Successful neoadjuvant chemotherapy plus sintilimab for locally advanced cervical cancer: case series and review of the literature.

BACKGROUND: The locally advanced cervical cancer (LACC) of FIGO stage IB3-IIA2 is characterized by large local mass, poor prognosis and survival rate. Tumor response to neoadjuvant chemotherapy for LACC, utilized as a surrogate endpoint, is urgently needed to improve. Given that the antitumor immune response can be suppressed by programed death-1 axis, the treatment paradigm of neoadjuvant chemotherapy combined with immunotherapy has been explored as one of the prognostic treatments in a variety of solid carcinoma. So far, the application of sintilimab, a domestic immune checkpoint inhibitor, combined with neoadjuvant chemotherapy is still limited in LACC, especially in large lesions. CASE DESCRIPTION: We present three postmenopausal women diagnosed with FIGO stage IB3-IIA2 cervical squamous cell carcinoma with lesions larger than 5 cm. Demographic, clinical, histopathological, laboratory and imaging data were record. At the completion of the neoadjuvant therapy with paclitaxel plus carboplatin combined with sintilimab, all patients underwent hysterectomy. After neoadjuvant treatment, a pathologic complete response in case 1 and partial responses in case 2 and case 3 were achieved, and neither patient showed any relapse during the follow-up period of 16 to 22 months. CONCLUSIONS: This report provide evidence to support the combination of sintilimab with neoadjuvant chemotherapy in cervical cancer, which has yet to be validated in prospective studies. More clinical data are needed to verify the effectiveness of the combined regimens. This literature review also collected studies involving potential predictors of response to NACT and immunotherapy, which would be helpful in stratifying patients for future trials.

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexistence with other lesions/hemorrhages, remains unexplored. In this paper, we, for the first time, focus on cerebral ventricle segmentation with the presence of intra-ventricular hemorrhages (IVH). To overcome the occlusions formed by IVH, we propose a symmetry-aware deep learning approach inspired by contrastive self-supervised learning. Specifically, for each slice, we jointly employ the raw slice and the horizontally flipped slice as inputs and penalize the consistency loss between the corresponding segmentation maps in addition to their segmentation losses. In this way, the symmetry of cerebral ventricles is enforced to eliminate the occlusions brought by IVH. Extensive experimental results show that the proposed symmetry-aware deep learning approach achieves consistent performance improvements for ventricle segmentation in both normal (i.e. without IVH) and challenging cases (i.e. with IVH). Through evaluation of multiple backbone networks, we demonstrate the architecture-independence of the proposed approach for performance improvements. Moreover, we re-design an end-to-end version of symmetry-aware deep learning, making it more extendable to other approaches for brain-related analysis.

Fracture R-CNN: An anchor-efficient anti-interference framework for skull fracture detection in CT images.

BACKGROUND: Skull fracture, as a common traumatic brain injury, can lead to multiple complications including bleeding, leaking of cerebrospinal fluid, infection, and seizures. Automatic skull fracture detection (SFD) is of great importance, especially in emergency medicine. PURPOSE: Existing algorithms for SFD, developed based on hand-crafted features, suffer from low detection accuracy due to poor generalizability to unseen samples. Deploying deep detectors designed for natural images like Faster Region-based Convolutional Neural Network (R-CNN) for SFD can be helpful but are of high redundancy and with nonnegligible false detections due to the cranial suture and skull base interference. Therefore, we, for the first time, propose an anchor-efficient anti-interference deep learning framework named Fracture R-CNN for accurate SFD with low computational cost. METHODS: The proposed Fracture R-CNN is developed by incorporating the prior knowledge utilized in clinical diagnosis into the original Faster R-CNN. Specifically, based on the distributions of skull fractures, we first propose an adaptive anchoring region proposal network (AA-RPN) to generate proposals for diverse-scale fractures with low computational complexity. Then, based on the prior knowledge that cranial sutures exist in the junctions of bones and usually contain sclerotic margins, we design an anti-interference head (A-Head) network to eliminate the cranial suture interference for better SFD detection. In addition, to further enhance the anti-interference ability of the proposed A-Head, a difficulty-balanced weighted loss function is proposed to emphasize more on distinguishing the interference areas from the skull base and the cranial sutures during training. RESULTS: Experimental results demonstrate that the proposed Fracture R-CNN outperforms the current state-of-the-art (SOTA) deep detectors for SFD with a higher recall and fewer false detections. Compared to Faster R-CNN, the proposed Fracture R-CNN improves the average precision (AP) by 11.74% and the free-response receiver operating characteristic (FROC) score by 11.08%. Through validating on various backbones, we further demonstrate the architecture independence of Fracture R-CNN, making it extendable to other detection applications. CONCLUSIONS: As the customized deep learning-based framework for SFD, Fracture R-CNN can effectively overcome the unique challenges in SFD with less computational cost, leading to a better detection performance compared to the SOTA deep detectors. Moreover, we believe the prior knowledge explored for Fracture R-CNN would shed new light on future deep learning approaches for SFD.

