Elevated of NDUFA4L2 expression in colon adenocarcinoma is correlated with an unfavorable prognosis and increased immune cell infiltration.

Background: Colon adenocarcinoma (COAD) is a prevalent malignancy worldwide, yet, its underlying pathogenesis and genetic characteristics are still unclear. Previous studies have suggested that NADH dehydrogenase 1 alpha subcomplex subunit 4-like 2 (NDUFA4L2) may affect tumor progression across various cancers. However, this effect on COAD has rarely been reported. Thus, this study investigated NDUFA4L2's prognostic and diagnostic relevance and explored its potential connection with immune cell infiltration in COAD. Methods: To achieve this, RNA sequencing data from Cancer Genome Atlas (TCGA) was analyzed to assess NDUFA4L2's prognostic value in COAD, and factors relevant to the prognosis of COAD, including NDUFA4L2, were scrutinized using Kaplan-Meier analyses as well as univariate and multivariate Cox regression. A nomogram model was created to project prognosis based on the results of multivariate Cox analysis. Furthermore, gene set enrichment analysis (GSEA) was employed to pinpoint key NDUFA4L2-related pathways, and single-sample GSEA (ssGSEA) on TCGA data was employed to investigate the connections of NDUFA4L2 with cancer immune infiltrations. Results: Our findings revealed significant associations of high NDUFA4L2 expression with poor overall survival, progression-free interval, and disease-specific survival of COAD patients. GSEA indicated close links of NDUFA4L2 with several signaling pathways implicated in tumorigenesis, including extracellular matrix receptor interaction, the intestinal immune network for immunoglobulin A production, natural killer (NK) cell-mediated cytotoxicity, pathways in cancer, cell adhesion molecules, mitogen-activated protein kinase signaling pathway, Hedgehog signaling pathway, transforming growth factor beta signaling pathway, and chemokine signaling pathway. Additionally, ssGSEA identified a positive link between increased NDUFA4L2 expression and higher infiltration degree of various immune cells, such as immature dendritic cells, macrophages, NK cells and dendritic cells. Conclusions: Collectively, our findings demonstrate the association of increased NDUFA4L2 expression with adverse prognosis and heightened immune cell infiltration in COAD patients.

Automatic artery/vein classification methods for retinal blood vessel: A review.

Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research.

Multi-View Diffusion Process for Spectral Clustering and Image Retrieval.

This paper presents a novel approach to multi-view graph learning that combines weight learning and graph learning in an alternating optimization framework. Multi-view graph learning refers to the problem of constructing a unified affinity graph using heterogeneous sources of data representation, which is a popular technique in many learning systems where no prior knowledge of data distribution is available. Our approach is based on a fusion-and-diffusion strategy, in which multiple affinity graphs are fused together via a weight learning scheme based on the unsupervised graph smoothness and utilised as a consensus prior to the diffusion. We propose a novel multi-view diffusion process that learns a manifold-aware affinity graph by propagating affinities on tensor product graphs, leveraging high-order contextual information to enhance pairwise affinities. In contrast to existing multi-view graph learning approaches, our approach is not limited by the quality of initial graphs or the assumption of a latent common subspace among multiple views. Instead, our approach is able to identify the consistency among views and fuse multiple graphs adaptively. We formulate both weight learning and diffusion-based affinity learning in a unified framework and propose an alternating optimization solver that is guaranteed to converge. The proposed approach is applied to image retrieval and clustering tasks on 16 real-world datasets. Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods for both retrieval and clustering on 13 out of 16 datasets.

Invertible Residual Blocks in Deep Learning Networks.

Residual blocks have been widely used in deep learning networks. However, information may be lost in residual blocks due to the relinquishment of information in rectifier linear units (ReLUs). To address this issue, invertible residual networks have been proposed recently but are generally under strict restrictions which limit their applications. In this brief, we investigate the conditions under which a residual block is invertible. A sufficient and necessary condition is presented for the invertibility of residual blocks with one layer of ReLU inside the block. In particular, for widely used residual blocks with convolutions, we show that such residual blocks are invertible under weak conditions if the convolution is implemented with certain zero-padding methods. Inverse algorithms are also proposed, and experiments are conducted to show the effectiveness of the proposed inverse algorithms and prove the correctness of the theoretical results.

Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection.

The canonical correlation analysis (CCA) has attracted wide attention in fault detection (FD). To improve the detection performance, we propose a new joint sparse constrained CCA (JSCCCA) model that integrates the l2,0 -norm joint sparse constraints into classical CCA. The key idea is that JSCCCA can fully exploit the joint sparse structure to determine the number of extracted variables. We then develop an efficient alternating minimization algorithm using the improved iterative hard thresholding and manifold constrained gradient descent method. More importantly, we establish the convergence guarantee with detailed analysis. Finally, we provide extensive numerical studies on the simulated dataset, the benchmark Tennessee Eastman process, and a practical cylinder-piston process. In some cases, the computing time is reduced by 600 times, and the FD rate is increased by 12.62% compared with classical CCA. The results suggest that the proposed approach is efficient and fast.

Texture-aware dual domain mapping model for low-dose CT reconstruction.

BACKGROUND: Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction. PURPOSE: Most of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. METHODS: The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self-attention (SA) residual encoder & decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature-aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. RESULTS: The proposed method was validated using both the American association of physicists in medicine (AAPM)-Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration. CONCLUSIONS: Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network (TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability.

iffDetector: Inference-Aware Feature Filtering for Object Detection.

Modern convolutional neural network (CNN)-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this article, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic inference-aware feature filtering (IFF) module that can be easily combined with existing detectors, resulting in our iffDetector. Unlike conventional open-loop feature calculation approaches without feedback, the proposed IFF module performs the closed-loop feature optimization by leveraging high-level semantics to enhance the convolutional features. By applying the Fourier transform to analyze our detector, we prove that the IFF module acts as a negative feedback that can theoretically guarantee the stability of the feature learning. IFF can be fused with CNN-based object detectors in a plug-and-play manner with little computational cost overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that our iffDetector consistently outperforms state-of-the-art methods with significant margins.

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial.

BACKGROUND: Cerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP. OBJECTIVE: This paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data. METHODS: Custom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective. RESULTS: Upon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years. CONCLUSIONS: Anecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367422.

Streptococcus suis MsmK: Novel Cell Division Protein Interacting with FtsZ and Maintaining Cell Shape.

Bacteria of different shapes have adopted distinct mechanisms to faithfully coordinate morphogenesis and segregate their chromosomes prior to cell division. Despite recent focuses and advances, the mechanism of cell division in ovococci remains largely unknown. Streptococcus suis, a major zoonotic pathogen that causes problems in human health and in the global swine industry, is an elongated and ellipsoid bacterium that undergoes successive parallel splitting perpendicular to its long axis. Studies on cell cycle processes in this bacterium are limited. Here, we report that MsmK (multiple sugar metabolism protein K), an ATPase that contributes to the transport of multiple carbohydrates, has a novel role as a cell division protein in S. suis MsmK can display ATPase and GTPase activities, interact with FtsZ via the N terminus of MsmK, and promote the bundling of FtsZ protofilaments in a GTP-dependent manner in vitro Deletion of the C-terminal region or the Walker A or B motif affects the affinity between MsmK and FtsZ and decreases the ability of MsmK to promote FtsZ protofilament bundling. MsmK can form a complex with FtsZ in vivo, and its absence is not lethal but results in long chains and short, occasionally anuclear daughter cells. Superresolution microscopy revealed that the lack of MsmK in cells leads to normal septal peptidoglycan walls in mother cells but disturbed cell elongation and peripheral peptidoglycan synthesis. In summary, MsmK is a novel cell division protein that maintains cell shape and is involved in the synthesis of the peripheral cell wall.IMPORTANCE Bacterial cell division is a highly ordered process regulated in time and space and is a potential target for the development of antimicrobial drugs. Bacteria of distinct shapes depend on different cell division mechanisms, but the mechanisms used by ovococci remain largely unknown. Here, we focused on the zoonotic pathogen Streptococcus suis and identified a novel cell division protein named MsmK, which acts as an ATPase of the ATP-binding cassette-type carbohydrate transport system. MsmK has GTPase and ATPase activities. In vitro protein assays showed that MsmK interacts with FtsZ and promotes FtsZ protofilament bundling that relies on GTP. Superresolution microscopy revealed that MsmK maintains cell shape and is involved in peripheral peptidoglycan synthesis. Knowledge of the multiple functions of MsmK may broaden our understanding of known cell division processes. Further studies in this area will elucidate how bacteria can faithfully and continually multiply in a constantly changing environment.

