Novel interacting proteins identified by tandem affinity purification and mass spectrometry associated with IFITM3 protein during PDCoV infection.

Interferon-induced transmembrane 3 (IFITM3) is a membrane-associated protein that exhibits antiviral activities against a wide range of viruses through interactions with other cellular and viral proteins. However, knowledge of the mechanisms of IFITM3 in Porcine deltacoronavirus (PDCoV) infection has been lacking. In this study, we demonstrate that IFN-α treatment induces the upregulation of IFITM3 activity and thus attenuates PDCoV infection. PDCoV replication is inhibited in a dose-dependent manner by IFITM3 overexpression. To clarify the novel roles of IFITM3 during PDCoV infection, proteins that interact with IFITM3 were screened by TAP/MS in an ST cell line stably expressing IFITM3 via a lentivirus. We identified known and novel candidate IFITM3-binding proteins and analyzed the protein complexes using GO annotation, KEGG pathway analysis, and protein interaction network analysis. A total of 362 cellular proteins associate with IFITM3 during the first 24 h post-infection. Of these proteins, the relationship between IFITM3 and Rab9a was evaluated by immunofluorescence colocalization analysis using confocal microscopy. IFITM3 partially colocalized with Rab9a and Rab9a exhibited enhanced colocalization following PDCoV infection. We also demonstrated that IFITM3 interacts specifically with Rab9a. Our results considerably expand the protein networks of IFITM3, suggesting that IFITM3 participates in multiple cellular processes during PDCoV infection.

Impaired meningeal lymphatic drainage in Listeria monocytogenes infection.

Previous studies have demonstrated an association between lymphatic vessels and diseases caused by bacterial infections. Listeria monocytogenes (LM) bacterial infection can affect multiple organs, including the intestine, brain, liver and spleen, which can be fatal. However, the impacts of LM infection on morphological and functional changes of lymphatic vessels remain unexplored. In this study, we found that LM infection not only induces meningeal and mesenteric lymphangiogenesis in mice, but also impairs meningeal lymphatic vessels (MLVs)-mediated macromolecules drainage. Interestingly, we found that the genes associated with lymphatic vessel development and function, such as Gata2 and Foxc2, were downregulated, suggesting that LM infection may affect cellular polarization and valve development. On the other hand, photodynamic ablation of MLVs exacerbated inflammation and bacterial load in the brain of mice with LM infection. Overall, our findings indicate that LM infection induces lymphangiogenesis and may affect cell polarization, cavity formation, and valve development during lymphangiogenesis, ultimately impairing MLVs drainage.

Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.

OBJECTIVE: To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS: We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS: The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION: Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

DAB2 promotes pulmonary fibrosis and may act as an intermediate between IGF­1R and PI3K/AKT signaling pathways.

Idiopathic pulmonary fibrosis (IPF) is a heterogeneous lung disease associated with high mortality. Disabled-2 (DAB2), an adapter protein, regulates cell-fibrinogen adhesion and fibrinogen uptake. DAB2 is differentially expressed in mouse fibrotic lungs induced by bleomycin according to a genome microarray analysis based on Gene Expression Omnibus database. However, the role of DAB2 in IPF has not been revealed. A bleomycin-induced mouse model of pulmonary fibrosis was constructed in the present study. It found that the expression of DAB2 was upregulated in bleomycin-induced fibrotic lung tissue with collagen fiber deposition and pulmonary interstitium thickening. Colocalization of DAB2 with α-smooth muscle actin (SMA) was observed in lung tissue sections. In vitro, human lung fibroblast MRC-5 cells were treated with TGF-ß1 and the expression of DAB2 was increased. Knockdown of DAB2 suppressed cell proliferation and the expression of α-SMA, collagen I, collagen IV and fibronectin in TGF-ß1-treated MRC-5 cells. The phosphorylation levels of PI3K and AKT were suppressed in DAB2-knockdown cells. IGF-1/IGF-1R has been reported to promote pulmonary fibrosis and activate the PI3K/Akt signaling. In the present study, the activation of IGF-1/IGF-1R signaling pathways in bleomycin-induced fibrotic lung tissues were positively associated with DAB2 expression. The phosphorylation level of IGF-1R was increased in MRC-5 cells with TGF-ß1 treatment, and DAB2 expression was decreased by silencing of IGF-1R. This suggested that DAB2 might be a downstream target of the IGF-1R pathway and thus induced PI3K/AKT signaling activation and fibrogenesis. The current study demonstrated the importance of DAB2 in pulmonary fibrosis and suggested the potential of IGF-1R/DAB2/PI3K in the pathogenesis of IPF.