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associated diseases. However, due to irregular shapes and extremely low contrast of PHE on CT images, manually annotating PHE in pixel-wise is time-consuming and labour intensive even for experienced experts, which makes it almost infeasible to deploy current supervised deep learning approaches for automated PHE segmentation. How to develop annotation-efficient deep learning to achieve accurate PHE segmentation is an open problem. In this paper, we, for the first time, propose a cross-task supervised framework by introducing slice-level PHE labels and pixel-wise SICH annotations, which are more accessible in clinical scenarios compared to pixel-wise PHE annotations. Specifically, we first train a multi-level classifier based on slice-level PHE labels to produce high-quality class activation maps (CAMs) as pseudo PHE annotations. Then, we train a deep learning model to produce accurate PHE segmentation by iteratively refining the pseudo annotations via an uncertainty-aware corrective training strategy for noise removal and a distance-aware loss for background compression. Experimental results demonstrate that, the proposed framework achieves a comparative performance with the fully supervised methods on PHE segmentation, and largely improves the baseline performance where only pseudo PHE labels are used for training. We believe the findings from this study of using cross-task supervision for annotation-efficient deep learning can be applied to other medical imaging applications.

Pointwise encoding time reduction with radial acquisition in subtraction-based magnetic resonance angiography to assess saccular unruptured intracranial aneurysms at 3 Tesla.

PURPOSE: To investigate the clinical utility of pointwise encoding time reduction with radial acquisition in subtraction-based magnetic resonance angiography (PETRA-MRA) and time-of-flight magnetic resonance angiography (TOF-MRA) to evaluate saccular unruptured intracranial aneurysms (UIAs). METHODS: A total of 49 patients with 54 TOF-MRA-identified saccular UIAs were enrolled. The morphologic parameters, contrast-to-noise-ratios (CNRs), and sharpness of aneurysms were measured using PETRA-MRA and TOF-MRA. Two radiologists independently evaluated subjective image scores, focusing on aneurysm signal homogeneities and sharpness depictions using a 4-point scale: 4, excellent; 3, good; 2, poor; 1, not assessable. PETRA-MRA and TOF-MRA acoustic noises were measured. RESULTS: All aneurysms were detected with PETRA-MRA. The morphologic parameters of 15 patients evaluated with PETRA-MRA were more closely correlated with those receiving computed tomography angiography over those receiving TOF-MRA. No significant differences between PETRA-MRA and TOF-MRA parameters were seen in the 54 UIAs (p > 0.10), excluding those with inflow angles (p < 0.05). In four patients with inflow angles on PETRA-MRA, the angles were more closely related to those of digital subtraction angiography than those of TOF-MRA. CNRs between TOF-MRA and PETRA-MRA were comparable (p = 0.068), and PETRA-MRA sharpness values and subjective image scores were significantly higher than those of TOF-MRA (p < 0.001). Inter-observer agreements were excellent for both PETRA-MRA and TOF-MRA (intraclass correlation coefficients were 0.90 and 0.97, respectively). The acoustic noise levels of PETRA-MRA were much lower than those of TOF-MRA (59 vs.73 dB, p < 0.01). CONCLUSIONS: PETRA-MRA, with better visualization of aneurysms and lower acoustic noise levels than TOF-MRA, showed a superior diagnostic performance for depicting saccular UIAs.

