Fasudil mediates cell therapy of EAE by immunomodulating encephalomyelitic T cells and macrophages.

Although Fasudil has shown therapeutic potential in EAE mice, the mechanism of action are still not fully understood. Here, we examined the immunomodulatory effect of Fasudil on encephalitogenic mononuclear cells (MNCs), and tested the therapeutic potential of Fasudil-treated MNCs in active EAE. Fasudil inhibited expression of CCL20 on T cells and migration of T cells, decreased CD4(+) IFN-γ(+) and CD4(+) IL-17(+) T cells, but increased CD4(+) IL-10(+) and CD4(+) TGF-ß(+) T cells. Fasudil reduced expression of CD16/32 and IL-12, while elevating expression of CD206, CD23, and IL-10. Fasudil also decreased levels of iNOS/NO, enhanced levels of Arg-1, and inhibited the TLR-4/NF-κB signaling and TNF-α, shifting M1 macrophage to M2 phenotype. These modulatory effects of Fasudil on T cells and macrophages were not altered by adding autoantigen MOG35-55 to the culture, i.e., autoantigen-independent. Further, we observed that, in vitro, Fasudil inhibited the capacity of encephalitogenic MNCs to adoptively transfer EAE and reduced TLR-4/p-NF-κB/p65 and inflammatory cytokines in spinal cords. Importantly, Fasudil-treated encephalitogenic MNCs exhibited therapeutic potential when injected into actively induced EAE mice. Together, our results not only provide evidence that Fasudil mediates the polarization of macrophages and the regulation of T cells, but also reveal a novel strategy for cell therapy in MS.

Safflower Yellow regulates microglial polarization and inhibits inflammatory response in LPS-stimulated Bv2 cells.

Activated microglia, especially polarized M1 cells, produce pro-inflammatory cytokines and free radicals, thereby contributing directly to neuroinflammation and various brain disorders. Given that excessive or chronic neuroinflammation within the central nervous system (CNS) exacerbates neuronal damage, molecules that modulate neuroinflammation are candidates as neuroprotective agents. In this study, we provide evidence that Safflor yellow (SY), the main active component in the traditional Chinese medicine safflower, modulates inflammatory responses by acting directly on BV2 microglia. LPS stimulated BV2 cells to upregulate expression of TLR4-Myd88 and MAPK-NF-κB signaling pathways and to release IL-1ß, IL-6, TNF-α, and COX-2. However, SY treatment inhibited expression of TLR4-Myd88 and p-38/p-JNK-NF-κB, downregulated expression of iNOS, CD16/32, and IL-12, and upregulated CD206 and IL-10. In conclusion, our results demonstrate that SY exerts an anti-inflammatory effect on BV2 microglia, possibly through TLR-4/p-38/p-JNK/NF-κB signaling pathways and the conversion of microglia from inflammatory M1 to an anti-inflammatory M2 phenotype.

Wuzi Yanzong pill, a Chinese polyherbal formula, alleviates testicular damage in mice induced by ionizing radiation.

BACKGROUND: Chinese medicine Wuzi Yanzong pill (WZYZP) was firstly documented in ancient Chinese medical works "She Sheng Zhong Miao Fang" by Shi-Che Zhang in 1550 AD. The traditional herbal formula is widely used in treating nephrasthenia lumbago, prospermia, erectile dysfunction and male sterility. The present study was to explore the effects of WZYZP on ionizing irradiation-induced testicular damage in mice. METHODS: The pelvic region of male mice was exposed to X-rays for inducing testicular damage. The effects of WZYZP on testicular damage were evaluated in terms of testes weight, sperm quantity and motility, testes oxidative status and serum hormone levels. The alterations in testicular structure were examined by hematoxylin-eosin staining. Additionally, changes in proliferating cell nuclear antigen (PCNA) expression of testes were explored by western blot. RESULTS: Pelvic exposure to x-ray induced reduction in testes weight and sperm quality, along with oxidative stress and abnormal testicular architecture in testes. Oral administration of WZYZP for 3 weeks markedly increased testes weight, sperm quantity and motility, and attenuated testicular architecture damage. Meanwhile, WZYZP treatment significantly reversed the reduction of serum testosterone, and decreased testes malondialdehyde (MDA) and Oxidative stress index (OSI) relative to the radiated mice. Additionally, WZYZP effectively prevented the downregulation of PCNA expression in testes induced by x-ray irradiation. CONCLUSION: These findings suggest WZYZP exhibits ameliorating effects against ionizing irradiation-induced testicular damage in mice, which may be related to its antioxidation.

