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The compound Chinese herbal medicine (CCHM) is one of the most commonly used types of synergistic herbal medicine. It is based on composite herbal formula (CHF), which makes quality evaluation of this kind of traditional Chinese medicine (TCM) difficult. Taking Nvjin Pills (NJP) as an example, this study reported the development of a novel principle of analysis in CCHM. In order to improve the effectiveness of marketed drugs related active ingredients, it was necessary to designate a more unified quality evaluation standard. The core of the experimental is to prepare 3 batches of TCM reference drugs (TCMRD) using high-quality Chinese materia medica (single Chinese herbals used in the NJP). The active ingredients identified in the herbal formula including glycyrrhizic acid, cinnamaldehyde, paeonol, baicalin, hesperidin, paeoniflorin and ferulic acid were analyzed in both 3 TCMRDs and 76 batches of commercial products from 19 manufacturers by high performance liquid chromatography (HPLC) method combined with wavelength switching. NJP is a well-known Chinese patent medicine that has been widely applied for the clinical treatment of polycystic ovarian syndrome (PCOS) and other gynecological diseases. For the first time, the relationship between the components mentioned above and their pharmacological in the treatment of PCOS was explored via network pharmacology analysis. The simple prediction results of network pharmacological analysis verified the feasibility and reliability of the established quantitative analysis method for 7 compounds in NJP, which were recommended as candidate indicators for quality evaluation ultimately. Using the TCMRD as the scientific ruler, quality grade specifications of NJP were proposed by comprehensive analysis of multiple index. Accordingly, 16, 47, and 13 batches of samples were primarily rated as first-grade, second-grade and unqualified grade respectively. This study will provide a chemical basis for quality control of NJP, which is necessary in the production process of pharmaceutical development.

Purpose This study aimed to establish a prediction model basing on onset age, gender, body mass index (BMI) and imaging measurements to predict the remaining life of knee joint for varus knee osteoarthritis patients, and to aid in patient selection and decision-making efforts regarding knee joint preservation and total knee arthroplasty (TKA). Methods Using data collected from our retrospective TKA database, 132 patients with varus knee osteoarthritis (153 knees) were included in the study. We focused on patients with a diagnosis of Kellgren–Lawrence (K-L) grade II in the initial stage of knee osteoarthritis (OA) accompanied by mild symptoms and a diagnosis of K-L grade IV with severe symptoms and TKA records at the endpoint. The interval between baseline and endpoint was defined as the remaining life of knee joint. The following imaging indexes of knee joints were assessed using standard anteroposterior (AP) radiography, medial proximal tibial angle (MPTA) and joint line convergence angle (JLCA) at baseline (MPTA0 and JLCA0) and at TKA period (MPTAT and JLCAT); changes in MPTA and JLCA between baseline and TKA period (ΔMPTA and ΔJLCA); and rates of change in MPTA and JLCA between baseline and TKA period (kMPTA and kJLCA). Meanwhile, basic information of patients was collected, including onset age, gender, body mass index (BMI). Multivariate linear regression analysis was performed for the prediction model about the remaining life of knee joint using statistically significant indexes. Results Multivariate linear regression analysis showed that, onset age, BMI, JLCA0, kMPTA and kJLCA were significantly negatively correlated with the remaining life of knee joint, which showed effectiveness in prediction model for TKA. Prediction model: remaining life of knee joint = 195.3–0.6×Onset age-2.0×BMI-4.4×JLCA0-159.9×kMPTA-205.5×kJLCA. Conclusions Onset age, BMI, JLCA0 and rates of change in MPTA and JLCA robustly predict the risk of requiring TKA. Level of Evidence: Retrospective Study Level III

