A new model combining the liver/spleen volume ratio and classification of varices predicts HVPG in hepatitis B patients with cirrhosis

2015 ◽  
Vol 27 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Shi-ping Yan ◽  
Hao Wu ◽  
Guang-chuan Wang ◽  
Yong Chen ◽  
Chun-qing Zhang ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Volume Ratio ◽  
Spleen Volume ◽  
New Model ◽  
Model Combining

scholarly journals Platelet count/spleen volume ratio has a good predictive value for esophageal varices in patients with hepatitis B liver cirrhosis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260774
Author(s):  
Sihao Yu ◽  
Wei Chen ◽  
Zicheng Jiang
Keyword(s):  
Platelet Count ◽  
Magnetic Resonance ◽  
Hepatitis B ◽  
Esophageal Varices ◽  
Multivariate Logistic Regression Analysis ◽  
Volume Ratio ◽  
Spleen Volume ◽  
Magnetic Resonance Examination ◽  
Research Subjects ◽  
Clinical Value

Background & aims Platelet count/spleen longest diameter ratio (PSDR) is widely used in clinical practice due to its good performance in predicting esophageal varices (EV). We obtained spleen volume (SV) by magnetic resonance examination, the purpose of this study was to evaluate the clinical value of platelet count/spleen volume ratio (PSVR) and spleen volume in predicting EV in patients with hepatitis B cirrhosis. Methods: This study was a diagnostic accuracy experiment and retrospective, 199 patients with hepatitis B cirrhosis who met the criteria were selected as the research subjects. All patients were collected blood samples in the morning on an empty stomach within 2 days, and related indicators were tested. Within 10 days, they received electronic gastroscopy and abdominal magnetic resonance examination. According to the Child-Pugh score, the patients were divided into groups with or without EV and with or without high-risk esophageal varices (HRV), then statistical analysis of the two groups was performed. Results The area under the curve (AUC) of PSVR in predicting EV or HRV in each group (85.5%-92.6%) was higher than PSDR, SV, spleen diameter, and platelet count. The AUC of PSDR in diagnosing HRV was higher than SV, and the AUC of SV in diagnosing EV was higher than PSDR, but the difference was not significant (P>0.05). In Child-Pugh A patients, Multivariate logistic regression analysis showed PSVR could be a predictor of HRV (P<0.05), SV was a reliable predictor of EV (P<0.05). Conclusion PSVR is better than PSDR, spleen diameter, platelet count in predicting EV; in the absence of serological results, SV could be used instead of PSDR. Both can predict EV or HRV of patients with hepatitis B cirrhosis.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

scholarly journals Improved Hough Transform and Total Variation Algorithms for Features Extraction of Wood

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Weiwei Du ◽  
Yarui Xi ◽  
Kiichi Harada ◽  
Yumei Zhang ◽  
Keiko Nagashima ◽  
...  
Keyword(s):  
Total Variation ◽  
Hough Transform ◽  
Annual Ring ◽  
Wood Quality ◽  
Volume Density ◽  
Low Noise ◽  
Impact Factors ◽  
Annual Rings ◽  
New Model ◽  
Model Combining

Research shows that the intensity impact factors of wood, such as late timber ratio, volume density and the intensity of itself, correlate with the width of wood annual rings. Therefore, extracting wood annual ring information from wood images is helpful for evaluating wood quality. During the past few years, many researchers have conducted defect detection by studying the information of wood images. However, there are few in-depth studies on the statistics and calculation of wood annual ring information. This study proposes a new model combining the Total Variation (TV) algorithm and the improved Hough transform to accurately measure the wood annual ring information. The TV algorithm is used to suppress image noise, and the Hough transform is for detecting the center of the wood image. Moreover, the edges of wood annual rings are extracted, and the statistical ring information is calculated. The experimental results show that the new model has good denoising capability, clearly extract the edges of wood annual rings and calculate the related parameters from the indoor wood images of the processed logs and the unprocessed low-noise logs.

