[Effect of transcutaneous electrical acupoint stimulation on postoperative cough in lung cancer patients undergoing video-assisted thoracoscopic surgery].

OBJECTIVE: To evaluate the effect of transcutaneous electrical acupoint stimulation (TEAS) for alleviating postoperative cough in lung cancer patients undergoing video-assisted thoracoscopic surgery. METHODS: A total of 110 patients with lung cancer undergoing video-assisted thoracoscopic surgery were randomly divided into a TEAS group (55 cases, 2 cases dropped out) and a sham-TEAS group (55 cases, 4 cases dropped out). In the TEAS group, TEAS was delivered 30 min before anesthesia and on day 1 to day 4 after operation separately, with disperse-dense wave, in frequence of 2 Hz/100 Hz. The acupoints included Feishu (BL 13), Pishu (BL 20), Shenshu (BL 23), Hegu (LI 4), Lieque (LU 7) and Taixi (KI 3) on the both sides. In the sham-TEAS group, at the same time points and same acupoints as the TEAS group, the electrode pads were attached to the acupoints, but without electric stimulation. The interventions were given 30 min each time, once daily in the two groups. The incidence of cough and the scores of visual analogue scale (VAS) for cough on the first day (T1), the third day (T2), the fifth day (T3), 1 month (T4) and 3 months (T5) after operation, as well as the scores of the Leicester cough questionnaire (LCQ) on T4 and T5 were compared between the two groups; the contents of serum C-reactive protein (CRP), interleukin 6 (IL-6) and tumor necrosis factor α (TNF-α) were detected before surgery (T0) and at T1, T2 and T3. The first flatus time, the first defecation time, the first ambulation time, the postoperative hospital day and the incidence of postoperative nausea and vomiting were compared between the two groups. RESULTS: Compared with the sham-TEAS group, the cough incidence at T3 and cough VAS scores at T1 to T5 were lower in the TEAS group (P<0.05, P<0.01), and the LCQ scores at T4 and T5 were higher (P<0.05). The serum contents of CRP, IL-6 and TNF-αat T1 to T3 in the TEAS group were lower than those of the sham-TEAS group (P<0.01). The first flatus time, the first defecation time and the first ambulation time were earlier (P<0.05, P<0.01); and the postoperative hospital day was shorter (P<0.05) and the incidence of postoperative nausea and vomiting was lower (P<0.05) in the TEAS group when compared with those of the sham-TEAS group. CONCLUSION: TEAS relieves cough in lung cancer patients undergoing video-assisted thoracoscopic surgery, improves quality of life and promotes the early postoperative recovery.

Acute stress impairs intentional memory suppression through aberrant prefrontal cortex activation in high trait ruminators.

Objective: Research shows that the effect of acute stress on intentional memory suppression could be modulated by individual differences in psychological traits. However, whether acute stress distinctly affects intentional memory suppression in high trait ruminators, a high at-risk group of stress-related disorders, and the neural correlations, remains unclear. Method: 55 healthy college students were divided into high and low trait ruminators (HTR and LTR), Following stress manipulation, a Think/No Think task assessed the memory suppression performance. Functional near-infrared spectroscopy was applied to explore the neural correlates. Psychophysiological interaction analyses were used to assess how the functional connectivity between a seed region and another brain region was modulated by tasks during memory suppression, further mediating memory suppression performance and state rumination. Results: The HTR exhibited poorer memory suppression performance than the LTR under the stress condition. Aberrant activation patterns and task-modulated functional connectivity in the dorsal prefrontal cortex (DLPFC) and superior temporal gyrus (STG) were observed only in the HTR during memory suppression under the stress condition. The effect of memory suppression performance on the state rumination of individuals was significantly mediated by the task-modulated functional connectivity between the DLPFC and STG. Conclusions: The findings could provide insights for prevention or early intervention in the development of stress-related disorders in HTR.

Pathological Priors Inspired Network for Vertebral Osteophytes Recognition.

