Selective ensemble method for anomaly detection based on parallel learning.

Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.

An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift.

A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. Four base classifiers of SVM, KNN, DT and RF are used in the first layer, and LR is used as meta classifier in second layer. Last but not least, we take consistency check of the incremental instance and check set to determine whether it is anormal by statistical learning, instead of threshold-based method. And the validation set is dynamic updated according to the consistency check result. Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. Experimental results show that the proposed method has significant advantages in anomaly detection of imbalanced data streams with concept drift.

Association of baseline and dynamic arterial stiffness status with dyslipidemia: a cohort study.

Background and aims: Dyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population. Methods: This study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019. Unadjusted and adjusted Cox proportional regression models were used to evaluate the associations of baseline baPWV and baPWV transition (persistent low, onset, remitted and persistent high) with incident dyslipidemia. Results: Of 4362 individuals (mean age: 55.5 years), 1490 (34.2%) developed dyslipidemia during a median follow-up of 5.9 years. After adjusting for potential confounders, participants with elevated arterial stiffness at baseline had an increased risk of dyslipidemia (HR, 1.194; 95% CI, 1.050-1.358). Compared with persistent low baPWV, new-onset and persistent high baPWV were associated with a 51.2% and 37.1% excess risk of dyslipidemia. Conclusion: The findings indicated that arterial stiffness is an early risk factor of dyslipidemia, suggesting a bidirectional association between arterial stiffness and lipid metabolism.

Thyroid hormone sensitivity and diabetes onset: a longitudinal cross-lagged cohort.

Purpose: Thyroid hormones sensitivity is a newly proposed clinical entity closely related with metabolic health. Prior studies have reported the cross-sectional relationship between thyroid hormones sensitivity and diabetes; however, the longitudinal association is unclear to date. We aimed to explore the relationship between impaired thyroid hormone sensitivity at baseline and diabetes onset using a cohort design. Methods: This study enrolled 7283 euthyroid participants at the first visit between 2008 and 2009, and then annually followed until diabetes onset or 2019. Thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) were measured to calculate thyroid hormone sensitivity by thyroid feedback quantile-based index (TFQI), Chinese-referenced parametric thyroid feedback quantile-based index (PTFQI), thyrotropin index (TSHI), thyrotroph thyroxine resistance index (TT4RI) and FT3/FT4 ratio. Cox proportional hazard model and cross-lagged panel analysis were used. Results: The mean baseline age was 44.2 ± 11.9 years, including 4170 (57.3%) male. During a median follow-up of 5.2 years, 359 cases developed diabetes. There was no significant association between thyroid hormones sensitivity indices and diabetes onset, and adjusted hazard ratios per unit (95% CIs) were 0.89 (0.65-1.23) for TFQI, 0.91 (0.57-1.45) for PTFQI, 0.95 (0.70-1.29) for TSHI, 0.98 (0.70-1.01) for TT4RI and 2.12 (0.17-5.78) for FT3/FT4 ratio. Cross-lagged analysis supported the temporal association from fasting glucose to impaired thyroid hormones sensitivity indices. Conclusions: Our findings could not demonstrate that thyroid hormones sensitivity status is a predictor of diabetes onset in the euthyroid population. Elevated fasting glucose (above 7.0 mmol/L) appeared to precede impaired sensitivity indices of thyroid hormones.

Comparison of Artificial Intelligence-Assisted Compressed Sensing (ACS) and Routine Two-Dimensional Sequences on Lumbar Spine Imaging.

Purpose: To evaluate and compare the image quality and diagnostic accuracy of Artificial Intelligence-assisted Compressed Sensing (ACS) sequences for lumbar disease, as an acceleration method for MRI combining parallel imaging, half-Fourier, compressed sensing and neural network and routine 2D sequences for lumbar spine. Methods: We collected data from 82 healthy subjects and 213 patients who used 2D ACS accelerated sequences to examine the lumbar spine while 95 healthy subjects and 234 patients used routine 2D sequences. Acquisitions included axial T2WI, sagittal T2WI, T1WI, and T2-fs sequences. All obtained images of these subjects were analyzed in the light of calculating image quality factors such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for selected regions of interest. The lumbar image quality, artifacts and visibility of lesion structure were assessed by two radiologists independently. Differences between the evaluation values above were tested for statistical significance by the Wilcoxon signed-ranks test. Inter-observer agreements of image quality between two radiologists were measured using Cohen's kappa correlation coefficient. Results: The ACS accelerated sequences not only reduced the scanning time by 18.9%, but also retained basically the same image quality as the routine 2D sequences in both healthy subjects and patients. Artifacts are less produced on ACS accelerated sequences compared with routine 2D sequences (p < 0.05). Apart from this, there were no significant differences in quantitative SNR, CNR measurements and qualitative scores within reviewing radiologists for each group (p > 0.05). Moreover, inter-observer agreement between two radiologists in scoring image quality was substantial consistently for ACS accelerated sequences and routine sequences (kappa = 0.622-0.986). Conclusion: Compared with routine 2D sequences, ACS accelerated sequences allow for faster lumbar spine imaging with similar imaging quality and present reliable diagnostic accuracy, which can potentially improve workflow and patient comfort in musculoskeletal examinations.