Intranasal delivery of FSD-C10, a novel Rho kinase inhibitor, exhibits therapeutic potential in experimental autoimmune encephalomyelitis.

Viewing multiple sclerosis (MS) as both neuroinflammation and neurodegeneration has major implications for therapy, with neuroprotection and neurorepair needed in addition to controlling neuroinflammation in the central nervous system (CNS). While Fasudil, an inhibitor of Rho kinase (ROCK), is known to suppress experimental autoimmune encephalomyelitis (EAE), an animal model of MS, it relies on multiple, short-term injections, with a narrow safety window. In this study, we explored the therapeutic effect of a novel ROCK inhibitor FSD-C10, a Fasudil derivative, on EAE. An important advantage of this derivative is that it can be used via non-injection routes; intranasal delivery is the preferred route because of its efficient CNS delivery and the much lower dose compared with oral delivery. Our results showed that intranasal delivery of FSD-C10 effectively ameliorated the clinical severity of EAE and CNS inflammatory infiltration and promoted neuroprotection. FSD-C10 effectively induced CNS production of the immunoregulatory cytokine interleukin-10 and boosted expression of nerve growth factor and brain-derived neurotrophic factor proteins, while inhibiting activation of p-nuclear factor-κB/p65 on astrocytes and production of multiple pro-inflammatory cytokines. In addition, FSD-C10 treatment effectively induced CD4(+) CD25(+) , CD4(+) FOXP3(+) regulatory T cells. Together, our results demonstrate that intranasal delivery of the novel ROCK inhibitor FSD-C10 has therapeutic potential in EAE, through mechanisms that possibly involve both inhibiting CNS inflammation and promoting neuroprotection.

Emergence, circulation, and spatiotemporal phylogenetic analysis of coxsackievirus a6- and coxsackievirus a10-associated hand, foot, and mouth disease infections from 2008 to 2012 in Shenzhen, China.

Sporadic hand, foot, and mouth disease (HFMD) outbreaks and other infectious diseases in recent years have frequently been associated with certain human enterovirus (HEV) serotypes. This study explored the prevalences and genetic characteristics of non-HEV71 and non-coxsackievirus A16 (CV-A16) human enterovirus-associated HFMD infections in Shenzhen, China. A total of 2,411 clinical stool specimens were collected from hospital-based surveillance for HFMD from 2008 to 2012. The detection of HEV was performed by real-time reverse transcription-PCR (RT-PCR) and RT-seminested PCR, and spatiotemporal phylogenetic analysis was performed based on the VP1 genes. A total of 1,803 (74.8%) strains comprising 28 different serotypes were detected. In the past 5 years, the predominant serotypes were HEV71 (60.0%), followed by CV-A16 (21.2%) and two uncommon serotypes, CV-A6 (13.0%) and CV-A10 (3.3%). However, CV-A6 replaced CV-A16 as the second most common serotype between 2010 and 2012. As an emerging pathogen, CV-A6 became as common a causative agent of HFMD as HEV71 in Shenzhen in 2012. Phylogenetic analysis revealed that little variation occurred in the Chinese HEV71 and CV-A16 strains. The genetic characteristics of the Chinese CV-A6 and CV-A10 strains displayed geographic differences. The CV-A6 and CV-A10 strains circulating in Shenzhen likely originated in Europe. It was found that human enteroviruses have a high mutation rate due to evolutionary pressure and frequent recombination (3.2 × 10(-3) to 6.4 ×10(-3) substitutions per site per year for HEV71, CV-A6, CV-A16, and CV-A10). Since certain serotypes are potential threats to the public health, this study provides further insights into the significance of the epidemiological surveillance of HFMD.

An ultrasound study of altered hydration behaviour of proteoglycan-degraded articular cartilage.