Advances in viral encephalitis: Viral transmission, host immunity, and experimental animal models.

Viral infections have led to many public health crises and pandemics in the last few centuries. Neurotropic virus infection-induced viral encephalitis (VE), especially the symptomatic inflammation of the meninges and brain parenchyma, has attracted growing attention due to its high mortality and disability rates. Understanding the infectious routes of neurotropic viruses and the mechanism underlying the host immune response is critical to reduce viral spread and improve antiviral therapy outcomes. In this review, we summarize the common categories of neurotropic viruses, viral transmission routes in the body, host immune responses, and experimental animal models used for VE study to gain a deeper understanding of recent progress in the pathogenic and immunological mechanisms under neurotropic viral infection. This review should provide valuable resources and perspectives on how to cope with pandemic infections.

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence.

OBJECTIVE: To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network. METHODS: This study retrospectively recruited 621 CKD patients and 625 healthy controls in Jinling hospital and 57 CKD patients in Hainan hospital. These CKD patients were divided into cognitive function impairment (CFI) group and non-CFI group based on diagnostic criteria. All patients underwent brain MRI scan, neuropsychological test and laboratory exam. A deep learning model (Attention MLP) based on structural and functional sub-network (determined by the comparison between the patients and healthy controls) topological properties was developed to generate the MRI signature for the discrimination of CFI. Finally, a clinical-topological logistic regression model was built by combining MRI signature and clinical features. The area under curve (AUC), sensitivity and specificity were calculated to evaluate the model performance. Delong test was used to examine the difference of AUCs between models. The integrated discrimination improvement (IDI) and net reclassification index (NRI) between models were calculated. RESULTS: Attention MLP model performed well in both internal test set and external test set (AUC = 0.744 and 0.763, respectively). After combining with the clinical features, the model performance was further improved both in the internal (AUC: 0.748) and external test sets (AUC: 0.774), while both IDI and NRI were significant (all p < 0.05) in the external test set. According to the comprehensive comparison, the AUC of the Attention MLP model was significantly or marginal significantly higher than that of traditional machine learning models (logistic regression: AUC = 0.634; support vector machine: AUC = 0.613; decision tree: AUC = 0.539; XGBoost: AUC = 0.639) in internal test set. The results showed that the model built on the combining of structural and functional networks data outperformed those on the single network, as well as the connection matrix. CONCLUSION: The result indicated that the integration of the clinical information and the MRI signature generated by artificial intelligence model based on structural and functional network topological properties could help to predict the CFI of CKD patients effectively. Our results provided a set of quantifiable imaging biomarkers for CFI which may be beneficial to CKD patients.

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

PURPOSE: This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs). EXPERIMENTAL DESIGN: A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort. RESULTS: The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment. CONCLUSION: Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning.

Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics.

Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal. Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists. Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05). Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study.

OBJECTIVES: To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets. METHODS: Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods. RESULTS: The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p < 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p > 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055). CONCLUSION: Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters. KEY POINTS: • The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.

Quercetin induces apoptosis and enhances gemcitabine therapeutic efficacy against gemcitabine-resistant cancer cells.

Quercetin, an abundant flavonoid found in various fruits and vegetables, displays multiple biological activities, including anticancer effects. Therefore, quercetin is receiving increasing attention as a potential adjuvant anticancer treatment. Gemcitabine (GEM) resistance is a major issue for clinicians and patients with advanced cancers, making it crucial to determine ways to bolster its effects. In this study, we explored the anticancer effects and mechanistic actions of quercetin in GEM-resistant cancer cells. Pancreatic cancer (BxPC-3, PANC-1) and hepatocellular carcinoma (HepG2, Huh-7) cell lines were studied. Proliferation assays showed that quercetin had cytotoxic effects on GEM-resistant cell lines (HepG2 and PANC-1), and flow cytometric analysis indicated a significant pro-apoptotic effect on these cell lines. GEM treatment, in combination with quercetin, resulted in increased anticancer effects compared with GEM alone. Quercetin led to S phase arrest in GEM-resistant cell lines, and western blot analysis revealed tumour protein p53 upregulation and cyclin D1 downregulation. This study provides mechanistic insight into the anticancer effects of quercetin and suggests that quercetin adjuvant treatment may benefit patients who are resistant to GEM therapy.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation. METHODS: Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. RESULTS: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. CONCLUSIONS: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. KEY POINTS: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Primary intracranial leiomyosarcoma in an immunocompetent patient: Case report with emphasis on imaging features.