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

Clinical evaluation of subtracted pointwise encoding time reduction with radial acquisition-based magnetic resonance angiography compared to 3D time-of-flight magnetic resonance angiography for improved flow dephasing at 3 Tesla.

OBJECTIVE: Flow dephasing artifacts within intracranial internal carotid artery (ICA) have been problematic for 3D time-of-flight magnetic resonance angiography (3D-TOF-MRA). This study aimed to evaluate pointwise encoding time reduction with radial acquisition subtraction-based MR angiography (PETRA-MRA) for decreasing flow dephasing artifacts compared to 3D-TOF-MRA in intracranial segments of ICA at 3 T. METHODS: Sixty healthy participants and seven patients with intracranial ICA aneurysms were enrolled to undergo 3D-TOF-MRA and PETRA-MRA. Two radiologists each evaluated the image quality of healthy participants using a 4-point scale (1: the best and 4: the worst). Quantitative analysis of the extent of homogeneity in signal intensity within the ICA and intracranial aneurysms was conducted using a parameter d: the higher the d value, the greater the signal homogeneity. Wilcoxon signed rank test, Chi-square test and the weighted kappa (κ) statistic were used for statistical analyses. RESULTS: The image quality of PETRA-MRA with an overall score of 1.35 ± 0.53 was significantly better than that obtained with 3D-TOF-MRA, with an overall score of 3.50 ± 0.62 (Z = -9.56, p < 0.001). The parameter d of PETRA-MRA was higher than that of 3D-TOF-MRA for both 60 healthy participants (0.97 ± 0.05, 0.87 ± 0.11; z = -13.21, p < 0.001) and 7 patients with intracranial aneurysms (0.81 ± 0.18, 0.74 ± 0.16; z = -2.37, p = 0.018). CONCLUSION: Compared with conventional 3D-TOF-MRA, PETRA-MRA remarkably improved the image quality with reduced flow dephasing artifacts in segments of intracranial ICA.

Novel ECHS1 mutations in Leigh syndrome identified by whole-exome sequencing in five Chinese families: case report.

BACKGROUND: Short-chain enoyl-CoA hydratase deficiency (ECHS1D), also known as ECHS1 deficiency, is a rare inborn metabolic disorder with clinical presentations characterized by Leigh syndrome (LS). Thirty-four different pathogenic mutations have been identified from over 40 patients to date. CASE PRESENTATION: Here, we report five Chinese patients with clinical syndromes typified as LS. Despite different initial symptoms, all patients presented developmental regression, dystonia, common radiological features such as symmetrical bilateral brain abnormalities, and similar metabolic results such as elevated plasma lactate and 2,3-dihydroxy-2-methylbutyrate. Utilizing whole-exome sequencing (WES), we identified eight distinct variants in ECHS1, with six novel variants, and the remaining two variants have been previously reported. Interestingly, one of the six novel variants, c.463G > A (p.Gly155Ser), was detected in three patients from unrelated families, suggesting a potential founder effect already described for a few mutations in LS. Incorporating both genetic analysis and medical results, including magnetic resonance imaging (MRI), electroencephalography (EEG), and biochemical testing, our study enriched the mutation spectrum of the ECHS1 gene and confirmed the phenotypic presentations of LS. CONCLUSIONS: The severity of ECHS1 deficiency seems to vary. It was affected by both genetics and external environmental factors that lead to increased metabolism. Our study enriched the mutation spectrum of the ECHS1 gene, confirmed the phenotypic presentations, and highlighted the importance of the valine catabolic pathway in Leigh syndrome. Further studies are required to examine the potential founder mutation c.463G > A (p.Gly155Ser) and the role of ECHS1 in relevant pathways.

Ψ-Net: Focusing on the border areas of intracerebral hemorrhage on CT images.