Automatic vertebral osteophyte recognition in Digital Radiography is of great importance for the early prediction of degenerative disease but is still a challenge because of the tiny size and high inter-class similarity between normal and osteophyte vertebrae. Meanwhile, common sampling strategies applied in Convolution Neural Network could cause detailed context loss. All of these could lead to an incorrect positioning predicament. In this paper, based on important pathological priors, we define a set of potential lesions of each vertebra and propose a novel Pathological Priors Inspired Network (PPIN) to achieve accurate osteophyte recognition. PPIN comprises a backbone feature extractor integrating with a Wavelet Transform Sampling module for high-frequency detailed context extraction, a detection branch for locating all potential lesions and a classification branch for producing final osteophyte recognition. The Anatomical Map-guided Filter between two branches helps the network focus on the specific anatomical regions via the generated heatmaps of potential lesions in the detection branch to address the incorrect positioning problem. To reduce the inter-class similarity, a Bilateral Augmentation Module based on the graph relationship is proposed to imitate the clinical diagnosis process and to extract discriminative contextual information between adjacent vertebrae in the classification branch. Experiments on the two osteophytes-specific datasets collected from the public VinDr-Spine database show that the proposed PPIN achieves the best recognition performance among multitask frameworks and shows strong generalization. The results on a private dataset demonstrate the potential in clinical application. The Class Activation Maps also show the powerful localization capability of PPIN. The source codes are available in https://github.com/Phalo/PPIN.

A modified model for predicting mortality after transjugular intrahepatic portosystemic shunt: A multicentre study.

BACKGROUND AND AIMS: The transjugular intrahepatic portosystemic shunt has controversial survival benefits; thus, patient screening should be performed preoperatively. In this study, we aimed to develop a model to predict post-transjugular intrahepatic portosystemic shunt mortality to aid clinical decision making. METHODS: A total of 811 patients undergoing transjugular intrahepatic portosystemic shunt from five hospitals were divided into the training and external validation data sets. A modified prediction model of post-transjugular intrahepatic portosystemic shunt mortality (ModelMT ) was built after performing logistic regression. To verify the improved performance of ModelMT , we compared it with seven previous models, both in discrimination and calibration. Furthermore, patients were stratified into low-, medium-, high- and extremely high-risk subgroups. RESULTS: ModelMT demonstrated a satisfying predictive efficiency in both discrimination and calibration, with an area under the curve of .875 in the training set and .852 in the validation set. Compared to previous models (ALBI, BILI-PLT, MELD-Na, MOTS, FIPS, MELD, CLIF-C AD), ModelMT showed superior performance in discrimination by statistical difference in the Delong test, net reclassification improvement and integrated discrimination improvement (all p < .050). Similar results were observed in calibration. Low-, medium-, high- and extremely high-risk groups were defined by scores of ≤160, 160-180, 180-200 and >200, respectively. To facilitate future clinical application, we also built an applet for ModelMT . CONCLUSIONS: We successfully developed a predictive model with improved performance to assist in decision making for transjugular intrahepatic portosystemic shunt according to survival benefits.

3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study.

BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed. RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS. CONCLUSION: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.

Unsupervised dual-domain disentangled network for removal of rigid motion artifacts in MRI.

Motion artifacts in magnetic resonance imaging (MRI) have always been a serious issue because they can affect subsequent diagnosis and treatment. Supervised deep learning methods have been investigated for the removal of motion artifacts; however, they require paired data that are difficult to obtain in clinical settings. Although unsupervised methods are widely proposed to fully use clinical unpaired data, they generally focus on anatomical structures generated by the spatial domain while ignoring phase error (deviations or inaccuracies in phase information that are possibly caused by rigid motion artifacts during image acquisition) provided by the frequency domain. In this study, a 2D unsupervised deep learning method named unsupervised disentangled dual-domain network (UDDN) was proposed to effectively disentangle and remove unwanted rigid motion artifacts from images. In UDDN, a dual-domain encoding module was presented to capture different types of information from the spatial and frequency domains to enrich the information. Moreover, a cross-domain attention fusion module was proposed to effectively fuse information from different domains, reduce information redundancy, and improve the performance of motion artifact removal. UDDN was validated on a publicly available dataset and a clinical dataset. Qualitative and quantitative experimental results showed that our method could effectively remove motion artifacts and reconstruct image details. Moreover, the performance of UDDN surpasses that of several state-of-the-art unsupervised methods and is comparable with that of the supervised method. Therefore, our method has great potential for clinical application in MRI, such as real-time removal of rigid motion artifacts.