RATIONALE: Primary intracranial leiomyosarcoma (LMS) is an extremely rare tumor in the central nervous system (CNS), and usually seen in immunocompromised individuals. Only a few cases of primary intracranial LMS have been documented in the literature and no study focused on their MRI findings. We reported a case of primary intracranial leiomyosarcoma in a immunocompetent patient and review its imaging features. PATIENT CONCERNS: A 20-year-old female was admitted to our hospital, complaining with nausea, weight loss and progressive headache in recent 2 years. DIAGNOSIS: The magnetic resonance imaging scan of the brain revealed a large well-defined extra-cerebral mass adherent to left temporal meninges. The mass was iso-intense on T1-weighted images (T1WI), lightly iso to hypointense on T2-weighted images (T2WI) and enhanced uniformly with contrast medium. The preoperative diagnosis is meningioma. INTERVENTIONS: A left craniotomy was performed for the complete resection of the mass. OUTCOMES: The lesion was diagnosed via surgical histopathology and immunochemistry as leiomyosarcoma. No evidence of recurrence and complications were found in the following 13 months. LESSIONS: Primary intracranial leiomyosarcoma which has some special MR imaging features should be considered in the differential diagnosis of intracranial tumor.

[Neuroprotective effect of surround needling combined with acupoint injection on acute herpetic neuralgia].

OBJECTIVE: To explore the effect and mechanism of surround needling combined with acupoint injection on acute herpetic neuralgia (AHN). METHODS: Ninety-nine patients with T6-T10 segment AHN were randomly divided into 3 groups, 33 cases in each group, including 2 cases dropped off in the surround needling group, 4 cases dropped off in the acupoint injection group, and 3 cases dropped off in the combined group. Oral valacyclovir was given in each group, 0.3 g each time, 2 times a day for 10 days. Oblique insertion of needle used at ashi points around the herpes in the surround needling group, and continuous wave was stimulated to tolerance for 20 min; the same acupoints were selected as the surround needling group, stimulated with the mixture injection of mecobalamin and lidocaine in the acupoint injection group; After the surround needling, acupoint injection was performed in the combined group. The treatment was given once a day, 14 times for a course, and one course was needed in all groups. The skin healing conditions (blistering, crusting, and dislocation time) of each group were compared after treatment. The pain scores, pain area and quality of life scores in each group were observed before and after treatment. The levels of neuron specific enolase (NSE), substance P (SP) and calcitonin gene-related peptide (CGRP) in the local blister fluid were measured before and after treatment in all groups. RESULTS: The blistering, crusting and dislocation time in the combined group were earlier than the other two groups (all P<0.05). The pain score and pain area in the each group were significantly lower than those before treatment, and the quality of life score was significantly higher than that before treatment (all P<0.05). The improvements of pain score and quality of life score in the combined group were more obvious than the other two groups (all P<0.05). After treatment, the levels of NSE, SP and CGRP in the local blister fluid in each groups were significantly lower than those before treatment (all P<0.05). The indexes in the combined group were significantly lower than those in the other two groups (all P<0.05). CONCLUSION: Both surround needling and acupoint injection have an adjuvant effect on AHN. The combination of the two is better, the skin is healed quickly, the analgesia is significant, and the contents of local NSE, SP and CGRP are significantly decreased. The mechanism of action is to exert neuroprotective effects.

Rapid lipidomics analysis for sepsis-induced liver injury in rats and insights into lipid metabolic pathways using ultra-performance liquid chromatography/mass spectrometry.