Semisupervised Learning on Graphs With an Alternating Diffusion Process.

Graph-based semisupervised learning is of great importance in many effective learning systems, particularly in agnostic settings where no parametric information or other prior knowledge about the data distribution is available. It leverages the graph structure to propagate labels from labeled nodes to unlabeled ones. Two separate stages are usually involved: constructing an affinity graph and propagating labels on the graph for transductive inference. It is suboptimal to manage them independently, as the correlation between the affinity graph and the labels would not be fully exploited. In this article, we integrate these two stages into one unified framework by formulating the graph construction as a regularized function estimation problem, similar to label propagation. We then propose an alternating diffusion process to solve them alternately, which allows us to learn the graph and unknown labels in an iterative fashion. With the proposed framework, we can construct a dynamic graph adapted to the given and predicted labels iteratively, resulting in more accurate and robust label propagation performance. Extensive experiments on synthetic data and various real-world data have demonstrated the superiority of the proposed method compared with other state-of-the-art methods.

Automatic prostate segmentation based on fusion between deep network and variational methods.

BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE: To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS: We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity. To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS: Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS: Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models.

A Bayesian Scene-Prior-Based Deep Network Model for Face Verification.

Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face recognition/verification recently. With large labeled training datasets, the features obtained from deep learning networks can achieve higher accuracy in comparison with shallow networks. However, many reported face recognition/verification approaches rely heavily on the large size and complete representative of the training set, and most of them tend to suffer serious performance drop or even fail to work if fewer training samples per person are available. Hence, the small number of training samples may cause the deep features to vary greatly. We aim to solve this critical problem in this paper. Inspired by recent research in scene domain transfer, for a given face image, a new series of possible scenarios about this face can be deduced from the scene semantics extracted from other face individuals in a face dataset. We believe that the "scene" or background in an image, that is, samples with more different scenes for a given person, may determine the intrinsic features among the faces of the same individual. In order to validate this belief, we propose a Bayesian scene-prior-based deep learning model in this paper with the aim to extract important features from background scenes. By learning a scene model on the basis of a labeled face dataset via the Bayesian idea, the proposed method transforms a face image into new face images by referring to the given face with the learnt scene dictionary. Because the new derived faces may have similar scenes to the input face, the face-verification performance can be improved without having background variance, while the number of training samples is significantly reduced. Experiments conducted on the Labeled Faces in the Wild (LFW) dataset view #2 subset illustrated that this model can increase the verification accuracy to 99.2% by means of scenes' transfer learning (99.12% in literature with an unsupervised protocol). Meanwhile, our model can achieve 94.3% accuracy for the YouTube Faces database (DB) (93.2% in literature with an unsupervised protocol).

Functional characterization of murB-potABCD operon for polyamine uptake and peptidoglycan synthesis in Streptococcus suis.