Semi-supervised hybrid spine network for segmentation of spine MR images.

Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases. However, segmenting the VBs and IVDs simultaneously is not trivial. Moreover, problems exist, including blurry segmentation caused by anisotropy resolution, high computational cost, inter-class similarity and intra-class variability, and data imbalances. We proposed a two-stage algorithm, named semi-supervised hybrid spine network (SSHSNet), to address these problems by achieving accurate simultaneous VB and IVD segmentation. In the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross pseudo supervision to obtain intra-slice features and coarse segmentation. In the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This model can be used to extract inter-slice information and combine the coarse segmentation and intra-slice features provided from the first stage. Moreover, a cross tri-attention module was applied to compensate for the loss of inter-slice and intra-slice information separately generated from 2D and 3D networks, thereby improving feature representation ability and achieving satisfactory segmentation results. The proposed SSHSNet was validated on a publicly available spine MR image dataset, and remarkable segmentation performance was achieved. Moreover, results show that the proposed method has great potential in dealing with the data imbalance problem. Based on previous reports, few studies have incorporated a semi-supervised learning strategy with a cross attention mechanism for spine segmentation. Therefore, the proposed method may provide a useful tool for spine segmentation and aid clinically in spinal disease diagnoses and treatments. Codes are publicly available at: https://github.com/Meiyan88/SSHSNet.

Multi-level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI.

Isocitrate dehydrogenase (IDH) is one of the most important genotypes in patients with glioma because it can affect treatment planning. Machine learning-based methods have been widely used for prediction of IDH status (denoted as IDH prediction). However, learning discriminative features for IDH prediction remains challenging because gliomas are highly heterogeneous in MRI. In this paper, we propose a multi-level feature exploration and fusion network (MFEFnet) to comprehensively explore discriminative IDH-related features and fuse different features at multiple levels for accurate IDH prediction in MRI. First, a segmentation-guided module is established by incorporating a segmentation task and is used to guide the network in exploiting features that are highly related to tumors. Second, an asymmetry magnification module is used to detect T2-FLAIR mismatch sign from image and feature levels. The T2-FLAIR mismatch-related features can be magnified from different levels to increase the power of feature representations. Finally, a dual-attention feature fusion module is introduced to fuse and exploit the relationships of different features from intra- and inter-slice feature fusion levels. The proposed MFEFnet is evaluated on a multi-center dataset and shows promising performance in an independent clinical dataset. The interpretability of the different modules is also evaluated to illustrate the effectiveness and credibility of the method. Overall, MFEFnet shows great potential for IDH prediction.

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases.

Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.

Reasoning and Tuning: Graph Attention Network for Occluded Person Re-identification.

Occluded person re-identification (re-id) aims to match occluded person images to holistic ones. Most existing works focus on matching collective-visible body parts by discarding the occluded parts. However, only preserving the collective-visible body parts causes great semantic loss for occluded images, decreasing the confidence of feature matching. On the other hand, we observe that the holistic images can provide the missing semantic information for occluded images of the same identity. Thus, compensating the occluded image with its holistic counterpart has the potential for alleviating the above limitation. In this paper, we propose a novel Reasoning and Tuning Graph Attention Network (RTGAT), which learns complete person representations of occluded images by jointly reasoning the visibility of body parts and compensating the occluded parts for the semantic loss. Specifically, we self-mine the semantic correlation between part features and the global feature to reason the visibility scores of body parts. Then we introduce the visibility scores as the graph attention, which guides Graph Convolutional Network (GCN) to fuzzily suppress the noise of occluded part features and propagate the missing semantic information from the holistic image to the occluded image. We finally learn complete person representations of occluded images for effective feature matching. Experimental results on occluded benchmarks demonstrate the superiority of our method.

Lyophilized mRNA-lipid nanoparticle vaccines with long-term stability and high antigenicity against SARS-CoV-2.