Lipidomics has been applied in the identification and quantification of molecular lipids within an organism, and to provide insights into mechanisms in clinical medicine. Sepsis is a major systemic inflammatory syndrome and the liver here is a potential target organ for dysfunctional response. However, the study of alterations in global lipid profiles associated with sepsis-induced liver injury is still limited. In this work, we set out to determine alterations of lipidomics profiles in a rat model of sepsis-induced liver injury using an untargeted lipidomics strategy. Liquid chromatography coupled with mass spectrometry in conjunction with multivariate data analysis and pathway analysis were used to acquire a global lipid metabolite profile. Meanwhile, biochemistry index and histopathological examinations of the liver were performed to obtain auxiliary measurements for determining the pathological changes associated with sepsis-induced liver injury. Eleven lipid metabolites and two metabolic pathways were discovered and associated with sepsis-induced liver injury. The results indicated that various biomarkers and pathways may provide evidence for and insight into lipid profile alterations associated with sepsis-induced liver injury, and hence pointed to potential strategic targets for clinical diagnosis and therapy in the future.

Retracted Article: A high-throughput metabolomics strategy for discovering the influence of differential metabolites and metabolic pathways of huaxian capsules on sepsis-associated Qi deficiency and blood stasis syndrome.

High-throughput metabolomics can be used to investigate the therapeutic targets and metabolic mechanisms of traditional Chinese medicine (TCM) formulae, which have multiple targets in disease therapy, but it is a great challenge to explore their mechanism of action. Huaxian capsule (HXC) is a classical formula in TCM that has therapeutic effects on a sepsis-associated Qi deficiency and blood stasis syndrome (SQBS). However, its targets and metabolic mechanisms need more investigation. To investigate the therapeutic effects of HXC in the treatment of SQBS and elucidate the potential mechanism, we used a high-throughput metabolomics strategy based on the ultraperformance liquid chromatography/mass spectrometry combined with chemometrics to analyze and identify differential metabolites and pathways. The pathological examination of organs and biochemical indices was also performed to verify the successful establishment of the rat model and protective effects of HXC. Pathological symptoms and biochemical indicators of SQBS rats were reversed by the HXC treatment. A total of 24 potential biomarkers were identified to indicate the difference between the control and model groups; they were closely associated with ten metabolic pathways and regulated by the HXC administration. From the pathway analysis, we further understood the protective activity of HXC against SQBS, which affected amino acid metabolism, molecular transport, small molecule biochemistry and cell signaling as well as vitamin and mineral metabolism. In conclusion, HXC protects against SQBS by modulating the metabolic biomarkers and functional pathways.

Overexpression of steroid receptor coactivators alleviates hyperglycemia-induced endothelial cell injury in rats through activating the PI3K/Akt pathway.

Hyperglycemia is a major factor in vascular endothelial injury that finally leads to a cardiovascular event. Steroid receptor coactivators (SRCs) are a group of non-DNA binding proteins that induce structural changes in steroid receptors (nuclear receptors) critical for transcriptional activation. SRCs, namely, SRC-1, SRC-2, and SRC-3, are implicated in the regulation of vascular homeostasis. In this study we investigate the role of SRCs in hyperglycemia-induced endothelial injury. Aortic endothelial cells were prepared from normal and diabetic rats, respectively. Diabetic rats were prepared by injection of streptozotocin (50 mg/kg, i.p.). The expression levels of SRC-1 and SRC-3 were significantly decreased in endothelial cells from the diabetic rats. Similar phenomenon was also observed in aortic endothelial cells from the normal rats treated with a high glucose (25 mM) for 4 h or 8 h. The expression levels of SRC-2 were little affected by hyperglycemia. Overexpression of SRC-1 and SRC-3 in high glucose-treated endothelial cells significantly increased the cell viability, suspended cell senescence, and inhibited cell apoptosis compared with the control cells. We further showed that overexpression of SRC-1 and SRC-3 markedly suppressed endothelial injury through restoring nitric oxide production, upregulating the expression of antioxidant enzymes (SOD, GPX, and CAT), and activating the PI3K/Akt pathway. The beneficial effects of SRC-1 and SRC-3 overexpression were blocked by treatment with the PI3K inhibitor LY294002 (10 mM) or with the Akt inhibitor MK-2206 (100 nM). In conclusion, hyperglycemia decreased SRC-1 and SRC-3 expression levels in rat aortic endothelial cells. SRC-1 and SRC-3 overexpression might protect against endothelial injury via inhibition of oxidative stress and activation of PI3K/Akt pathway.