Spermidine (Spd), spermine (Spm), and putrescine (Put), which are the most widely distributed cellular polyamines, are essential for normal growth and multiplication of both eukaryotic and prokaryotic cells. In this study, we identified the only putative polyamine transport system PotABCD in Streptococcus suis, a worldwide zoonotic Gram-positive pathogen causing lethal infections in humans and pigs. It was discovered that S. suis could uptake polyamines preferably Spd and Spm. By constructing a potA deleted mutant, we confirmed that PotABCD was responsible for polyamine uptake, and PotD bound to the protein of polyamines. The four PotABCD genes were co-transcribed with murB, a gene involved in peptidoglycan (PG) synthesis. Furthermore the roles of polyamine transport system in maintaining the PG structure were detected to understand the biological significance of this co-transcription. In contrast to the wild type, the mutant ΔpotA exhibited elongated chain length and abnormal cell division morphology. Phenotypic changes were attributed to be the up-regulation of genes involved in PG synthesis and hydrolysis in ΔpotA. Additionally, polyamines functioned not only as feedback regulators of PotA by inhibiting PotA activity but also as regulators on potABCD and genes involved in PG synthesis. This study reveals the functions of PotABCD in polyamine transport and the regulatory roles of polyamines in PG synthesis. Results provide new insights into the machineries contributing to normal growth and cell division of S. suis.

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.

The Eukaryote-Like Serine/Threonine Kinase STK Regulates the Growth and Metabolism of Zoonotic Streptococcus suis.

Like eukaryotes, bacteria express one or more serine/threonine kinases (STKs) that initiate diverse signaling networks. The STK from Streptococcus suis is encoded by a single-copy stk gene, which is crucial in stress response and virulence. To further understand the regulatory mechanism of STK in S. suis, a stk deletion strain (Δstk) and its complementary strain (CΔstk) were constructed to systematically decode STK characteristics by applying whole transcriptome RNA sequencing (RNA-Seq) and phosphoproteomic analysis. Numerous genes were differentially expressed in Δstk compared with the wild-type parental strain SC-19, including 320 up-regulated and 219 down-regulated genes. Particularly, 32 virulence-associated genes (VAGs) were significantly down-regulated in Δstk. Seven metabolic pathways relevant to bacterial central metabolism and translation are significantly repressed in Δstk. Phosphoproteomic analysis further identified 12 phosphoproteins that exhibit differential phosphorylation in Δstk. These proteins are associated with cell growth and division, glycolysis, and translation. Consistently, phenotypic assays confirmed that the Δstk strain displayed deficient growth and attenuated pathogenicity. Thus, STK is a central regulator that plays an important role in cell growth and division, as well as S. suis metabolism.

The involvement of MsmK in pathogenesis of the Streptococcus suis serotype 2.

Streptococcus suis serotype 2 (SS2) is an important swine and human pathogen that causes global economic and public health problems. Virulent S. suis strains successfully maintain high bacterial concentrations in host blood and rapidly adapt to challenging environments within hosts. Successful survival in hosts is a major factor influencing the pathogenesis of SS2. We have previously identified that SS2 colonization in mouse brain is possibly affected by the ATPase, MsmK of carbohydrate ATP-binding cassette (ABC) transporters because of carbohydrate utilization. In this study, the chain length of the msmK deletion mutant was longer than that of the wild type, and the former was significantly more susceptible than the latter when theses strains were exposed to mouse blood both in vivo and in vitro. The hemolytic activity of the mutant strain was decreased. Although the adhesion of the mutant to HEp-2 cell lines was enhanced, the deletion of msmK impaired the abilities of SS2 to resist phagocytosis and survive severe stress conditions. MsmK contributed to the survival and adaptation of SS2 in host bloodstream. Therefore, MsmK was identified as a multifunctional component that not only contributed to carbohydrate utilization but also participated in SS2 pathogenesis.

GidA, a tRNA Modification Enzyme, Contributes to the Growth, and Virulence of Streptococcus suis Serotype 2.