BACKGROUND AND OBJECTIVE: The volume of the intracerebral hemorrhage (ICH) obtained from CT scans is essential for quantification and treatment planning. However,a fast and accurate volume acquisition brings great challenges. On the one hand, it is both time consuming and operator dependent for manual segmentation, which is the gold standard for volume estimation. On the other hand, low contrast to normal tissues, irregular shapes and distributions of the hemorrhage make the existing automatic segmentation methods hard to achieve satisfactory performance. METHOD: To solve above problems, a CNN-based architecture is proposed in this work, consisting of a novel model, which is named as Ψ-Net and a multi-level training strategy. In the structure of Ψ-Net, a self-attention block and a contextual-attention block is designed to suppresses the irrelevant information and segment border areas of the hemorrhage more finely. Further, an multi-level training strategy is put forward to facilitate the training process. By adding the slice-level learning and a weighted loss, the multi-level training strategy effectively alleviates the problems of vanishing gradient and the class imbalance. The proposed training strategy could be applied to most of the segmentation networks, especially for complex models and on small datasets. RESULTS: The proposed architecture is evaluated on a spontaneous ICH dataset and a traumatic ICH dataset. Compared to the previous works on the ICH sementation, the proposed architecture obtains the state-of-the-art performance(Dice of 0.950) on the spontaneous ICH, and comparable results(Dice of 0.895) with the best method on the traumatic ICH. On the other hand, the time consumption of the proposed architecture is much less than the previous methods on both training and inference. Morever, experiment results on various of models prove the universality of the multi-level training strategy. CONCLUSIONS: This study proposed a novel CNN-based architecture, Ψ-Net with multi-level training strategy. It takes less time for training and achives superior performance than previous ICH segmentaion methods.

Environmental remodeling of human gut microbiota and antibiotic resistome in livestock farms.

Anthropogenic environments have been implicated in enrichment and exchange of antibiotic resistance genes and bacteria. Here we study the impact of confined and controlled swine farm environments on temporal changes in the gut microbiome and resistome of veterinary students with occupational exposure for 3 months. By analyzing 16S rRNA and whole metagenome shotgun sequencing data in tandem with culture-based methods, we show that farm exposure shapes the gut microbiome of students, resulting in enrichment of potentially pathogenic taxa and antimicrobial resistance genes. Comparison of students' gut microbiomes and resistomes to farm workers' and environmental samples revealed extensive sharing of resistance genes and bacteria following exposure and after three months of their visit. Notably, antibiotic resistance genes were found in similar genetic contexts in student samples and farm environmental samples. Dynamic Bayesian network modeling predicted that the observed changes partially reverse over a 4-6 month period. Our results indicate that acute changes in a human's living environment can persistently shape their gut microbiota and antibiotic resistome.

Universal and Naked-Eye Gene Detection Platform Based on the Clustered Regularly Interspaced Short Palindromic Repeats/Cas12a/13a System.

Gold-nanoparticles-based colorimetric assay is an attractive detection format, but is limited by the tedious and ineffective posthybridization manipulations for genomic analysis. Here, we present a new design for a colorimetric gene-sensing platform based on the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system. In this strategy, programmable recognition of DNA by Cas12a/crRNA and RNA by Cas13a/crRNA with a complementary target activates the trans-ssDNA or -ssRNA cleavage. Target-induced trans-ssDNA or ssRNA cleavage triggers an aggregation behavior change for the designed AuNPs-DNA probes pair, enabling the completion of naked-eye gene detection (transgenic rice, African swine fever virus, and miRNAs as the models) within 1 h. This platform is also showing promise as a fast and inexpensive tool for bacteria identification using 16S rDNA or 16S rRNA. A CRISPR/Cas-based colorimetric platform shows superior characteristics, such as probe universality, compatibility with isothermal reaction conditions, on-site detection capability, and high sensitivity, thus, demonstrating its use as a robust next-generation gene detection platform.

Canonical Transient Receptor Potential Channel 3 Contributes to Febrile Seizure Inducing Neuronal Cell Death and Neuroinflammation.

Febrile seizure (FS) counts as the most common seizures symptom in children undergoing recurrent seizures, posing a high risk to developing subsequent temporal lobe epilepsy. Canonical transient receptor potential channel (TRPC) members are identified as the FS-related genes in hyperthermia prone rats. However, the role of TRPC3 in hyperthermia-induced FS rats remains unclear. In the present study, we investigated whether TRPC3 functionally contributes to the development of FSs. Elevated TRPC3 mRNA and protein levels was detected in hyperthermia-induced FS rats and rat hippocampal neuron cells. The specific inhibitor of TRPC3, Pyr3, remarkably attenuated the susceptibility and severity of seizures, neuronal cell death, and neuroinflammation in FS rats. Conversely, NCX3 activation was apparently suppressed in rats subjected to recurrent FS and rat hippocampal neuron cells. The expression of NCX3 was up-regulated after TRPC3 inhibition in vivo and in vitro. Furthermore, an interaction between TRPC3 and NCX3 was detected by co-immunoprecipitation. Inhibition of TRPC3 suppressed intracellular Ca2+ levels in hyperthermia-treated hippocampal neuronal cells. In conclusion, our findings supported that TRPC3 functions as a critical regulator of seizure susceptibility and targeting TRPC3 may be a new therapeutic strategy for FS.