In vitro and in vivo biological evaluation of newly synthesized multi-target 20(R)-panaxadiol derivatives for treating Alzheimer's disease.

An extensive study was performed to discover a series of novel 20(R)-panaxadiol derivatives with various substituents at the 3-OH position as nontoxic, brain-permeable, multi-target leads for treating Alzheimer's disease. In vitro analysis revealed that a compound bearing benzyl-substituted carbamate, which we denoted compound 14a, exhibited the most potent neuroprotective activity, with an EC50 of 13.17 µM. The neuroprotective effect of compound 14a was slightly more potent than that of donepezil and much more potent than that of 20(R)-panaxadiol. Compound 14a at 7.5-120 µM exhibited low toxicity in various cell lines. In addition, compound 14a exhibited a wide range of biological activities, including inhibiting apoptosis; inducing tau hyperphosphorylation; affecting beta-amyloid (Aß), ß-secretase, reactive oxygen species, tumor necrosis factor-α, cyclooxygenase-2, and interleukin-1ß production; and promoting Aß25-35 disaggregation. The effective permeability of compound 14a across the blood-brain barrier was 26.13 × 10-6 cm/s, indicating that it can provide adequate exposure in the central nervous system. Further, compound 14a improved learning, memory, and novel object recognition in mice, and in vivo toxicity experiments confirmed a good therapeutic safety range. Thus, compound 14a is a promising multifunctional lead for treating Alzheimer's disease and offers new avenues for natural product-derived anti-Alzheimer's disease drugs.

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke.

Background: Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients. Purpose: We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke. Materials and methods: A total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)-binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients-the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation. Results: In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781-0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model. Conclusion: The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency. Summary: Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay. Key Results: Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).

Effect of freeze-thaw cycle aging and high-temperature oxidation aging on the sorption of atrazine by microplastics.

This study aims to better understand the aging characteristics of microplastics in the environment and the influence of aging microplastics on the migration and transformation of organic pollutants. In this study, polyvinyl chloride (PVC) and polyethylene (PE) were chosen as research objects, and the effects of two aging methods (freeze-thaw cycle aging and high-temperature oxidation aging) on their surface properties and atrazine (ATZ) sorption were investigated. The crystallinity of PE increased after freeze-thaw cycling and decreased after high-temperature oxidation. The freeze-thaw cycle destroys the amorphous region of PE, reducing the micropores on the PE surface and decreasing the ATZ adsorbed by PE. Although aging had no significant effect on the surface structure of PVC, it caused new oxygen-containing functional groups to be produced on the PVC surface, which reduced the ATZ adsorption capacity. These results show that the two aging modes change the surface properties of PVC and PE, thus affecting the sorption mechanism of ATZ, and provide a theoretical premise for the natural behavior and ecological chance assessment of ATZ in the presence of microplastics.

Accelerating Knee MRI: 3D Modulated Flip-Angle Technique in Refocused Imaging with an Extended Echo Train and Compressed Sensing.

PURPOSE: The three-dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in the imaging of musculoskeletal joints; however, its long acquisition time limits its clinical application. In such conditions, compressed sensing (CS) is introduced to accelerate MRI in clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences in knee imaging. METHODS: Images from different sequences of both the accelerated CS-MATRIX and the corresponding conventional acquisitions were prospectively analyzed and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the structures within the knees were measured for quantitative analysis. The subjective image quality and diagnostic agreement were compared between CS-MATRIX and conventional 2D sequences. Quantitative and subjective image quality scores were statistically analyzed with the paired t-test and Wilcoxon signed-rank test, respectively. Diagnostic agreements of knee substructure were assessed using Cohen's weighted kappa statistic. RESULTS: For quantitative analysis, images from the CS-MATRIX sequence showed a significantly higher SNR than T2-fs 2D sequences for visualizing cartilage, menisci, and ligaments, as well as a higher SNR than proton density (pd) 2D sequences for visualizing menisci and ligaments. There was no significant difference between CS-MATRIX and 2D T2-fs sequences in subjective image quality assessment. The diagnostic agreement was rated as moderate to very good between CS-MATRIX and 2D sequences. CONCLUSION: This study demonstrates the feasibility and clinical potential of the CS-MATRIX sequence technique for detecting knee lesions The CS-MATRIX sequence allows for faster knee imaging than conventional 2D sequences, yielding similar image quality to 2D sequences.

Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning.

OBJECTIVE: To develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images. METHODS: We retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared. RESULTS: The sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively. CONCLUSIONS: Deep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.

The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making.

Hypergraph learning for identification of COVID-19 with CT imaging.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

Segmenting Brain Tumor Using Cascaded V-Nets in Multimodal MR Images.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections. Focal loss is chosen to improve performance on hard samples as well as balance the positive and negative samples. We further propose three preprocessing pipelines for multimodal magnetic resonance images to train different models. By ensembling the segmentation probability maps obtained from these models, segmentation result is further improved. In other hand, we propose to segment the whole tumor first, and then divide it into tumor necrosis, edema, and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364, and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953, and 0.7364, respectively. We also evaluate the proposed method in two additional data sets from local hospitals comprising of 28 and 28 subjects, and the best results are 0.8635, 0.8036, and 0.7217, respectively. We further make a prediction of patient overall survival by ensembling multiple classifiers for long, mid and short groups, and achieve accuracy of 0.519, mean square error of 367240 and Spearman correlation coefficient of 0.168 for BraTS 2018 online testing set.

CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions.

BACKGROUND: To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions. METHODS: A retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans. The lesion areas were delineated, and a total of 1,029 radiomics features were extracted. The least absolute shrinkage and selection operator (Lasso) algorithm method was used to select the radiomics features significantly associated with discrimination of high-risk from low-risk lesions in the anterior mediastinum. Then, 8-fold and 3-fold cross-validation logistic regression (LR) models were taken as the feature selection classifiers to build the radiomics models for UECT and CECT scan respectively. The predictive performance of the radiomics features was evaluated based on the receiver operating characteristics (ROC) curve. RESULTS: Each of the two radiomics classifiers included the optimal 12 radiomic features. In terms of the area under ROC curve, using the radiomics model in discriminating high-risk lesions from the low-risks, CECT images accounted for 74.1% with a sensitivity of 66.67% and specificity of 64.81%. Meanwhile, UECT images were 84.2% with a sensitivity of 71.43% and specificity of 74.07%. CONCLUSIONS: The association of the two proposed CT-based radiomics features with the discrimination of high and low-risk lesions in anterior mediastinum was confirmed, and the radiomics features of the UECT scan were proven to have better prediction performance than the CECT's in risk grading.

Effects of sulindac sulfide on proliferation and apoptosis of human breast cancer cell.

The present study aimed to observe the effects of sulindac sulfide on the proliferation and apoptosis of human breast cancer cells MCF-7, and to explore the potential underlying molecular mechanism. The inhibitory ratio was detected using a cell counting kit-8 assay. The changes in cell cycle distribution were assessed using flow cytometry (FCM). Furthermore, the changes in cell apoptosis rates were detected by Hoechst 33258 staining and FCM coupled with Annexin V-FITC/propidium iodide (PI) staining. In addition, the protein expression was detected using western blotting. Sulindac sulfide was able to inhibit the proliferation of breast cancer in a dose- and time-dependent manner. In addition, sulindac sulfide altered the cell cycle of breast cancer cells. The results of Hoechst 33258 staining and FCM coupled with Annexin V-FITC/PI staining demonstrated that sulindac sulfide could significantly induce the apoptosis of MCF-7 cells in a dose-dependent, and time-dependent manner. The western blot analysis demonstrated the protein expression of Bcl-2 was downregulated, and Bax and cleaved caspase-3 were upregulated. The results of the present study suggest that sulindac sulfide can inhibit the proliferation and induce the apoptosis of MCF-7 cells.

Study on the separation effect of high-speed ultrasonic vibration cutting.

High-speed ultrasonic vibration cutting (HUVC) has been proven to be significantly effective when turning Ti-6Al-4V alloy in recent researches. Despite of breaking through the cutting speed restriction of the ultrasonic vibration cutting (UVC) method, HUVC can also achieve the reduction of cutting force and the improvements in surface quality and cutting efficiency in the high-speed machining field. These benefits all result from the separation effect that occurs during the HUVC process. Despite the fact that the influences of vibration and cutting parameters have been discussed in previous researches, the separation analysis of HUVC should be conducted in detail in real cutting situations, and the tool geometry parameters should also be considered. In this paper, three situations are investigated in details: (1) cutting without negative transient clearance angle and without tool wear, (2) cutting with negative transient clearance angle and without tool wear, and (3) cutting with tool wear. And then, complete separation state, partial separation state and continuous cutting state are deduced according to real cutting processes. All the analysis about the above situations demonstrate that the tool-workpiece separation will take place only if appropriate cutting parameters, vibration parameters, and tool geometry parameters are set up. The best separation effect was obtained with a low feedrate and a phase shift approaching 180 degrees. Moreover, flank face interference resulted from the negative transient clearance angle and tool wear contributes to an improved separation effect that makes the workpiece and tool separate even at zero phase shift. Finally, axial and radial transient cutting force are firstly obtained to verify the separation effect of HUVC, and the cutting chips are collected to weigh the influence of flank face interference.