Advanced mRNA vaccines play vital roles against SARS-CoV-2. However, most current mRNA delivery platforms need to be stored at -20 °C or -70 °C due to their poor stability, which severely restricts their availability. Herein, we develop a lyophilization technique to prepare SARS-CoV-2 mRNA-lipid nanoparticle vaccines with long-term thermostability. The physiochemical properties and bioactivities of lyophilized vaccines showed no change at 25 °C over 6 months, and the lyophilized SARS-CoV-2 mRNA vaccines could elicit potent humoral and cellular immunity whether in mice, rabbits, or rhesus macaques. Furthermore, in the human trial, administration of lyophilized Omicron mRNA vaccine as a booster shot also engendered strong immunity without severe adverse events, where the titers of neutralizing antibodies against Omicron BA.1/BA.2/BA.4 were increased by at least 253-fold after a booster shot following two doses of the commercial inactivated vaccine, CoronaVac. This lyophilization platform overcomes the instability of mRNA vaccines without affecting their bioactivity and significantly improves their accessibility, particularly in remote regions.

Is metformin a possible treatment for diabetic neuropathy?

Metformin is a hypoglycemic drug widely used in the treatment of type 2 diabetes. It has been proven to have analgesic and neuroprotective effects. Metformin can reverse pain in rodents, such as diabetic neuropathic pain, neuropathic pain caused by chemotherapy drugs, inflammatory pain and pain caused by surgical incision. In clinical use, however, metformin is associated with reduced plasma vitamin B12 levels, which can further neuropathy. In rodent diabetes models, metformin plays a neuroprotective and analgesic role by activating adenosine monophosphate-activated protein kinase, clearing methylgloxal, reducing insulin resistance, and neuroinflammation. This paper also summarized the neurological adverse reactions of metformin in diabetic patients. In addition, whether metformin has sexual dimorphism needs further study.

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making.

Stress is one of the contributing factors affecting decision-making. Therefore, early stress recognition is essential to improve clinicians' decision-making performance. Functional near-infrared spectroscopy (fNIRS) has shown great potential in detecting stress. However, the majority of previous studies only used fNIRS features at the individual level for classification without considering the correlations among channels corresponding to the brain, which may provide distinguishing features. Hence, this study proposes a novel joint-channel-connectivity-based feature selection and classification algorithm for fNIRS to detect stress in decision-making. Specifically, this approach integrates feature selection and classifier modeling into a sparse model, where intra- and inter-channel regularizers are designed to explore potential correlations among channels to obtain discriminating features. In this paper, we simulated the decision-making of medical students under stress through the Trier Social Stress Test and the Balloon Analog Risk Task and recorded their cerebral hemodynamic alterations by fNIRS device. Experimental results illustrated that our method with the accuracy of 0.961 is superior to other machine learning methods. Additionally, the stress correlation and connectivity of brain regions calculated by feature selection have been confirmed in previous studies, which validates the effectiveness of our method and helps optimize the channel settings of fNIRS. This work was the first attempt to utilize a sparse model that simultaneously considers the sparsity of features and the correlation of brain regions for stress detection and obtained an admirable classification performance. Thus, the proposed model might be a useful tool for medical personnel to automatically detect stress in clinical decision-making situations.

2D-3D cascade network for glioma segmentation in multisequence MRI images using multiscale information.

BACKGROUND AND OBJECTIVE: Glioma segmentation is an important procedure for the treatment plan and follow-up evaluation of patients with glioma. UNet-based networks are widely used in medical image segmentation tasks and have achieved state-of-the-art performance. However, context information along the third dimension is ignored in 2D convolutions, whereas difference between z-axis and in-plane resolutions is large in 3D convolutions. Moreover, an original UNet structure cannot capture fine details because of the reduced resolution of feature maps near bottleneck layers. METHODS: To address these issues, a novel 2D-3D cascade network with multiscale information module is proposed for the multiclass segmentation of gliomas in multisequence MRI images. First, a 2D network is applied to fully exploit potential intra-slice features. A variational autoencoder module is incorporated into 2D DenseUNet to regularize a shared encoder, extract useful information, and represent glioma heterogeneity. Second, we integrated 3D DenseUNet with the 2D network in cascade mode to extract useful inter-slice features and alleviate the influence of large difference between z-axis and in-plane resolutions. Moreover, a multiscale information module is used in the 2D and 3D networks to further capture the fine details of gliomas. Finally, the whole 2D-3D cascade network is trained in an end-to-end manner, where the intra-slice and inter-slice features are fused and optimized jointly to take full advantage of 3D image information. RESULTS: Our method is evaluated on publicly available and clinical datasets and achieves competitive performance in these two datasets. CONCLUSIONS: These results indicate that the proposed method may be a useful tool for glioma segmentation.

Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma.

Macrovascular invasion (MaVI) is a major threat to survival in hepatocellular carcinoma (HCC), which should be treated as early as possible to ensure safety and efficacy. In this aspect, MaVI prediction can be helpful. However, MaVI prediction is difficult because of the inter-class similarity and intra-class variation of HCC in computed tomography (CT) images. Moreover, existing methods fail to include clinical priori knowledge associated with HCC, leading to incomprehensive information extraction. In this paper, we proposed a prior knowledge-aware fusion network (PKAFnet) to accurately achieve MaVI prediction in CT images. First, a perception module was presented to extract features related to tumor marginal heterogeneity in the graph domain, which contributed to rotation invariance and captured intensity variations of tumor margin. Second, a tumor segmentation network was built to obtain global information of a 3D tumor image and information associated with tumor internal heterogeneity in the image domain. Finally, multi-domain features associated with the tumor margin and tumor region were combined by using a multi-domain attentional feature fusion module. Thus, by incorporating MaVI-related prior knowledge, our PKAFnet can alleviate overfitting, which can improve the discriminative ability. The proposed PKAFnet was validated on a multi-center dataset, and remarkable performance was achieved in an independent testing set. Moreover, the interpretability of perception module and segmentation network were presented in our paper, which illustrated the effectiveness and credibility of PKAFnet. Therefore, the proposed method showed great application potential for MaVI prediction.

Structure-constrained combination-based nonlinear association analysis between incomplete multimodal imaging and genetic data for biomarker detection of neurodegenerative diseases.

Multimodal imaging data are widely applied in imaging genetic studies to identify associations between imaging and genetic data for the biomarker detection of neurodegenerative diseases (NDs). However, the incomplete multimodal imaging data and complex relationships among imaging and genetic data make it difficult to effectively analyze associations between imaging and genetic data and accurately detect disease-related biomarkers. This study proposed a novel structure-constrained combination-based nonlinear association analysis method to exploit associations between incomplete multimodal imaging and genetic data for potential biomarker detection of NDs. Two types of structure constraints were used in imaging and genetic data. First, a parallel concatenated projection method with multiple constraints was adopted to handle missing data. Modality-shared and modality-specific information could be well captured to obtain latent imaging representations. A locality preserving constraint was applied to the imaging data for retaining structure information before and after projection. A connectivity penalty was also included to capture structure associations among latent imaging representations. Second, a group-induced graph self-expression constraint was incorporated into our method to exploit strong structure correlations among inter- and intra-group of genetic data. Finally, a nonlinear kernel-based method was used to explore the complex associations between latent imaging representations and genetic data for biomarker detection. A set of simulation data and two sets of real ND data, which were obtained from Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases, were applied to assess the effectiveness of our method. High accuracy of biomarker detection was achieved. Moreover, the identification of disease-related biomarkers was confirmed in previous studies. Therefore, our method may provide a novel way to gain insights into the pathological mechanism of NDs and early prediction of these diseases.

Development of an indirect ELISA for detecting swine acute diarrhoea syndrome coronavirus IgG antibodies based on a recombinant spike protein.