Background Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models. Purpose To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T1 -weighted imaging. Study type Prospective. Animal model Wistar rats, total 97. Field strength/sequence 3T, 3D T1 -weighted images with fast-spoiled gradient echo (FSPGR). Assessment Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination. Statistical tests Receiver operating characteristic curve, fuzzy comprehensive evaluation. Results For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, F0-2 and F3-4, and F0-3 and F4, the accuracies of 12 radiomics models were 77.27-90.91%, 73.33-86.67%, 80.56-91.67%, 74.07-88.89%, 76.47-88.24%, and 79.49-92.31%, respectively. The AUCs of the radiomics models were 0.86-0.97, 0.85-0.95, 0.89-0.97, 0.81-0.96, 0.82-0.93, and 0.85-0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO-SVM) model had high AUCs of 0.93-0.97. For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, and F0-2 and F3-4, the fuzzy comprehensive evaluation showed that the LASSO-SVM model had a high fuzzy score/order of 0.087-0.091/1. Data conclusion LASSO-SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T1 -weighted imaging in a rodent model of liver fibrosis. Level of evidence 2. Technical efficacy stage 2.

PurposeThis study aimed to establish a predictive model to aid in patient selection and decision-making efforts regarding total knee arthroplasty (TKA).MethodsUsing data collected from our retrospective TKA database, 132 patients with varus knee osteoarthritis (153 knees) were included in the study. We focused on patients with a diagnosis of Kellgren–Lawrence (K-L) grade II in the initial stage of knee osteoarthritis (OA) and K-L grade IV with TKA at the endpoint. The interval between baseline and endpoint was defined as the remaining life of the knee joint. The following lateral and medial knee indicators were assessed using standard anteroposterior (AP) and lateral radiography: fibula height (FH), lateral plateau widening (LPW), medial proximal tibial angle (MPTA), and joint line convergence angle (JLCA) at baseline (FH0, LPW0, MPTA0, and JLCA0, respectively); FH, LPW, MPTA, and JLCA in the TKA period (FHT, LPWT, MPTAT, and JLCAT, respectively); changes in FH, LPW, MPTA, and JLCA between baseline and TKA periods (cFH, cLPW, cMPTA, and cJLCA, respectively); and rates of change in FH, LPW, MPTA, and JLCA between baseline and TKA periods (ΔFH, ΔLPW, ΔMPTA, and ΔJLCA, respectively). Multivariate linear regression analysis was performed for comparisons of indicators with the remaining life of the knee joint.ResultsRates of change in FH and LPW between baseline and TKA periods were negatively correlated with the remaining life of the knee joint (ΔFH, β = −335.901, t = −2.182, P = 0.035; ΔLPW, β = −488.516, t = −2.588, P = 0.013). There were positive correlations of ΔMPTA and ΔJLCA with risk of requiring TKA, respectively (r = −0.523 and − 0.480, respectively, all P < 0.001), but these correlations were not statistically significant in the predictive model of the remaining life of the knee joint. Other indicators were not significantly correlated with the remaining life of the knee joint (all P > 0.05).ConclusionsRates of change in FH and LPW robustly predict the risk of requiring TKA.Level of Evidence: Retrospective Study Level III

Purpose: To investigate the clinical feasibility of diagnosing and classifying patent foramen ovale (PFO) in patients with cerebral disorders by cardiac cine MRI (CCMRI) without contrast.Materials and Methods: Forty-four patients (24 males and 20 females; mean age, 41.3 years; range, 21–64 years) with cerebral disorders underwent contrast transcranial Doppler sonography (cTCD) and non-enhanced CCMRI examinations between October 2019 and March 2020. CCMRI was performed with a 3.0T magnetic resonance (MR) scanner using the OBL FIESTA CINE 4CH sequence. The scanning direction was perpendicular to the interatrial septum (IAS). The obtained MR images were analyzed by AW station 4.4. Pseudo-color coding was performed based on the different phases. The blood shunt condition was observed and recorded, noting the PFO length and width and whether it was complicated by IAS aneurysm or secondary septum thickening.Results: Thirty-nine of the 44 patients with cerebral disorders were confirmed to have right-to-left shunt by cTCD, and 37 of them were diagnosed with PFO by CCMRI. Two of the five remaining patients were also diagnosed with PFO by CCMRI. Compared with cTCD as a standard, CCMRI assessment resulted in the following: sensitivity, 94.9%; specificity, 60.0%; accuracy, 90.9%; positive predictive value, 94.9%; negative predictive value, 60.0%; area under the curve, 0.774. Using pseudo-color coding, a right-to-left color jet was observed in 34 patients, and a two-way shunt was found in five. IAS aneurysm and secondary septum thickening were found in five and three patients (11.4% and 6.8%), respectively. The maximum PFO diameters ranged from 1.7 to 16.8 mm, and the mean diameter was 5.4 ± 3.4 mm.Conclusion: The noninvasive CCMRI without contrast proved an excellent method for PFO identification, evaluation, and classification, with high sensibility (92.85%) and concordance (90.9%) compared to cTCD.

