area under curve
Recently Published Documents


TOTAL DOCUMENTS

366
(FIVE YEARS 199)

H-INDEX

19
(FIVE YEARS 5)

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Matthew Feeback ◽  
Bailey Reitsma

Introduction: To further understand the effects of L-arginine on both its ability to enhance lactate clearance and increase overall blood flow before, during, and after exhaustive anaerobic exercise. Methods:  Twelve healthy male subjects between the ages of 18-25 first completed an initial visit where baseline data was recorded. Subjects completed three additional visits, in which they ingested either a placebo, two-gram or four-gram dose of L-arginine. Blood flow (BF) and lactate were recorded before ingestion of the treatment, 5 and 15-minutes post-ingestion prior to performing a push-up test to volitional fatigue.  Immediately following the push-up test, BF and lactate were assessed and again 15-minutes post exercise. Results: Blood lactate did not differ across condition (p=0.569).  Lactate clearance was not influenced by L-arginine when analyzing the Area Under Curve.  Blood flow increased with ingestion of the four-gram dose while at rest (the 15-minute mark).  Blood flow was also enhanced in the four-gram dose immediately after exercise at the 25-minute mark.  Conclusions: The data suggests that a four-gram dose of L-arginine plays a more significant role in blood flow than the clearance of lactate after exercise compared to a two-gram dose or placebo.


2022 ◽  
Author(s):  
lihua zhang ◽  
jinnan Feng ◽  
di jin ◽  
zekun yu ◽  
mingyang qu ◽  
...  

Abstract This study aims to explore the predictive value of LUSsc(Lung Ultrasound Score) in the selection of respiratory support mode for premature infants with dyspnea.We prospectively included 857 preterm infants and performed LU in the first 2 hours of admission and scored LUSsc by two specialist sonographers. They were divided into two stratification according to gestational age (<32 +0 weeks and 32 +0 -36+6 weeks), and had two main outcomes: invasive and non-invasive respiratory support. In the training set, analysis the clinical factors finding the best cut-off value of lung ultrasound score then verified the consistency in the verification set. The choice of invasive respiratory support is based on neonatal mechanical ventilation rules. Preterm infants with invasive respiratory support had higher LUS scores and lower OI 、birth weight、than those with non-invasive support. For preterm <32 +0 weeks the cut-off point of LUSsc was 6.5 that the area under curve was 0.749 (95% CI: 0.689-0.809), which was statistically significant (P<0.05), and the sensitivity and specificity were 74.0% and 68.3%, for preterm 32 +0 -36 +6 weeks, cut-off point was 6.5 and the area under curve was 0.863 (95% CI: 0.811-0.911), sensitivity and specificity were 75.3% and 0.836%.In the validation set, use actual clinical respiratory support selection results to verify, for preterm <32 +0 weeks (Kappa value 0.660, P<0.05, McNemar test P >0.05),for preterm 32 +0 -36 +6 weeks (Kappa value 0.779, P<0.05, McNemar test P >0.05). Conclusion: The LUS score shows good reliability to predict respiratory support mode for preterm infants with dyspnea


2022 ◽  
Vol 9 (3) ◽  
pp. 64-67
Author(s):  
Ishwarya Ramadoss ◽  
Anandaraj Jayaraman ◽  
Shobana Dhanapal

Abstract Aims :To compare the NAFLD fibrosis score and FIBROSIS 4 score to fibroscan, and affirm whether the scores shall be used as a screening tool for liver fibrosis, in place of fibroscan. Methodology: It was a cross-sectional study. Patients with fatty liver on ultrasonological examination with 200 sample size. After obtaining the informed consent the following details were collected socio-demographic details, history, co-morbidities, anthropometric measurements, Laboratory investigations. Results: the ROC curve analysis of fibroscan reveals the area under curve of 0.499 and based on the cut off value of 4.50Kpas the sensitivity and specificity was found to be 85.7% and 83.5% respectively. The ROC curve analysis of fibrosis-4 reveals the area under curve of 0.495 and based on the cut off value of 0.80 the sensitivity and specificity was found to be 91.9% and 92.1% respectively. Analysis of NAFLD fibrosis score reveals the area under curve of 0.476 and based on the cut off value of -1.53 the sensitivity and specificity was found to be 93.1% and 93.9% respectively. Conclusion: Henceforth the study suggests that NAFLD fibrosis score shall be used as a non -invasive bedside assessment of liver fibrosis in high risk population and hence guiding their follow up for prevention of morbidity in resource limited settings.


2021 ◽  
Vol 7 ◽  
pp. e822
Author(s):  
Zhisheng Yang ◽  
Jinyong Cheng

In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hongtao Tian ◽  
Yan Zhao ◽  
Chao Du ◽  
Xiao Zong ◽  
Xiuping Zhang ◽  
...  