Glucose-inhibited division protein (GidA), is a tRNA modification enzyme functioning together with MnmE in the addition of a carboxymethylaminomethyl group to position 5 of the anticodon wobble uridine of tRNA. Here, we report a GidA homolog from a Chinese isolate SC-19 of the zoonotic Streptococcus suis serotype 2 (SS2). gidA disruption led to a defective growth, increased capsule thickness, and reduced hemolytic activity. Moreover, the gidA deletion mutant (ΔgidA) displayed reduced mortality and bacterial loads in mice, reduced ability of adhesion to and invasion in epithelial cells, and increased sensitivity to phagocytosis. The iTRAQ analysis identified 372 differentially expressed (182 up- and 190 down-regulated) proteins in ΔgidA and SC-19. Numerous DNA replication, cell division, and virulence associated proteins were downregulated, whereas many capsule synthesis enzymes were upregulated by gidA disruption. This is consistent with the phenotypes of the mutant. Thus, GidA is a translational regulator that plays an important role in the growth, cell division, capsule biosynthesis, and virulence of SS2. Our findings provide new insight into the regulatory function of GidA in bacterial pathogens.

MsmK, an ATPase, Contributes to Utilization of Multiple Carbohydrates and Host Colonization of Streptococcus suis.

Acquisition and metabolism of carbohydrates are essential for host colonization and pathogenesis of bacterial pathogens. Different bacteria can uptake different lines of carbohydrates via ABC transporters, in which ATPase subunits energize the transport though ATP hydrolysis. Some ABC transporters possess their own ATPases, while some share a common ATPase. Here we identified MsmK, an ATPase from Streptococcus suis, an emerging zoonotic bacterium causing dead infections in pigs and humans. Genetic and biochemistry studies revealed that the MsmK was responsible for the utilization of raffinose, melibiose, maltotetraose, glycogen and maltotriose. In infected mice, the msmK-deletion mutant showed significant defects of survival and colonization when compared with its parental and complementary strains. Taken together, MsmK is an ATPase that contributes to multiple carbohydrates utilization and host colonization of S. suis. This study gives new insight into our understanding of the carbohydrates utilization and its relationship to the pathogenesis of this zoonotic pathogen.

Overexpression of Porcine Beta-Defensin 2 Enhances Resistance to Actinobacillus pleuropneumoniae Infection in Pigs.

To reduce the need for antibiotics in animal production, alternative approaches are needed to control infection. We hypothesized that overexpression of native defensin genes will provide food animals with enhanced resistance to bacterial infections. In this study, recombinant porcine beta-defensin 2 (PBD-2) was overexpressed in stably transfected PK-15 porcine kidney cells. PBD-2 antibacterial activities against Actinobacillus pleuropneumoniae, an important respiratory pathogen causing porcine contagious pleuropneumonia, were evaluated on agar plates. Transgenic pigs constitutively overexpressing PBD-2 were produced by a somatic cell cloning method, and their resistance to bacterial infection was evaluated by direct or cohabitation infection with A. pleuropneumoniae. Recombinant PBD-2 peptide that was overexpressed in the PK-15 cells showed antibacterial activity against A. pleuropneumoniae. PBD-2 was overexpressed in the heart, liver, spleen, lungs, kidneys, and jejunum of the transgenic pigs, which showed significantly lower bacterial loads in the lungs and reduced lung lesions after direct or cohabitation infection with A. pleuropneumoniae. The results demonstrate that transgenic overexpression of PBD-2 in pigs confers enhanced resistance against A. pleuropneumoniae infection.

[Contrast of curative effect and complications between peripheral venous thrombolysis and catheter-direct thrombolysis for acute deep venous thrombosis in lower limbs].

OBJECTIVE: To evaluate the effects of catheter-direct thrombolysis in acute deep venous thrombosis (DVT). METHODS: A total of 86 cases were divided into 2 groups of peripheral venous thrombolysis (group A, n = 33) and catheter-direct thrombolysis (group B, n = 53). The curative effect of two groups was compared by swelling rate and vascular potency. RESULTS: No significant difference existed in swelling rate between two groups (P > 0.05). Vascular patency rates of group B was significantly better than those of group A (P < 0.01). The incidence of bleeding had no significant difference (P > 0.05) and there was no asymptomatic pulmonary embolism in two groups. CONCLUSION: Both treatments of acute DVT are effective in improving symptoms. But catheter-directed thrombolysis results in significant vascular patency rate and does not increase the risk of thrombolytic bleeding.