A deep facial attribute editing model strives to meet two requirements: (1) attribute correctness – the target attribute should correctly appear on the edited face image; (2) irrelevance preservation – any irrelevant information (e.g., identity) should not be changed after editing. Meeting both requirements challenges the state-of-the-art works which resort to either spatial attention or latent space factorization. Specifically, the former assume that each attribute has well-defined local support regions; they are often more effective for editing a local attribute than a global one. The latter factorize the latent space of a fixed pretrained GAN into different attribute-relevant parts, but they cannot be trained end-to-end with the GAN, leading to sub-optimal solutions. To overcome these limitations, we propose a novel latent space factorization model, called L2M-GAN, which is learned end-to-end and effective for editing both local and global attributes. The key novel components are: (1) A latent space vector of the GAN is factorized into an attribute-relevant and irrelevant codes with an orthogonality constraint imposed to ensure disentanglement. (2) An attribute-relevant code transformer is learned to manipulate the attribute value; crucially, the transformed code are subject to the same orthogonality constraint. By forcing both the original attribute-relevant latent code and the edited code to be disentangled from any attribute-irrelevant code, our model strikes the perfect balance between attribute correctness and irrelevance preservation. Extensive experiments on CelebA-HQ show that our L2M-GAN achieves significant improvements over the state-of-the-arts.

Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models.To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced TProspective.Wistar rats, total 97.3T, 3D TLiver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination.Receiver operating characteristic curve, fuzzy comprehensive evaluation.For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, F0-2 and F3-4, and F0-3 and F4, the accuracies of 12 radiomics models were 77.27-90.91%, 73.33-86.67%, 80.56-91.67%, 74.07-88.89%, 76.47-88.24%, and 79.49-92.31%, respectively. The AUCs of the radiomics models were 0.86-0.97, 0.85-0.95, 0.89-0.97, 0.81-0.96, 0.82-0.93, and 0.85-0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO-SVM) model had high AUCs of 0.93-0.97. For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, and F0-2 and F3-4, the fuzzy comprehensive evaluation showed that the LASSO-SVM model had a high fuzzy score/order of 0.087-0.091/1.LASSO-SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T2.2.

Highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) is characterized by high fever and high mortality in pigs of all ages and has severely affected the pork industry of China in the last decades. This study evaluated the differences in protection conferred by of three MLV PRRS vaccines derived from classical PRRSV (C-PRRSV, VR2332) and HP-PRRSV (TJM-F92 and JXA1-R) against the field challenge of HP-PRRSV TP strain (JXA1-R like). Compared to the experiment pigs in control group which were vaccinated with normal saline, the MLV PRRS vaccinated pigs had milder clinical symptoms, fewer pathological changes in the lung, and higher body weight gain at the end of the study. However, piglets vaccinated with VR2332 had higher body temperature, higher viral loads and lower body weight gain when compared with piglets vaccinated with TJM-F92 or JXA1-R vaccines at the end of the study. The results demonstrated that VR2332 vaccine provided a limited cross-protection against the HP-PRRSV TP strain infection, while in contrast the TJM-F92 and JXA1-R vaccines provided more efficacious protection. The findings of this study could serve as a valuable reference guide for the pig producers and veterinarians when considering the choice of which type of MLV PRRS vaccines to protect their pig herds against field challenge by HP-PRRSV TP strain.