BACKGROUND: Articular cartilage is a solid-fluid biphasic material covering the bony ends of articulating joints. Hydration of articular cartilage is important to joint lubrication and weight-wearing. The aims of this study are to measure the altered hydration behaviour of the proteoglycan-degraded articular cartilage using high-frequency ultrasound and then to investigate the effect of proteoglycan (PG) degradation on cartilage hydration. METHODS: Twelve porcine patellae with smooth cartilage surface were prepared and evenly divided into two groups: normal group without any enzyme treatment and trypsin group treated with 0.25% trypsin solution for 4 h to digest PG in the tissue. After 40-minute exposure to air at room temperature, the specimens were immerged into the physiological saline solution. The dehydration induced hydration behaviour of the specimen was monitored by the high-frequency (25 MHz) ultrasound pulser/receiver (P/R) system. Dynamic strain and equilibrium strain were extracted to quantitatively evaluate the hydration behaviour of the dehydrated cartilage tissues. RESULTS: The hydration progress of the dehydrated cartilage tissue was observed in M-mode ultrasound image indicating that the hydration behaviour of the PG-degraded specimens decreased. The percentage value of the equilibrium strain (1.84 ± 0.21%) of the PG-degraded cartilage significantly (p < 0.01) decreased in comparison with healthy cartilage (3.46 ± 0.49%). The histological sections demonstrated that almost PG content in the entire cartilage layer was digested by trypsin. CONCLUSION: Using high-frequency ultrasound, this study found a reduction in the hydration behaviour of the PG-degraded cartilage. The results indicated that the degradation of PG decreased the hydration capability of the dehydrated tissue. This study may provide useful information for further study on changes in the biomechanical property of articular cartilage in osteoarthritis.

[Status and analysis of Astragali Radix resource in China].

To explore the status of the resources of Astragali Radix, a survey on its germplasm resources was carried out. Some conclusions can be drawn for Astragali Radix: the major source is the cultivated Astragalus mongolicus. The new major cultivation areas for A. mongolicus and A. membranaceus are Shandong and Gansu province. The semi-wildly planting model in Shanxi province maintains the genuine trait of Astragali Radix, but its yield is limited, and now a combination model has been developed. The major problems for Astragali Radix are the selection of planting sites, the rot root and difficulty in collecting and processing. Several developmental proposals for Astragali Radix were put forward including rational distribution of planting areas, establishment of standard system, development and standardization of producing technologies.

Semi-Supervised Segmentation of Interstitial Lung Disease Patterns from CT Images via Self-Training with Selective Re-Training.

Accurate segmentation of interstitial lung disease (ILD) patterns from computed tomography (CT) images is an essential prerequisite to treatment and follow-up. However, it is highly time-consuming for radiologists to pixel-by-pixel segment ILD patterns from CT scans with hundreds of slices. Consequently, it is hard to obtain large amounts of well-annotated data, which poses a huge challenge for data-driven deep learning-based methods. To alleviate this problem, we propose an end-to-end semi-supervised learning framework for the segmentation of ILD patterns (ESSegILD) from CT images via self-training with selective re-training. The proposed ESSegILD model is trained using a large CT dataset with slice-wise sparse annotations, i.e., only labeling a few slices in each CT volume with ILD patterns. Specifically, we adopt a popular semi-supervised framework, i.e., Mean-Teacher, that consists of a teacher model and a student model and uses consistency regularization to encourage consistent outputs from the two models under different perturbations. Furthermore, we propose introducing the latest self-training technique with a selective re-training strategy to select reliable pseudo-labels generated by the teacher model, which are used to expand training samples to promote the student model during iterative training. By leveraging consistency regularization and self-training with selective re-training, our proposed ESSegILD can effectively utilize unlabeled data from a partially annotated dataset to progressively improve the segmentation performance. Experiments are conducted on a dataset of 67 pneumonia patients with incomplete annotations containing over 11,000 CT images with eight different lung patterns of ILDs, with the results indicating that our proposed method is superior to the state-of-the-art methods.

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level. METHODS: A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m2 ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models. RESULTS: The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05). CONCLUSION: A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.

Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis.

Aim: Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods: Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model's performance on the internal test dataset and external test datasets. Results: A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion: The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

Role of Dietary Nutrients in the Modulation of Gut Microbiota: A Narrative Review.

Understanding how dietary nutrients modulate the gut microbiome is of great interest for the development of food products and eating patterns for combatting the global burden of non-communicable diseases. In this narrative review we assess scientific studies published from 2005 to 2019 that evaluated the effect of micro- and macro-nutrients on the composition of the gut microbiome using in vitro and in vivo models, and human clinical trials. The clinical evidence for micronutrients is less clear and generally lacking. However, preclinical evidence suggests that red wine- and tea-derived polyphenols and vitamin D can modulate potentially beneficial bacteria. Current research shows consistent clinical evidence that dietary fibers, including arabinoxylans, galacto-oligosaccharides, inulin, and oligofructose, promote a range of beneficial bacteria and suppress potentially detrimental species. The preclinical evidence suggests that both the quantity and type of fat modulate both beneficial and potentially detrimental microbes, as well as the Firmicutes/Bacteroides ratio in the gut. Clinical and preclinical studies suggest that the type and amount of proteins in the diet has substantial and differential effects on the gut microbiota. Further clinical investigation of the effect of micronutrients and macronutrients on the microbiome and metabolome is warranted, along with understanding how this influences host health.