Aim. To explore the expression levels of miR-210, miR-137, and miR-153 in patients with acute cerebral infarction. Material and Methods. 76 patients with acute cerebral infarction treated in our hospital from April 2016 to October 2017 were enrolled as the observation group. Another 64 normal patients were selected as the control group. The patients were divided into the death and survival groups based on 1-year mortality of patients. qRT-PCR was used to detect the expression of miR-210, miR-137, and miR-153 in the serum of each group. Receiver operating characteristic (ROC) curve was employed to analyze the diagnostic value and predictive value of miR-210, miR-137 and miR-153 death in patients. The correlation between miR-210, miR-137, and miR-153 in the serum of the observation group was analyzed by Pearson’s test. Results. Levels of miR-210 and miR-137 in the observation group were significantly lower than those in the control group, while levels of miR-153 in the observation group were significantly higher than those in the control group (all P < 0.05 ). The ROC curve of diagnosis of acute cerebral infarction showed that the area under curve of miR-210 was 0.836, that of miR-137 was 0.843, and that of miR-153 was 0.842. The 1-year survival rate was 71.05%. The 1-year survival of the low-expression group of miR-210 and miR-137 was significantly lower than that of the high-expression group, while the 1-year survival of the low-expression group of miR-153 was significantly higher than that of the high-expression group (all P < 0.05 ). The ROC curve for predicting death showed that the area under curve of miR-210 was 0.786, that of miR-137 was 0.824, and that of miR-153 was 0.858. Pearson’s correlation analysis showed that the expression of miR-210 was positively correlated with that of miR-137, while miR-137 was negatively correlated with that of miR-153 and miR-210 was negatively correlated with that of miR-153. Conclusion. miR-210, miR-137, and miR-153 have a certain value in the diagnosis and prediction of 1-year death of acute cerebral infarction and may be potential diagnostic and predictive indicators.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wu-Hao Lin ◽  
Jian Xiao ◽  
Zi-Yi Ye ◽  
Da-Liang Wei ◽  
Xiao-Hui Zhai ◽  
...  

Abstract Background Circulating tumor DNA (ctDNA) is a promising diagnostic and prognostic marker for many cancers and has been actively investigated in recent years. Previous studies have already demonstrated the potential use of ctDNA methylation markers in the diagnosis and prognostication of colorectal cancer (CRC). This retrospective study validated the value of methylation biomarker MYO1-G (cg10673833) in CRC diagnosis and disease monitoring using digital droplet PCR (ddPCR), a biomarker selected from our previous study due to its highest diagnostic efficiency. Methods Blood samples of CRC and control samples from tumor-free individuals at two institutions were collected to quantify the methylation ratio using ddPCR. Area under curve (AUC) was calculated after constructing receiver operating characteristic curve (ROC) for CRC diagnosis. Sensitivity and specificity were estimated and comparisons of methylation ratio in different groups were performed. Results We collected 673 blood samples from 272 patients diagnosed with stage I-IV CRC and 402 normal control samples. The methylation biomarker discriminated patients with CRC from normal controls with high accuracy (area under curve [AUC] = 0.94) and yielded a sensitivity of 84.3% and specificity of 94.5%. Besides, methylation ratio of MYO1-G was associated with tumor burden and treatment response. The methylation ratio was significantly lower in patients after their radical operation than when compared with those before surgeries (P < 0.001). Methylation ratio was significantly higher in patients with disease progression than those with stable disease (P = 0.002) and those with complete response or partial response (P = 0.009). Conclusions Together, our study indicated that this methylation marker can serve as a potential biomarker for diagnosing and monitoring CRC.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 903-904
Author(s):  
Rachel Logue ◽  
Susan Brown ◽  
Rebecca Hasson ◽  
Matthew Davis

Abstract Grip strength is commonly used to assess hand function in older adults and is associated with health outcomes including muscle strength, cognition, and mortality. However, the degree to which grip strength predicts an actual hand limitation is unknown. This study evaluated grip strength as a predictor of hand limitations associated with activities of daily living. Using the 2011-14 National Health and Nutrition Examination Survey (NHANES), we selected five self-reported hand-related functional limitations to classify older adults reporting one or more limitations versus those with no limitations. We identified 2,064 older adults (age≥65), 31% of whom reported a hand-related limitation. Odds ratios were used to assess the association between grip strength quartile and the likelihood of a hand limitation while controlling for sex, race/ethnicity, education level, income, and pain. Receiver operator curves were used to evaluate the degree to which grip strength discriminates between those with limitations versus those without. Older adults with very low grip strength (lowest quartile) were more likely to have at least one limitation (OR:6.1, 95% CI:3.2,11.8) than those with high grip strength (highest quartile). However, receiver operator curves suggested grip strength only modestly discriminated hand limitations (area under curve:0.71). While self-reported hand limitations were associated with lower grip strength, it was a relatively poor predictor of hand impairments among older adults. This study suggests grip strength may not predict hand function as well as previously thought. Better assessments are needed to adequately evaluate upper extremity impairments to help older adults maintain functional independence.


2021 ◽  
Vol 930 (1) ◽  
pp. 012095
Author(s):  
R Aprilia ◽  
E Hidayah ◽  
D Junita K

Abstract Flood is one of the disaster threats downstream of Welang river, Pasuruan. A flood susceptibility map is needed to anticipate floods disasters. This research aimed to map flood Susceptibility in the Welang watershed using a Geographical Information System. In determining flood hazard, the Frequency Ratio (FR) approach was used. Flood locations were identified from the interpretation of field survey data as training data and model validation. The data were represented in a Digital Elevation Model (DEM) map, geological data, land use, river data, and Landsat Satellite Imagery and processed into a spatial database on the GIS platform. The factors that caused flooding consisted of Flood inventory, slope, Elevation, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Flow Accumulation, Distance to the river, River Density, Rainfall, Vegetation Index (NDVI), and Landuse. The map results with acceptable accuracy showed that the FR model gained an Area Under Curve (AUC) value of 90%, and the incidence for the Area Under Curve ( AUC ) was 93%. It is known that 1% of the flood-prone area is very high. The local Government can use the research to minimize the risk of flooding in the Welang watershed.


2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Aizhu Sheng ◽  
Aijing Li ◽  
Jianbi Xia ◽  
Yizhai Ye

Objective. The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination. Methods. Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). Results. This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other. Conclusions. The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.


2021 ◽  
Vol 11 (22) ◽  
pp. 10528
Author(s):  
Khin Yadanar Win ◽  
Noppadol Maneerat ◽  
Syna Sreng ◽  
Kazuhiko Hamamoto

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.


Sign in / Sign up

Export Citation Format

Share Document