Swine acute diarrhoea syndrome coronavirus (SADS-CoV) is a newly identified swine enteropathogenic coronavirus that causes watery diarrhoea in neonatal piglets, leading to significant economic losses to the swine industry. Currently, there are no suitable serological methods to assess the infection of SADS-CoV and effectiveness of vaccines, making an urgent need to exploit effective enzyme-linked immunosorbent assay (ELISA) to compensate for this deficiency. Here, a recombinant plasmid that expresses the spike (S) protein of SADS-CoV fused to the Fc domain of human IgG was constructed to generate recombinant baculovirus and expressed in HEK 293F cells. The S-Fc protein was purified with protein G Resin, which retained reactivity with anti-human Fc and anti-SADS-CoV antibodies. The S-Fc protein was then used to develop an indirect ELISA (S-iELISA) and the reaction conditions of S-iELISA were optimized. As a result, the cut-off value was determined as 0.3711 by analyzing OD450nm values of 40 SADS-CoV-negative sera confirmed by immunofluorescence assay (IFA) and western blot. The coefficient of variation (CV) of 6 SADS-CoV-positive sera within and between runs of S-iELISA were both less than 10%. The cross-reactivity assays demonstrated that S-iELISA was non-cross-reactive with other swine viruses' sera. Furthermore, the overall coincidence rate between IFA and S-iELISA was 97.3% based on testing 111 clinical serum samples. Virus neutralization test with seven different OD450nm values of the sera showed that the OD450nm values tested by S-iELISA are positively correlated with the virus neutralization assay. Finally, a total of 300 pig field serum samples were tested by S-iELISA and commercial kits of other swine enteroviruses showed that the IgG-positive for SADS-CoV, TGEV, PDCoV and PEDV was 81.7, 54, 65.3 and 6%, respectively. The results suggest that this S-iELISA is specific, sensitive, repeatable and can be applied for the detection of the SADS-CoV infection in the swine industry.

Research of Low-Temperature Performance of Polyphosphoric Acid-Modified Asphalt.

Polyphosphoric acid (PPA) modifier, which can effectively improve the rheological properties of asphalt, is widely used in pavement engineering. In order to accurately evaluate the low-temperature performance of PPA-modified asphalt, in this study, PPA-modified asphalt and PPA/SBR-modified asphalt were prepared. The modification mechanism was explored by scanning electron microscopy (SEM) and fourier transform infrared spectroscopy (FTIR). Bending Beam Rheology (BBR) test was carried out, and four indexes, including K index, viscous flow (η1), low-temperature integrated flexibility (Jc), and relaxation time (λ), were obtained by combining the Burgers model. The optimal low-temperature performance evaluation index of modified asphalt was determined by the analytic hierarchy process (AHP). The test results show that PPA addition to asphalt will produce chemical reactions, which can effectively improve the compatibility between SBR and neat asphalt. In the multi-index evaluation based on K, η1, Jc, and λ, the same optimum content of PPA was obtained. AHP analysis further demonstrates that Jc is the optimal evaluation index for laboratory research on the low-temperature performance of PPA-modified asphalt, and λ index is the ideal evaluation index for the low-temperature performance of asphalt in engineering applications.

Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma.

BACKGROUND: Models predicting future macrovascular invasion in hepatocellular carcinoma are constructed to assist timely interventions. METHODS: A total of 366 HCC cases were retrospectively collected from five Chinese hospitals between April 2007 and November 2016: the training dataset comprised 281 patients from four hospitals; the external validation dataset comprised 85 patients from another hospital. Multi-task deep learning network-based models were constructed to predict future macrovascular invasion. The discrimination, calibration, and decision curves were compared to identify the best model. We compared the time to macrovascular invasion and overall survival using the best model and related image heterogeneity scores (H-score). Then, we determined the need for a segmentation subnet or the replacement deep learning algorithm by logistic regression in screening clinical/radiological factors. Finally, an applet was constructed for future application. FINDINGS: The best model combined clinical/radiological factors and radiomic features. It achieved best discrimination (areas under the curve: 0·877 in the training dataset and 0·836 in the validation dataset), calibration, and decision curve. Its performance was not affected by the treatments and disease stages. The subgroups had statistical significance for time to macrovascular invasion (training: hazard ratio [HR] = 0·073, 95% confidence interval [CI]: 0·032-0·167, p < 0·001 and validation: HR = 0·090, 95%CI: 0·022-0·366, p < 0·001) and overall survival (training: HR = 0·344, 95%CI: 0·246-0·547, p < 0·001 and validation: HR = 0·489, 95%CI: 0·279 - 0·859, p = 0·003). Similar results were achieved when the patients were subdivided by the H-score. The subnet for segmentation and end-to-end deep learning algorithms improved the performance of the model. INTERPRETATION: Our multi-task deep learning network-based model successfully predicted future macrovascular invasion. In high-risk populations, besides the current first-line treatments, more therapies may be explored for macrovascular invasion.

Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease.

Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.