synthesized images. In addition, TMP-GAN employs a progressive generation mechanism to steadily improve the accuracy of the medical image synthesizer. Experiments on the publicly available dataset CBIS-DDMS and our pancreatic tumor dataset show that the precision/recall/F1-score of the detector trained on the TMP-GAN augmented dataset improves by 2.59%/2.70%/2.77% and 2.44%/2.06%/2.36%, respectively, compared to the optimal results of other data augmentation methods. The FROC curve of the detector is also better than the curve from the contrast-augmented trained dataset. Therefore, we believe the proposed TMP-GAN is a practical technique to efficiently implement lesion detection case studies.

Generative Adversarial Networks in Medical Image augmentation: A review.

OBJECT: With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed. METHOD: This paper reviews 105 medical image augmentation related papers, which mainly collected by ELSEVIER, IEEE Xplore, and Springer from 2018 to 2021. We counted these papers according to the parts of the organs corresponding to the images, and sorted out the medical image datasets that appeared in them, the loss function in model training, and the quantitative evaluation metrics of image augmentation. At the same time, we briefly introduce the literature collected in three journals and three conferences that have received attention in medical image processing. RESULT: First, we summarize the advantages of various augmentation models, loss functions, and evaluation metrics. Researchers can use this information as a reference when designing augmentation tasks. Second, we explore the relationship between augmented models and the amount of the training set, and tease out the role that augmented models may play when the quality of the training set is limited. Third, the statistical number of papers shows that the development momentum of this research field remains strong. Furthermore, we discuss the existing limitations of this type of model and suggest possible research directions. CONCLUSION: We discuss GAN-based medical image augmentation work in detail. This method effectively alleviates the challenge of limited training samples for medical image diagnosis and treatment models. It is hoped that this review will benefit researchers interested in this field.

Regeneration of Phytochemicals by Structure-Driven Organization of Microbial Biosynthetic Steps.

Medicinal phytochemicals, such as artemisinin and taxol, have impacted the world, and hypericin might do so if its availability issue could be addressed. Hypericin is the hallmark component of Saint John's wort (Hypericum perforatum L.), an approved depression alleviator documented in the US, European, and British pharmacopoeias with its additional effectiveness against diverse cancers and viruses. However, the academia-to-industry transition of hypericin remain hampered by its low in planta abundance, unfeasible bulk chemical synthesis, and unclear biosynthetic mechanism. Here, we present a strategy consisting of the hypericin-structure-centered modification and reorganization of microbial biosynthetic steps in the repurposed cells that have been tamed to enable the designed consecutive reactions to afford hypericin (43.1 mg L-1 ), without acquiring its biosynthetic knowledge in native plants. The study provides a synthetic biology route to hypericin and establishes a platform for biosustainable access to medicinal phytochemicals.

Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.

Due to the irregular shapes,various sizes and indistinguishable boundaries between the normal and infected tissues, it is still a challenging task to accurately segment the infected lesions of COVID-19 on CT images. In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing multi-scale feature maps of different levels based on the encoder-decoder architecture. To this end, a deep collaborative supervision (Co-supervision) scheme is proposed to guide the network learning the features of edges and semantics. More specifically, an Edge Supervised Module (ESM) is firstly designed to highlight low-level boundary features by incorporating the edge supervised information into the initial stage of down-sampling. Meanwhile, an Auxiliary Semantic Supervised Module (ASSM) is proposed to strengthen high-level semantic information by integrating mask supervised information into the later stage. Then an Attention Fusion Module (AFM) is developed to fuse multiple scale feature maps of different levels by using an attention mechanism to reduce the semantic gaps between high-level and low-level feature maps. Finally, the effectiveness of the proposed scheme is demonstrated on four various COVID-19 CT datasets. The results show that the proposed three modules are all promising. Based on the baseline (ResUnet), using ESM, ASSM, or AFM alone can respectively increase Dice metric by 1.12%, 1.95%,1.63% in our dataset, while the integration by incorporating three models together can rise 3.97%. Compared with the existing approaches in various datasets, the proposed method can obtain better segmentation performance in some main metrics, and can achieve the best generalization and comprehensive performance.

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.

BACKGROUND: At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs. PURPOSE: A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs). MATERIALS AND METHODS: This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard. RESULTS: Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs. CONCLUSION: The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.

Sex-related Difference in Mental Rotation Performance is Mediated by the special Functional Connectivity Between the Default Mode and Salience Networks.

The mental rotation task is a particular spatial skill that helps people process visual information and is associated with intelligence and academic performance. Previous studies have found consistent sex difference in mental rotation. However, the neural mechanism of the sex-related difference in mental rotation remains unclear. This study investigates the association between sex, mental rotation and the functional connectivity (FC) of resting-state networks (RSNs) to explore neural correlates of different mental rotation abilities between males and females. Compared with females, males performed better on the mental rotation test. The mental rotation scores were significantly correlated with the special FC between the default mode network (DMN) and salience network (SN). The results of the mediation analysis revealed that the special FC between the DMN and SN mediated the association between sex and mental rotation. Based on these findings, males had higher FC between the DMN and SN, which subsequently promoted their mental rotation performance. These results emphasized the importance of sex in spatial cognition studies of both healthy people and individuals with neuropsychiatric disorders and deepened our understanding of the neural mechanisms underlying sex difference in mental rotation.

An adaptive learning method of anchor shape priors for biological cells detection and segmentation.