Objective: Saturation biopsy is more sensitive than transrectal biopsy in the detection of prostate cancer but is an invasive method and has a risk of overdiagnosis. Multiparametric magnetic resona...

Purpose. To investigate the relationship between gut microbiota and liver fibrosis and establish a microbiota biomarker for detecting and staging liver fibrosis. Methods. 131 Wistar rats were used in our study, and liver fibrosis was induced by carbon tetrachloride. Stool samples were collected within 72 hours after the last administration. The V4 regions of 16S rRNA gene were amplified. The sequencing data was processed using the Quantitative Insights Into Microbial Ecology (QIIME version 1.9). The diversity, principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS), and linear discriminant analysis (LDA) effect size (LEfSe) were performed. Random-Forest classification was performed for discriminating the samples from different groups. Microbial function was assessed using the PICRUST. Results. The Simpson in the control group was lower than that in the liver fibrosis group (p=0.048) and differed significantly among different fibrosis stages (p=0.047). The Chao1 index in the control group was higher than that in the liver fibrosis group (p<0.001). NMDS analysis showed a marked difference between the control and liver fibrosis groups (p<0.001). PCoA analysis indicated the different community composition between the control and liver fibrosis groups with variances of PC1 13.76% and PC2 5.89% and between different liver fibrosis stages with variances of PC1 10.51% and PC2 7.78%. LEfSe analysis showed alteration of gut microbiota in the liver fibrosis group. Biomarkers obtained from Random-Forest classification showed excellent diagnostic accuracy in prediction of liver fibrosis with AUROCs of 0.99. The AUROCs were 0.77~0.84 in prediction of stage F4. There were six increased and 17 decreased metabolic functions in the liver fibrosis group and 6 metabolic functions significantly differed among four liver fibrosis stages. Conclusion. Gut microbiota is a potential biomarker for detecting and staging liver fibrosis with high diagnostic accuracies.

Zero-shot learning (ZSL) typically learns a projection function between a feature space and a semantic space (e.g. attribute space) to recognize unseen classes without any training samples. However, when the projection function learned only with seen class training samples is applied to unseen class test samples, most existing ZSL models suffer from projection domain shift. In this paper, we propose a new ZSL model, termed joint inductive learning (JIL) of bidirectional projections and shared subspace, to learn more generalizable projections and thus algin the seen and unseen class domains. By inducing the two tasks of bidirectional projection learning (BPL) and shared subspace learning (SSL), we integrate three forms of projection learning into a unified framework, which makes our JIL model become more generalizable across the two domains. Moreover, since a shared subspace is actually learned between the feature and semantic spaces, our JIL model can be naturally applied to cross-modal retrieval (CMR). Extensive experiments demonstrate that our JIL model generally outperforms the state-of-the-art alternatives in the two challenging tasks (i.e. ZSL and CMR).

Streptococcus pneumoniae (pneumococcus) is touted to be the generally found pathogen in patients with respiratory issues and there is an epidemiologic linkage present between Respiratory syncytial virus (RSV). This study aim at investigating the interaction between RSV and two serotypes of S. pneumoniae using a distinct animal model and a well-established colonizing pneumococcal strain. Phase variants phenotype of each strain was determined under oblique light. Co infection model was developed using BALB/c mice housed in a BSL-2 facility. Coinfection experiments were performed and number of bacterial colonies was quantified and phase determination was evaluated. RSV was detected in sample through real-time quantitative PCR. Adherence assays were performed to determine adherence of Spn strains and its knock out ΔNanA to nasopharyngeal carcinoma (NPC) epithelial CNE3 cell line. The biofilm viability was determined and phase composition was counted using plate count. Neuraminidase activity was measured in fluorometircassessed using 2’-(4-methylumbelliferyl)-α-D-N-acetylneuraminic acid (MUAN) as substrate as described in earlier literature. The GraphPad Software version 5.01 i.e., GraphPad Prism was used to conduct the statistical analysis. The extent of bacterial colonization was increased significantly (p

