Synergistic pathogenicity by coinfection and sequential infection with JXA1-like HP-PRRSV and PCV2d in PCV2 antibody-positive post-weaned pigs.

Porcine reproductive and respiratory syndrome virus (PRRSV) and Porcine circovirus (PCV) are two important pathogens, which caused respiratory disease in pigs. PRRSV and PCV2 had caused great economic losses to the pig industry. Pigs coinfection with PCV2 and PRRSV were common in the clinic, PCV2 antibodies can be detected in most of the pigs. PCV2d and HP-PRRSV(JXA1-like) were two major viruses circulating in the pigs in China. In this study, HP-PRRSV (JXA1-like) and PCV2d were used to coinfect and (or) sequential infect 5-week-old weaned PCV2-antibody positive pigs and the clinical indications, pathological, virus load, and specific antibodies of the challenged post-weaned piglets were evaluated. Thirty 5-week-old post-weaned pigs were divided into six groups infected with PBS, PCV2, PRRSV, PCV2-PRRSV, PRRSV-PCV2, and Co-PRRSV-PCV2 according to the PCV2 specific antibodies. Pigs infected with PRRSV can experience diarrhea, increased body temperature, weight loss, and even death. The pigs in the PRRSV infected group and PRRSV-PCV2 infected group showed severe clinical symptoms, high mortality, and low average daily gain. The main pathological changes were widening of the lung interstitium, lung adhesion, and so on. The PRRSV-PCV2 infected group showed high levels of TNF-α and IL-2. In conclusion, PRRSV and PRRSV-PCV2 sequential infected pigs showed most pathogenic signs, and PCV2-PRRSV sequential infected pigs showed less pathogenicity than pigs of PCV2 and PRRSV coinfection and PRRSV monoinfection from day 10-14, partially suppressing the cytokine storm produced by PRRSV.

Characterization of a new NSP2-deletion NADC34-Like Porcine Reproductive and Respiratory Syndrome Virus in China.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the causative agent of Porcine Reproductive and Respiratory Syndrome (PRRS), which has caused huge economic losses to the pig industry worldwide. PRRSV NADC34-Like PRRSV 2020-Acheng-1 strain, which caused high morbidity and high mortality were isolated from dead piglets (high-throughput sequencing to show that only PRRSV and TGEV) on a farm in northeastern China. The full-length genome sequence of 2020-Acheng-1 shares 95.6% nucleotide homology with NADC34 PRRSV without any gene insertion, but has a unique 17 amino acid (469aa to 486aa) deletion in Nsp2 compared with all NADC34-Like strains in NCBI and there are unique 100 amino acid deletions. In addition, difference degree of changes in signal peptide, trans-membrane region (TM), main neutralizing epitope (PNE), non-neutralizing epitope and N-glycosylation site were observed in GP5 of 2020-Acheng-1 and other PRRSV-2 strains, we only found a change in the fifteenth amino acid of signal peptide of in GP5 of 2020-Acheng-1 with NADC34 strains. Recombination analysis showed that 2020-Acheng-1 strain did not have any recombination events with representative PRRSV-2 strains in China. This study provided valuable evidence for understanding the role of NADC34-Like strain that impact on pathogenicity.

Codon Usage for Genetic Diversity, and Evolutionary Dynamics of Novel Porcine Parvoviruses 2 through 7 (PPV2-PPV7).

Porcine parvovirus (PPV) is the main pathogen of reproductive disorders. In recent years, a new type of porcine parvovirus has been discovered and named porcine parvovirus 2 to 7 (PPV2-PPV7), and it is associated with porcine circovirus type 2 in pigs. Codon usage patterns and their effects on the evolution and host adaptation of different PPV sub-types are still largely unknown. Here, we define six main sub-types based on the Bayesian method of structural proteins of each sub-type of PPV, including PPV2, PPV3, PPV4, PPV5, PPV6, and PPV7, which show different degrees of codon usage preferences. The effective number of codons (ENC) indicates that all PPV sub-types have low codon bias. According to the codon adaptation index (CAI), PPV3 and PPV7 have the highest similarity with the host, which is related to the main popular tendency of the host in the field; according to the frequency of optimal codons (FOP), PPV7 has the highest frequency of optimal codons, indicating the most frequently used codons in its genes; and according to the relative codon deoptimization index (RCDI), PPV3 has a higher degree. Therefore, it is determined that mutational stress has a certain impact on the codon usage preference of PPV genes, and natural selection plays a very decisive and dominant role in the codon usage pattern. Our research provides a new perspective on the evolution of porcine parvovirus (PPV) and may help provide a new method for future research on the origin, evolutionary model, and host adaptation of PPV.