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

scholarly journals Classification of Gan Dan Shi Re Pattern and Gan Shen Yin Xu Pattern in Patients with Hepatitis B Cirrhosis Using Metabonomics

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chao-qun Zhao ◽  
Long Chen ◽  
Hong Cai ◽  
Wei-li Yao ◽  
Qun Zhou ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Metabolic Pathways ◽  
Serum Samples ◽  
Flight Mass Spectrometry ◽  
Material Basis ◽  
Data Points ◽  
Scientific Nature ◽  
Objective Basis ◽  
Important Objective

Objective. This study aimed to analyze the differential metabolites and their metabolic pathways from the serum of patients with hepatitis B cirrhosis, with two typical patterns of Gan Dan Shi Re (GDSR) and Gan Shen Yin Xu (GSYX) based on the theory of traditional Chinese medicine (TCM). It also investigated the variation in the internal material basis for the two types of patterns and provided an objective basis for classifying TCM patterns using metabolomic techniques. Methods. The serum samples taken from 111 qualified patients (40 GDSR cases, 41 GSYX cases, and 30 Latent Pattern (LP) cases with no obvious pattern characters) and 60 healthy volunteers were tested to identify the differential substances relevant to hepatitis B cirrhosis and the two typical TCM patterns under the gas chromatography–time-of-flight mass spectrometry platform. The relevant metabolic pathways of differential substances were analyzed using multidimensional statistical analysis. Results. After excluding the influence of LP groups, six common substances were found in GDSR and GSYX patterns, which were mainly involved in the metabolic pathways of glycine, serine, threonine, and phenylalanine. Eight specific metabolites involved in the metabolic pathways of linoleic, glycine, threonine, and serine existed in the two patterns. Conclusions. The data points on the metabolic spectrum were found to be well distributed among the differential substances between the two typical TCM patterns of patients with hepatitis B cirrhosis using metabolomic techniques. The differential expression of these substances between GDSR and GSYX patterns provided an important objective basis for the scientific nature of TCM pattern classification at the metabolic level.

A baseline model including quantitative anti-HBc to predict response of peginterferon in HBeAg-positive chronic hepatitis B patients

2021 ◽  
pp. 135965352110598
Author(s):  
Yu-Qing Fang ◽  
Xiao-Yan Xu ◽  
Feng-Qin Hou ◽  
Wei Jia
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Treatment Response ◽  
Chronic Hepatitis B ◽  
Post Treatment ◽  
Additive Models ◽  
Core Antigen ◽  
Serological Response ◽  
New Model ◽  
Positive Chronic Hepatitis

Background Few models to predict antiviral response of peginterferon were used in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B patients and the prediction efficacy was unsatisfied. Quantitative antibody to hepatitis B core antigen (anti-HBc) is a new predictor of treatment response. We aimed to develop a new model to identify HBeAg-positive Chinese patients who were more likely to respond to peginterferon. Methods Data from 140 peginterferon recipients with HBeAg-positive were applied with generalized additive models and multiple logistic regression analysis to develop a baseline scoring system to predict serological response (SR: HBeAg loss and HBeAg seroconversion 24 weeks post-treatment) and combined response (CR: SR plus serum HBV DNA levels <2000 IU/mL 24 weeks post-treatment). Results Anti-HBc levels, alanine aminotransferase ratio, and HBeAg were retained in the final model. The new model scored from 0 to 3. Among patients with scores of 0, 1, or ≥2, SR was achieved in 6.45% (2/31), 13.21% (7/51), and 55.36% (31/56), respectively, and CR in 3.23% (1/31), 9.43% (5/53), and 25.00% (14/56), respectively. Our model has a higher AUROC for SR comparing to Chan’s (Z = 2.77 > 1.96, p < 0.05) and Lampertico’s (Z = 2.06 > 1.96, p < 0.05) model. The negative predictive value for SR and CR were both 100% in patients with score 0 and hepatitis B surface antigen ≥20,000 IU/mL at week 12. Conclusions Patients with higher scores at baseline were more likely to respond to peginterferon. This new model may predict the treatment response.

SAT-423-Spleen extracellular volume fraction and platelet count/spleen volume ratio are accurate non-invasive markers of portal hypertension

2019 ◽  
Vol 70 (1) ◽  
pp. e820-e821
Author(s):  
Christina Levick ◽  
Michael Pavlides ◽  
DavidJ Breen ◽  
Kathryn Nash ◽  
Gideon Hirschfield ◽  
...  
Keyword(s):  
Platelet Count ◽  
Portal Hypertension ◽  
Volume Fraction ◽  
Extracellular Volume ◽  
Volume Ratio ◽  
Extracellular Volume Fraction ◽  
Spleen Volume ◽  
Non Invasive
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