Inhibition of proanthocyanidin A2 on porcine reproductive and respiratory syndrome virus replication in vitro.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a widely prevalent and endemic swine pathogen that causes significant economic losses for the global pig industry annually. Currently, the most prevalent strategy for PRRSV control remains the prevention of virus transmission, with highly effective therapeutic agents and vaccines still lacking. Proanthocyanidin A2 (PA2) belongs to the family of tea polyphenols, which have been reported to exhibit a range of biological activities including anti-oxidative, cardio-protective, anti-tumoural, anti-bacterial, anti-viral, and anti-inflammatory effects in vitro as well as in vivo. Here, we demonstrate that PA2 exhibits potent anti-viral activity against PRRSV infection in Marc-145 cells. Similar inhibitory effects were also found in porcine alveolar macrophages, the primary target cell type of PRRSV infection in pigs in vivo. For traditional type II PRRSV CH-1a strain and high pathogenic GD-XH strain and GD-HD strain, PA2 exhibited broad-spectrum and comparable inhibitory activities in vitro with EC50 ranging from 2.2 to 3.2 µg/ml. Treatment of PRRSV-infected Marc-145 cells with PA2 significantly inhibited viral RNA synthesis, viral protein expression and progeny virus production in a dose-dependent manner. In addition, PA2 treatment reduced gene expressions of cytokines (TNF-α, IFN-α, IL-1ß and IL-6) induced by PRRSV infection in PAMs. Mechanistically, PA2 inhibited PRRSV replication by targeting multiple pathways including blockade of viral entry and progeny virus release. Altogether, our findings suggest that PA2 has the potential to serve as a novel prophylactic and therapeutic strategies against PRRSV infection.

Complex dissemination of the diversified mcr-1-harbouring plasmids in Escherichia coli of different sequence types.

The emergence of the mobilized colistin resistance gene, representing a novel mechanism for bacterial drug resistance, challenges the last resort against the severe infections by Gram-negative bacteria with multi-drug resistances. Very recently, we showed the diversity in the mcr-1-carrying plasmid reservoirs from the gut microbiota. Here, we reported that a similar but more complex scenario is present in the healthy swine populations, Southern China, 2016. Amongst the 1026 pieces of Escherichia coli isolates from 3 different pig farms, 302 E. coli isolates were determined to be positive for the mcr-1 gene (30%, 302/1026). Multi-locus sequence typing assigned no less than 11 kinds of sequence types including one novel Sequence Type to these mcr-1-positive strains. PCR analyses combined with the direct DNA sequencing revealed unexpected complexity of the mcr-1-harbouring plasmids whose backbones are at least grouped into 6 types four of which are new. Transcriptional analyses showed that the mcr-1 promoter of different origins exhibits similar activity. It seems likely that complex dissemination of the diversified mcr-1-bearing plasmids occurs amongst the various ST E. coli inhabiting the healthy swine populations, in Southern China.

Deoxynivalenol inhibits proliferation and induces apoptosis in human umbilical vein endothelial cells.

Deoxynivalenol (DON) is a stable mycotoxins found in cereals infected by certain fungal species and causes adverse health effects in animals and human such as vomiting, diarrhea and reproductive toxicity. In this study, we investigated the toxic and apoptotic effects of DON in human umbilical vein endothelial cells (HUVECs), a good model for studying inflammation. The results show that DON significantly inhibited the viability of HUVECs. DON could also inhibit the proliferation of HUVECs through G2/M phase arrest in cell cycle progression. Moreover, oxidative stress induced by DON was indicated by observations of increased levels of reactive oxygen species (ROS). In addition, DON also causes mitochondrial damage by decreasing the mitochondrial membrane potential and inducing apoptosis by up-regulation of apoptosis-related genes like caspase-3, caspase-9, and Bax genes, and down-regulation of Bcl-2 gene. These results together suggest that DON could induce cell cycle arrest, oxidative stress, and apoptosis in HUVECs.