Synergistic Pathogenicity by Coinfection and Sequential Infection with NADC30-like PRRSV and PCV2 in Post-Weaned Pigs.

Porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus (PCVs) are two major viruses that affect pigs. Coinfections between PRRSV and PCV2 are frequently reported in most outbreaks, with clinical presentations involving dyspnea, fever, reduced feed intake, weight loss, and death in fattening pigs. The NADC30-like PRRSV and PCV2d are the main circulating virus strains found in China. This study determines the impact of NADC30-like PRRSV and PCV2d mono-infection and coinfection on the immune system, organ pathology, and viral shedding in five-week-old post-weaned pigs. Pigs were randomly divided into six groups: PBS, PRRSV, PCV2, PRRSV-PCV2 coinfection (co), and PRRSV-PCV2 or PCV2-PRRSV sequential infections. Fever, dyspnea, decreased feed intake, weight loss, and pig deaths occurred in groups infected with PRRSV, Co-PRRSV-PCV2, and PRRSV-PCV2. The viral load was higher in Co-PRRSV-PCV2, PRRSV-PCV2, and PCV2-PRRSV than those mono-infected with PRRSV or PCV2. Additionally, cytokines (IFN-γ, TNF-α, IL-4, and IL-10) produced by pigs under Co-PRRSV-PCV2 and PRRSV-PCV2 groups were more intense than the other groups. Necropsy findings showed hemorrhage, emphysema, and pulmonary adhesions in the lungs of pigs infected with PRRSV. Smaller alveoli and widened lung interstitium were found in the Co-PRRSV-PCV2 and PRRSV-PCV2 groups. In conclusion, PRRSV and PCV2 coinfection and sequential infection significantly increased viral pathogenicity and cytokine responses, resulting in severe clinical signs, lung pathology, and death.

Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms.

PURPOSE: A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations. METHODS: The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass-conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask. RESULTS: The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (+/- 0.160) and 0.540 (+/- 0.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation. CONCLUSIONS: The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.

Robust learning-based parsing and annotation of medical radiographs.

In this paper, we propose a learning-based algorithm for automatic medical image annotation based on robust aggregation of learned local appearance cues, achieving high accuracy and robustness against severe diseases, imaging artifacts, occlusion, or missing data. The algorithm starts with a number of landmark detectors to collect local appearance cues throughout the image, which are subsequently verified by a group of learned sparse spatial configuration models. In most cases, a decision could already be made at this stage by simply aggregating the verified detections. For the remaining cases, an additional global appearance filtering step is employed to provide complementary information to make the final decision. This approach is evaluated on a large-scale chest radiograph view identification task, demonstrating a very high accuracy ( > 99.9%) for a posteroanterior/anteroposterior (PA-AP) and lateral view position identification task, compared with the recently reported large-scale result of only 98.2% (Luo, , 2006). Our approach also achieved the best accuracies for a three-class and a multiclass radiograph annotation task, when compared with other state of the art algorithms. Our algorithm was used to enhance advanced image visualization workflows by enabling content-sensitive hanging-protocols and auto-invocation of a computer aided detection algorithm for identified PA-AP chest images. Finally, we show that the same methodology could be utilized for several image parsing applications including anatomy/organ region of interest prediction and optimized image visualization.

Multi-level ground glass nodule detection and segmentation in CT lung images.

Early detection of Ground Glass Nodule (GGN) in lung Computed Tomography (CT) images is important for lung cancer prognosis. Due to its indistinct boundaries, manual detection and segmentation of GGN is labor-intensive and problematic. In this paper, we propose a novel multi-level learning-based framework for automatic detection and segmentation of GGN in lung CT images. Our main contributions are: firstly, a multi-level statistical learning-based approach that seamlessly integrates segmentation and detection to improve the overall accuracy for GGN detection (in a subvolume). The classification is done at two levels, both voxel-level and object-level. The algorithm starts with a three-phase voxel-level classification step, using volumetric features computed per voxel to generate a GGN class-conditional probability map. GGN candidates are then extracted from this probability map by integrating prior knowledge of shape and location, and the GGN object-level classifier is used to determine the occurrence of the GGN. Secondly, an extensive set of volumetric features are used to capture the GGN appearance. Finally, to our best knowledge, the GGN dataset used for experiments is an order of magnitude larger than previous work. The effectiveness of our method is demonstrated on a dataset of 1100 subvolumes (100 containing GGNs) extracted from about 200 subjects.