In view of the present intelligent substation fault diagnosis of complicated structure, sample data acquisition difficult, randomness of failure and other issues, build a kind of intelligent substation fault diagnosis based on the optimization of support vector machine (SVM) model, first of all, through the principal component analysis (pca) to simplify the fault source and key fault feature extraction, reduce the complexity of fault diagnosis.Then, based on the characteristics of the operation mode of the substation, the multi-classification SVM classifier is constructed and optimized by the imperial competition algorithm to find the optimal parameters. At last, the fault events of real substation are verified by experiments. The results show that the proposed fault diagnosis method can effectively solve the problem of less training samples and has a better diagnosis effect.

Epoxy resin insulation transformer is a typical dry-type insulation equipment in power system. The insulation performance diagnosis of such equipment is the current research focus. This paper analyses the basic principles of two typical dynamic methods, FWO method and Kissinger method, and introduces the specific application methods of the two methods in the calculation of the activation energy of epoxy resin insulated transformers, which proves the effectiveness of the method. This article provides theoretical basis and example verification for insulation aging diagnosis of dry-type transformers based on quantitative characterization of activation energy.

Ultra-High Frequency (UHF) method is one of the most effective methods, which is widely applied for partial discharge (PD) detection of HV switchgear owning to its high sensitivity and strong resistance to external interference. Despite of these advantages, it is still uneasy to realize the accurate identification of the fault types of HV switchgear because of the lack of a systematic research and analysis on the partial discharge characteristics of different defects in switchgear. In this paper, three typical partial discharge defect models, including needle discharge, suspended discharge, and internal discharge were designed. Then the ϕ-q-n patterns of different partial discharges were obtained by application of UHF method. The results demonstrated based on the characteristics of ϕ-q-n patterns of various PD and differences among them, the type of single or combined PD could be effectively recognized by extracting the characteristic variables of the ϕ-q-n patterns. Moreover, the accuracy of locating PD in switchgear could be improved by use of iterative least square method. Therefore, UHF provides an effective way to evaluate and predict the defect type and position of PD of HV switchgear.

Ultra-High Frequency (UHF) method is one of the most effective methods for partial discharge (PD) detection of HV switchgear, due to its high sensitivity and strong resistance to external interference. Despite of these advantages, it is still uneasy to realize the accurate identification of the fault types and discharge stages of HV switchgear owning to the lack of a systematic research and analysis on the partial discharge characteristics of different defects in switchgear. In this paper, three typical partial discharge defect models, including needle discharge, suspended discharge, and internal discharge were designed. The PRPD spectra of different discharge defect models at various discharge stages were obtained by application of UHF method. The results demonstrated that based on the characteristics of the PRPD spectra of various discharge defect model, the type of PD could be effectively recognized by analyzing the characteristic variables of the PRPD spectra. Moreover, the discharge stages could also be roughly inferred from the diversity of different PRPD spectra. Therefore, UHF provides an effective way to evaluate the defect type and stage of PD in HV switchgear.

Objectives: To assess the quality of life (QOL) and factors affecting QOL in hemodialysis patients so as to improve QOL of dialysis patients and provide the basis for better clinical care. Methods: A retrospective study was performed to assess the QOL and factors affecting QOL in hemodialysis patients. We recruited 125 patients who had been receiving hemodialysis for at least 2 years in the dialysis units of nine hospitals in Shanxi Province, China, and conducted a multi-center questionnaire survey between 1 May 2015 and 1 July 2016. We investigated the patients’ general condition and clinical data and used the Short Form-36 (SF-36) scale to measure QOL in these patients. Results: The overall SF-36 score was 107.55 ± 14.50 in patients who had received hemodialysis for more than 2 years. Age (p