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image.

Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches. A novel similarity measure is defined to capture and enhance the features correlation using Pearson correlation coefficient and Pearson distance. Interactive labels provided by user play a subsidiary role in the semi-supervised segmentation. The continuous graph cut model is solved via a fast minimization algorithm based on augmented Lagrangian and operator splitting. Additionally, Non-Uniform Rational B-Spline (NURBS) curve fitting is used as post-processing to solve the low resolution problem caused by the graph-based method. 200 B-mode US images of left ventricle of the rats were collected in this study. The myocardial tissues were segmented using the proposed fSP-CMC method compared with the method of fast Neighborhood Patches based Continuous Min-Cut (fP-CMC). The results show that the fSP-CMC segmented the myocardial tissues with a higher agreement with the ground truth (GT) provided by medical experts. The mean absolute distance (MAD) and Hausdorff distance (HD) were significantly lower than those values of fP-CMC (p < 0.05), while the Dice was significantly higher (p < 0.05). In conclusion, the proposed fSP-CMC method accurately and effectively segments the myocardiumn in US images. This method has potentials to be a reliable segmentation method and useful for the functional evaluation of myocardium in the future study.

Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study.

The assessment of myocardial motion plays a promising role in the evaluation of cardiac function. This study aims to propose a novel framework of global estimation of the myocardial motion using radio-frequency (RF) data. The framework consists of B-mode image reconstruction, displacement estimation, myocardium extraction, and image fusion. The RF data of murine heart in parasternal long-axis (PLAX) view were collected for B-mode image reconstruction and displacement estimation. The vectorized normalized cross-correlation (VNCC) approach was proposed to globally estimate the displacements of the RF frames, while a sum-table based normalized cross-correlation (STNCC) was performed as reference algorithm. The bimodal fusion images were obtained to visualize the motion and anatomical structure of myocardium by an improved fast mapping algorithm (IFMA). In comparison with STNCC, the computation time of displacement using VNCC reduced by approximate 10s. The myocardial motions of anterior wall and posterior wall during one cardiac cycle were similarly tracked by VNCC as that of STNCC. The averaged absolute error in displacement between the two methods ranges from 1 to 3µm. The obtained myocardial elastographic images using VNCC intuitively present the morphological and mechanical changes during the contraction period of left ventricle. The results demonstrate that the proposed framework is an efficient tool for the estimation of myocardial motion reflecting cardiac systolic function. This approach has potentials to provide visualized information of myocardium for diagnosis and prognosis of cardiovascular diseases (CVDs).

[Study on absorption mechanism of genistein self-microemulsifying system in rat intestines].

OBJECTIVE: To investigate the absorption mechanism of genistein self-microemulsifying system in rat intestines. METHOD: The concentrations of phenol red and genistein by in situ perfusion in rats were determined by UV and HPLC, respectively. The effects of drug concentrations, pH, various intestinal segments and P-glycoprotein (P-gp) inhibitor verapamil on the absorption had been studied. RESULT: The absorption rate constant (Ka) of genistein had no significant difference at concentrations of 0.05-0.5 mg x mL(-1) and pH of 5.4-7.8 in perfusion. It was Ka of jejunum > ileum > duodenum > colon. The absorption of genistein in jejunum had significant difference (P < 0.05) compared with other parts of intestines. Ka was increased obviously when verapamil was coper-fused with genistein (P < 0. 05). CONCLUSION: The absorption of genistein self-microemulsifying system is a first order process with passive diffusion mechanism related to P-gp efflux. It can be absorbed at all segments of rat intestine, and the jejunum is the best absorption segment, pH had no special effect on the absorption of genistein self-microemulsifying system in rat intestine.

[Segmentation of brain tumor on magnetic resonance images using 3D full-convolutional densely connected convolutional networks].

OBJECTIVE: Accurate segmentation of multiple gliomas from multimodal MRI is a prerequisite for many precision medical procedures. To effectively use the characteristics of glioma MRI and im-prove the segmentation accuracy, we proposes a multi-Dice loss function structure and used pre-experiments to select the good hyperparameters (i.e. data dimension, image fusion step, and the implementation of loss function) to construct a 3D full convolution DenseNet-based image feature learning network. This study included 274 segmented training sets of glioma MRI and 110 test sets without segmentation. After grayscale normalization of the image, the 3D image block was extracted as a network input, and the network output used the image block fusion method to obtain the final segmentation result. The proposed structure improved the accuracy of glioma segmentation compared to a general structure. In the on-line assessment of the open BraTS2015 data set, the Dice values for the entire tumor area, tumor core area, and enhanced tumor area were 0.85, 0.71, and 0.63, respectively.