Efficacy evaluation of two commercial modified-live virus vaccines against a novel recombinant type 2 porcine reproductive and respiratory syndrome virus.

NADC30-like porcine reproductive and respiratory syndrome virus (PRRSV) causing clinical disease outbreaks has been recently reported in China. The recombination occurring among PRRSV strains could lead to the emergence of novel and more virulent viruses. In our previous study, a novel recombinant type 2 PRRSV (TJnh1501) between NADC30-like and modified-live virus (MLV)-like derived from the Chinese highly pathogenic PRRSV was shown to have higher pathogenicity than NADC30-like PRRSV. It remains unknown whether the emergence of the novel recombinant PRRSV strain can lead to variable protection efficacy of the MLV vaccines. In this paper, two typical commercial MLV vaccines were used to evaluate their efficacy to block TJnh1501 infection and onset of clinical symptoms. Our results showed that both MLV vaccines could shorten the period of fever and reduce viral loads in sera, but were not able to reduce the clinical signs and lung lesions indicating that the two commercial MLV vaccines provide limited cross-protection efficacy against the novel recombinant type 2 PRRSV infection. This study gives valuable suggestions for the use of MLV vaccines to control PRRSV infection in the field.

Astragalus Membranaceus Improving Asymptomatic Left Ventricular Diastolic Dysfunction in Postmenopausal Hypertensive Women with Metabolic Syndrome: A Prospective, Open-Labeled, Randomized Controlled Trial.

BACKGROUND: Postmenopausal women with metabolic syndrome (MetS) have increased cardiovascular morbidity and left ventricular diastolic dysfunction (LVDD). The various protective effects of astragalus membranaceus (AM) have been described in previous studies. Therefore, this study aimed to evaluate the effects of different doses of AM on diastolic function in postmenopausal hypertensive women with MetS. METHODS: This was a prospective, randomized controlled study. The postmenopausal hypertensive patients with MetS were enrolled from Lanzhou University Second Hospital from March 2014 to April 2015. Patients were divided into three groups: control group (received conventional medical treatment), AM Group 1 (received AM capsules at 5 g/d additionally), and AM Group 2 (received AM capsules at 10 g/d additionally). Echocardiographic and clinical characteristics were evaluated before and 12 months after treatment. Quantitative data were analyzed using unpaired t-test, analysis of variance, and multiple linear regression analysis. RESULTS: A total of 154 patients were subjected to final analysis. In the AM Group 2, significant improvements were noted in diastolic function 12 months after treatment than those of the control group, including the early diastolic mitral annular velocity (E'; 0.065 ± 0.007 m/s vs. 0.061 ± 0.008 m/s, P = 0.014), the ratio of the early diastolic mitral peak flow velocity to the late diastolic mitral peak flow velocity (E/A; 0.81 ± 0.05 vs. 0.80 ± 0.06, P = 0.012), the ratio of E' to the late diastolic mitral annular velocity (E'/A'; 0.56 ± 0.12 vs. 0.51 ± 0.13, P = 0.048), and the ratio of the early diastolic mitral peak flow velocity (E) to E' (E/E'; 10.70 ± 1.30 vs. 11.37 ± 1.73, P = 0.031). After treatment, E/E' (10.70 ± 1.30 vs. 11.24 ± 1.56, P = 0.021), deceleration time (DT; 261.49 ± 44.41 ms vs. 268.74 ± 53.87 ms, P = 0.046), and E'/A' (0.56 ± 0.12 vs. 0.52 ± 0.13, P = 0.019) values improved more significantly than those of AM Group 2 before treatment. Besides, waist circumference was positively correlated with E' (r = 0.472; P = 0.003) and E'/A' (r = 0.321; P = 0.047). In addition, the waist-to-hip ratio was a significant predictor of DT (r = 0.276; P = 0.041), E' (r = -0.590; P < 0.001), E/E' (r = 0.454; P = 0.004), and E'/A' (r = -0.377; P = 0.018). CONCLUSIONS: Conventional medical plus AM therapy improved diastolic function. Moreover, WC and WHR might be risk factors for LVDD. CHINESE CLINICAL TRIAL REGISTER: ChiCTR-TRC-11001747. http://www.chictr.org.cn/showprojen.aspx?proj=7798.