Evaluation of deoxynivalenol-induced toxic effects on DF-1 cells in vitro: cell-cycle arrest, oxidative stress, and apoptosis.

Deoxynivalenol (DON) is one of the most common mycotoxin contaminants of raw and processed cereal food. Lymphoid cells and fibroblasts are specified to be the most DON-sensitive cell types. In this study, we investigated the toxic effects of DON in chicken embryo fibroblast DF-1 cells. The results showed that DON significantly inhibited DF-1 cell viability in both a time- and concentration-dependent manner. DON could also inhibit the proliferation of DF-1 cells through G2/M phase arrest in the cell cycle progression. Moreover, oxidative stress induced by DON was indicated by increased levels of reactive oxygen species (ROS), malondialdehyde (MDA), and decreased levels of glutathione (GSH) and superoxide dismutase (SOD). In addition, DON could also cause mitochondrial damage by decreasing the mitochondrial membrane potential and induce apoptosis accompanied with the up-regulation of apoptosis-related genes including Caspase-3, Caspase-8, Caspase-9, and AIFM1. These results suggested that DON could cause cell cycle arrest, oxidative stress, and apoptosis in DF-1 cells.

Gene expression profiling analysis of deoxynivalenol-induced inhibition of mouse thymic epithelial cell proliferation.

Deoxynivalenol (DON) is a mycotoxin produced as a secondary metabolite by fungal species. It has been shown that DON has serious toxic effects on many kinds of immune cells. However, the toxic effects on thymic epithelial cells were poorly understood. The purpose of this study is to investigate the gene expression differences for the DON-induced inhibition on the proliferation of mouse thymic epithelial cell line 1 (MTEC1). After the experiments of cell viability, morphological investigation and cell cycle analysis, microarray analysis was carried out. The differentially expressed genes belong to a variety of functional categories, including genes involved in metabolic process, cell cycle, oxidation-reduction process and apoptosis. Our results provide molecular insights into the gene expression differences of DON-induced toxic effects and suggest that p53 signaling pathway may play an important role in the inhibition of MTEC1 cell proliferation.

Characterization of a wild rabies virus isolate of porcine origin in China.

Rabies virus (RABV) that circulates worldwide in a variety of mammals can cause fatal encephalomyelitis. GD-SH-01, a street rabies virus, was isolated from a rabid pig in China. We investigated the pathogenicity of GD-SH-01 in suckling and adult mice, and compared the susceptibility of NA and BHK-21 cells in the culture to infection by GD-SH-01 and CVS-24. The complete GD-SH-01 genome sequence was determined and compared with known RABV wild strains to understand the mutations and genetic diversity that allow RABV to spread and adapt in new hosts, such as pigs. Our results suggest that GD-SH-01 possesses the characteristics of a virulent strain in Southern China and shows higher pathogenicity index than that of CVS-24 regardless of its lower level of replication in mouse brain. Up to 47 unique nucleotide substitutions were found in the genome, including five missense mutations. These data provide useful information for further understanding the transmission mechanism and the genetic variation of RABV in dissimilar hosts.

Complete genome sequence of a highly virulent rabies virus isolated from a rabid pig in south China.

A virulent rabies virus (RABV) strain, GD-SH-01, was isolated from brain tissue of a rabid pig in China. This report describes the first complete genome sequence of a swine-origin RABV strain, and this information will provide important insights into the transmission cycle and genetic diversity of RABV from different hosts in China.

Complete genome sequence of a highly prevalent isolate of porcine epidemic diarrhea virus in South China.

A widespread porcine epidemic diarrhea virus (PEDV) occurred in southern China during 2010 to 2012. A virulent field PEDV strain, GD-B, was isolated from a sucking piglet suffering from severe diarrhea in Guangdong, China. We sequenced and analyzed the complete genome of strain GD-B, which will promote a better understanding of the molecular epidemiology and genetic diversity of PEDV field isolates in southern China.