[Segmentation of the prostate on magnetic resonance images using an ellipsoidal shape prior constraint algorithm].

We propose a novel strategy for multi-atlas-based image segmentation of the prostate on magnetic resonance (MR) images using an ellipsoidal shape prior constraint algorithm. An ellipsoidal shape prior constraint was incorporated into the process of multi-atlas based segmentation to restrict the regions of interest on the prostate images and avoid the interference by the surrounding tissues and organs in atlas selection. In the subsequent process of atlas fusion, the ellipsoidal shape prior constraint calibrated and compensated for the shape prior obtained by the registration technique to avoid incorrect segmentation caused by registration errors. Evaluation of this proposed method on prostate images from 50 subjects showed that this algorithm was effective and yielded a mean Dice similarity coefficients of 0.8812, suggesting its high accuracy and robustness to segment the prostate on MR images.

A new biocompatible microemulsion increases extraction yield and bioavailability of Andrographis paniculata.

The purpose of this study was to design and prepare a biocompatible microemulsion of Andrographis paniculata (BMAP) containing both fat-soluble and water-soluble constituents. We determined the contents of active constituents of BMAP and evaluated its bioavailability. The biocompatible microemulsion (BM), containing lecithin and bile salts, was optimized in the present study, showing a good physical stability. The mean droplet size was 19.12 nm, and the average polydispersity index (PDI) was 0.153. The contents of andrographolide and dehydroandrographolide in BMAP, as determined by high performance liquid chromatography (HPLC), were higher than that in ethanol extraction. The pharmacokinetic results of BMAP showed that the AUC0-7 and AUC0→∞ values of BMAP were 2.267 and 27.156 µg·mL(-1)·h(-1), respectively, and were about 1.41-fold and 6.30-fold greater than that of ethanol extraction, respectively. These results demonstrated that the bioavailability of and rographolide extracted by BMAP was significantly higher than that extracted by ethanol. In conclusion, the BMAP preparation displayed ann improved dose form for future clinical applications.

Complete genome sequence of a coxsackievirus a16 strain, isolated from a fatal case in shenzhen, southern china, in 2014.

We determined the complete genome sequence of a coxsackievirus A16 strain (CVA16/SZ29/CHN/2014) from a fatal case in Shenzhen, southern China, in 2014. The strain was assigned to subgenotype B1b based on phylogenetic analysis of the VP1 gene.

A fast sequential image fractal coding approach based on optimal fuzzy clustering.

To reduce the coding time of the conventional method, a fast sequential image fractal compression algorithm was proposed on the basis of the principle of optimal fuzzy clustering (OFC) for an unsupervised sample set with the category number settled by the algorithm itself. We utilized the cost function defined by the OFC algorithm to obtain the best category number corresponding to the minimum value of the function. Firstly the Linde-Buzo-Gray (LBG) algorithm was realized to acquire a rough cluster of the domain pool. Then the optimal category number was obtained by implementing our algorithm with small computational cost. Finally the more precise category was gained and the detail of the reconstructed image efficiently preserved. As a global optimal algorithm, OFC not only helps LBG eliminate the local minima, but also effectively compensates for the arbitrary interference in hard clustering problem. Soft clustering of the domain blocks allows classified searches instead of global ones and takes less coding time, and therefore clearly outperforms to the classic method relying on reduction of the size of the domain pool by classification. In computer simulation, OFC-based algorithm for the fractal coding scheme achieved excellent performance. For some standard and sequential medical images, the results denoted that the encoding speed was improved by about 5 folds without affecting the signal-to-noise ratio and compression ratio, and the quality of the reconstructed image could be better retained.

[Automatic feature extraction and new method for retrieval from CT image database].

OBJECTIVE: To propose a new method for content-based retrieval from medical CT image database on the basis of automatically extracted features of the images. METHODS: An automatic feature extraction method is proposed based on expectation-maximization algorithm. A CT image is represented by a set of regions, each of which is characterized by a fuzzy regional feature vector reflecting the grey level, texture, shape, and the cumulative distribution histogram feature of the region of interest (ROI) to efficiently describe the difference between the ROIs. RESULTS: Compared with the submitted query image, the target images were retrieved in the order of similarity calculated by the proposed similarity measures. CONCLUSION: The proposed technique for CT image retrieval is suitable for clinical application, with greater precision and efficiency for retrieval than the conventional methods.