BACKGROUND AND OBJECTIVE: Owing to the variable shapes, large size difference, uneven grayscale and dense distribution among biological cells in an image, it is still a challenging task for the standard Mask R-CNN to accurately detect and segment cells. Especially, the state-of-the-art anchor-based methods fail to generate the anchors of sufficient scales effectively according to the various sizes and shapes of cells, thereby hardly covering all scales of cells. METHODS: We propose an adaptive approach to learn the anchor shape priors from data samples, and the aspect ratios and the number of anchor boxes can be dynamically adjusted by using ISODATA clustering algorithm instead of human prior knowledge in this work. To solve the identification difficulties for small objects owing to the multiple down-samplings in a deep learning-based method, a densification strategy of candidate anchors is presented to enhance the effects of identifying tinny size cells. Finally, a series of comparative experiments are conducted on various datasets to select appropriate a network structure and verify the effectiveness of the proposed methods. RESULTS: The results show that the ResNet-50-FPN combining the ISODATA method and densification strategy can obtain better performance than other methods in multiple metrics (including AP, Precision, Recall, Dice and PQ) for various biological cell datasets, such as U373, GoTW1, SIM+ and T24. CONCLUSIONS: Our adaptive algorithm could effectively learn the anchor shape priors from the various sizes and shapes of cells. It is promising and encouraging for a real-world anchor-based detection and segmentation application of biomedical engineering in the future.

Rhodomentosones A and B: Two Pairs of Enantiomeric Phloroglucinol Trimers from Rhodomyrtus tomentosa and Their Asymmetric Biomimetic Synthesis.

Rhodomentosones A and B (1 and 2), two pairs of novel enantiomeric phloroglucinol trimers featuring a unique 6/5/5/6/5/5/6-fused ring system were isolated from Rhodomyrtus tomentosa. Their structures with absolute configurations were elucidated by NMR spectroscopy, X-ray crystallography, and ECD calculation. The bioinspired syntheses of 1 and 2 were achieved in six steps featuring an organocatalytic asymmetric dehydroxylation/Michael addition/Kornblum-DeLaMare rearrangement/ketalization cascade reaction. Compounds 1 and 2 exhibited promising antiviral activities against respiratory syncytial virus (RSV).

Effects of growth hormone receptor antagonism and somatostatin analog administration on quality of life in acromegaly.

OBJECTIVE: Acromegaly is associated with impaired quality of life (QoL). We investigated the effects of biochemical control of acromegaly by growth hormone receptor antagonism vs somatostatin analog therapy on QoL. DESIGN: Cross-sectional. PATIENTS: 116 subjects: n = 55 receiving a somatostatin analog (SSA group); n = 29 receiving pegvisomant (PEG group); n = 32 active acromegaly on no medical therapy (ACTIVE group). MEASUREMENTS: Acromegaly QoL Questionnaire (AcroQoL), Rand 36-Item Short Form Survey (SF-36) and Gastrointestinal QoL Index (GIQLI); fasting glucose, insulin and IGF-1 levels (LC/MS, Quest Diagnostics). RESULTS: There were no group differences in mean age, BMI or sex [(whole cohort mean ± SD) age 52 ± 14 years, BMI 30 ± 6 kg/m2 , and male sex 38%]. Mean IGF-1 Z-scores were higher in ACTIVE (3.9 ± 1.0) vs SSA and PEG, which did not differ from one another (0.5 ± 0.7 and 0.5 ± 0.7, P < .0001 vs ACTIVE). Eighty-three per cent of PEG previously received somatostatin analogs, which had been discontinued due to lack of efficacy (52%) or side effects (41%). There were no differences in the four QoL primary end-points (AcroQoL Global Score, SF-36 Physical Component Summary Score, SF-36 Mental Health Summary Score and GIQLI Global Score) between SSA and PEG. Higher HbA1c, BMI and IGF-1 Z-scores were associated with poorer QoL in several domains. CONCLUSION: Our data support a comparable QoL in patients receiving pegvisomant vs somatostatin analogs, despite the fact that the vast majority receiving pegvisomant did not respond to or were not able to tolerate somatostatin analogs.

Two new azaphilone pigments from Talaromyces albobiverticillius and their anti-inflammatory activity.

Two new azaphilone pigments, talaralbols A and B (3 and 7), along with five known azaphilone metabolites (1, 2, and 4-6), were isolated from the culture of Talaromyces albobiverticillius associated with the isopod Armadillidium vulgare. Their structures were elucidated by a combination of 1 D and 2 D NMR data, ECD calculations, chemical transformations, and NMR data analogy with model compounds. Talaralbol A (3) showed a moderate inhibition on the lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW264.7 cells with the inhibitory rate being 31.0% at the concentration of 10 µM.[Formula: see text].

Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization.

In image denoising (IDN) processing, the low-rank property is usually considered as an important image prior. As a convex relaxation approximation of low rank, nuclear norm-based algorithms and their variants have attracted a significant attention. These algorithms can be collectively called image domain-based methods whose common drawback is the requirement of great number of iterations for some acceptable solution. Meanwhile, the sparsity of images in a certain transform domain has also been exploited in image denoising problems. Sparsity transform learning algorithms can achieve extremely fast computations as well as desirable performance. By taking both advantages of image domain and transform domain in a general framework, we propose a sparsifying transform learning and weighted singular values minimization method (STLWSM) for IDN problems. The proposed method can make full use of the preponderance of both domains. For solving the nonconvex cost function, we also present an efficient alternative solution for acceleration. Experimental results show that the proposed STLWSM achieves improvement both visually and quantitatively with a large margin over state-of-the-art approaches based on an alternatively single domain. It also needs much less iteration than all the image domain algorithms.