Inhibitory effect of baicalin on allergic response in ovalbumin-induced allergic rhinitis guinea pigs and lipopolysaccharide-stimulated human mast cells.

OBJECTIVE: Baicalin, a flavonoid compound purified from the dry roots of Scutellaria baicalensis Georgi, has generally been used for the treatment of various allergic diseases. However, there is little information about the anti-inflammatory effects of baicalin for allergic rhinitis. This study aims to investigate the anti-allergic effect of baicalin on allergic response in ovalbumin (OVA)-induced allergic rhinitis guinea pigs and lipopolysaccharide (LPS)-stimulated human mast cells. METHODS: Using in vivo models, we evaluated the effect of baicalin on allergic rhinitis symptoms via recording the number of nasal rubs and sneezes. The levels of histamine, OVA-specific immunoglobulin E(IgE), eosinophil cationic protein (ECP) and inflammatory cytokines were measured using enzyme-linked immunosorbent assay (ELISA). The histological changes of nasal mucosa were observed by light microscope after HE staining. In vitro, the release of histamine and ß-hexosaminidase of compound 48/80-induced human mast cells were measured by ELISA and PNP-NAG colorimetry, respectively. The productions of inflammatory cytokines of LPS-stimulated human mast cells were determined using ELISA. Western blot was used to test the protein expression of JAK2, p-JAK2, STAT5, p-STAT5, IKKß, p-IKKß, IκBα, p-IκBα and NF-κB (p65) of LPS-stimulated human mast cells. RESULTS: The oral administration of baicalin at doses of 50 and 200 mg/kg improved allergic rhinitis symptoms and the histological changes of nasal mucosa and decreased the serum levels of histamine, ECP, interleukin (IL)-1ß, IL-6, IL-8, tumor necrosis factor (TNF)-α and OVA-specific IgE in OVA-induced allergic rhinitis guinea pigs. In vitro, baicalin suppressed the release of histamine and ß-hexosaminidase in compound 48/80-induced human mast cells. In addition, baicalin also inhibited the productions of inflammatory cytokines such as IL-1ß, IL-6, IL-8 and TNF-α and suppressed the phosphorylation of JAK2, STAT5, IKKß, IκBα and the nuclear translocation of NF-κB (p65) subunit in LPS-stimulated human mast cells. CONCLUSIONS: These results suggest that baicalin can effectively prevent allergic response in OVA-induced allergic rhinitis guinea pigs and inhibit inflammatory response via blocking JAK2-STAT5 and NF-κB signaling pathways in LPS-stimulated human mast cells. Considered together,the results show that baicalin may be a useful drug in the treatment of allergic rhinitis.

[Inhibitory effect of kaempferol on inflammatory response of lipopolysaccharide-stimulated human mast cells].

This study is to investigate the inhibitory effect of kaempferol on inflammatory response of lipopolysaccharide(LPS)-stimulated HMC-1 mast cells. The cytotoxicity of kaempferol to HMC-1 mast cells were analyzed by using MTT assay and then the administration concentrations of kaempferol were established. Histamine, IL-6, IL-8, IL-1ß and TNF-α were measured using ELISA assay in activated HMC-1 mast cells after incubation with various concentrations of kaempferol (10, 20 and 40 µmol.L-1). Western blot was used to test the protein expression of p-IKKß, IκBα, p-IκBα and nucleus NF-κB of LPS-induced HMC-1 mast cells after incubation with different concentrations of kaempferol. The optimal concentrations of kaempferol were defined as the range from 5 µmol.L-1 to 40 µmol.L-1. Kaempferol significantly decreased the release of histamine, IL-6, IL-8, IL-1ß and TNF-α of activated HMC-1 mast cells (P<0.01). After incubation with kaempferol, the protein expression of p-IKKß, p-IKBa and nucleus NF-κB (p65) markedly reduced in LPS-stimulated HMC-1 mast cells (P<0.01). Taken together, we concluded that kaempferol markedly inhibit mast cell-mediated inflammatory response. At the same time, kaempferol can inhibit the activation of IKKß, block the phosphorylation of IκBα, prevent NF-KB entering into the nucleus, and then decrease the release of inflammatory mediators.