scholarly journals Association between metabolic indicators and clinical endometritis during the transition period in Brown Swiss cows

2021 ◽  
Vol 77 (05) ◽  
pp. 6524-2021
Author(s):  
MURAT ONUR YAZLIK ◽  
HATICE ESRA COLAKOGLU ◽  
MERT PEKCAN ◽  
UFUK KAYA ◽  
SERDAL KURT ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Transition Period ◽  
Area Under The Curve ◽  
Subsequent Development ◽  
Roc Curves ◽  
Nefa Concentration ◽  
Operating Characteristics ◽  
Brown Swiss ◽  
Metabolite Profiles ◽  
The Relationship

The objective of the current study was to evaluate the relationship between the serum macromineral, metabolite profiles, and the clinical endometritis during the transition period in Brown Swiss dairy cows. Sixty Brown Swiss dairy cows were used in the present study. Blood samples collected at d 10 (± 4) antepartum and 3, 10 and 30 (± 4) days in milk (DIM) to determine calcium (Ca), phosphorus, glucose, cholesterol, non-esterified fatty acid (NEFA) levels. Beta-hydroxybutyric acid (BHB) concentration measured during the postpartum period. Receiver operating characteristics (ROC) curves were used to determine the cow-level thresholds for the subsequent development of clinical endometritis. In addition, pairwise comparisons were made of the area under the curve (AUC) of ROC curves for the thresholds of NEFA, Ca, and glucose predicting clinical endometritis. The mean Ca concentration at 3 DIM was 8.85 ± 0.20 mg/dL in healthy cows compared to 8.30 ± 0.22 mg/dL in cows that subsequently developed endometritis (P < 0.05). NEFA concentrations at 10 DIM and BHB concentration at 10 and 30 ± 4 DIM were higher (P < 0.05) in cows that subsequently developed endometritis. Serum NEFA concentration at 10 days postpartum is the best predictor for diagnosis of clinical endometritis with the AUC values of 0.741. The cows with clinical endometritis also had significantly higher values of glucose at 3 DIM and lower BCS at 10 DIM (P < 0.05). While 58.6% of the cows that developed clinical endometritis were cyclic, 64.5% were cyclic in healthy cows at 30 ± 4 DIM. Serum NEFA concentration was the only risk factor for clinical endometritis at 10 DIM. In addition, a decrease in serum Ca at 3 DIM and increase in NEFA and BHB concentrations at 10 and 30 ± 4 DIM may be associated with clinical endometritis and delayed resumption activity on the ovaries.

scholarly journals MicroRNAs Which Can Prognosticate Aggressiveness of Bladder Cancer

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1551 ◽  
Author(s):  
Edyta Marta Borkowska ◽  
Tomasz Konecki ◽  
Michał Pietrusiński ◽  
Maciej Borowiec ◽  
Zbigniew Jabłonowski
Keyword(s):  
Bladder Cancer ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Control Group ◽  
Low Grade ◽  
Operating Characteristics ◽  
High Death Rate ◽  
Biological Potential ◽  
High Death ◽  
Muscle Invasive

Bladder cancer (BC) is still characterized by a very high death rate in patients with this disease. One of the reasons for this is the lack of adequate markers which could help determine the biological potential of the tumor to develop into its invasive stage. It has been found that some microRNAs (miRNAs) correlate with disease progression. The purpose of this study was to identify which miRNAs can accurately predict the presence of BC and can differentiate low grade (LG) tumors from high grade (HG) tumors. The study included 55 patients with diagnosed bladder cancer and 30 persons belonging to the control group. The expression of seven selected miRNAs was estimated with the real-time PCR technique according to miR-103-5p (for the normalization of the results). Receiver operating characteristics (ROC) curves and the area under the curve (AUC) were used to evaluate the feasibility of using selected markers as biomarkers for detecting BC and discriminating non-muscle invasive BC (NMIBC) from muscle invasive BC (MIBC). For HG tumors, the relevant classifiers are miR-205-5p and miR-20a-5p, whereas miR-205-5p and miR-182-5p are for LG (AUC = 0.964 and AUC = 0.992, respectively). NMIBC patients with LG disease are characterized by significantly higher miR-130b-3p expression values compared to patients in HG tumors.

Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs

2021 ◽  
pp. 20200513
Author(s):  
Su-Jin Jeon ◽  
Jong-Pil Yun ◽  
Han-Gyeol Yeom ◽  
Woo-Sang Shin ◽  
Jong-Hyun Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cone Beam Ct ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Learning System ◽  
Operating Characteristics ◽  
Panoramic Radiographs ◽  
Test Sets ◽  
Sensitivity Specificity ◽  
Deep Learning Model

Objective: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Methods: Panoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions. Results: The accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals. Conclusions: The deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.

PREDICTING SOFTWARE CHANGE IN AN OPEN SOURCE SOFTWARE USING MACHINE LEARNING ALGORITHMS

2013 ◽  
Vol 20 (06) ◽  
pp. 1350025
Author(s):  
RUCHIKA MALHOTRA ◽  
ANKITA JAIN BANSAL
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Roc Analysis ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Preventive Actions ◽  
Change Proneness ◽  
The Relationship

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.

scholarly journals Optimized Combination of Circulating Biomarkers as Predictors of Prognosis in AECOPD Patients Complicated with Heart Failure

2020 ◽  
Author(s):  
Can Yao ◽  
Lingwei Wang ◽  
Fei Shi ◽  
Rongchang Chen ◽  
Binbin Li ◽  
...  
Keyword(s):  
Heart Failure ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Chronic Obstructive ◽  
Circulating Biomarkers ◽  
Operating Characteristics ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Survivor Group ◽  
Significant Difference

Abstract BackgroundSystematic inflammation, nutritional status, and cardiovascular function have been associated with the outcomes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients with heart failure (HF). However, the value of their relevant biomarkers in predicting mortality has not been well defined yet. We aimed to investigate the prognostic value of circulating biomarkers including C-reaction protein (CRP) /albumin (ALB), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), and N-terminal pro-brain natriuretic peptide (NT-proBNP) for AECOPD patients with HF.Methods A total of 146 cases of AECOPD complicated with HF were enrolled and classified into survivor group (n=94) and non-survivor group (n=52). The baseline characteristics and blood-based biomarkers were collected. The predictors for prognosis were analyzed by multivariate logistic regression, and the ability to predict 28-day mortality was evaluated by receiver operating characteristics curve (ROC) and the area under the curve (AUC).ResultsThe patients in non-survivors had significantly higher levels of CRP, CRP/ALB, NLR, PCT and NT-proBNP, but lower ALB levels compared to the survivors [145.8±110.1 VS. 66.6±70.2mg/L, 5.9±4.9 VS. 2.3±2.6, 22.2 (11.1, 40.1) VS. 12.0 (6.2, 24.8), 2.6 (0.2, 10.3) VS. 0.08 (0.1, 0.5)ng/ml, 17912.5 (9344.0, 34344.5) VS. 9809.0 (4415.9, 16387.2)ng/ml, 26.8±6.4 VS. 31.0±4.6g/L; P < 0.001, <0.001, 0.001, <0.001, <0.001, and < 0.001, respectively]. No significant difference in PLR was found between the two groups (P=0.413). The logistic analysis revealed that CRP/ALB (OR=1.303, 95%CI: 1.145-1.483, P<0.001), NT-proBNP (OR=1.041, 95%CI: 1.010-1.073, P=0.009) and NLR (OR=1.010, 95%CI: 0.999-1.022, P<0.001) are independent risk factors for predicting the 28-day mortality. The AUC of the ROC curves were 0.768, 0.767, 0.757, 0.723, 0.716, and 0.668 for CRP/ALB, PCT, CRP, NT-proBNP, ALB, and NLR, respectively. The combination of CRP/ALB, NLR and NT-proBNP as biomarkers was shown to have better accuracy for predicting prognosis (AUC=0.830, 95%CI: 0.761-0.899, P<0.001), with a higher specificity of 80.8% and specificity of 77.7% as compared with each single biomarkers.ConclusionsHigh levels of NLR, CRP/ALB and NT-proBNP may be clinical usefully predictors for death in AECOPD patients with HF. Combination of NLR with CRP/ALB and NT-proBNP can provide a higher accuracy for predicting 28-day mortality in these patients.

scholarly journals Diagnostic Biomarker KIF23 is Associated With Immune Infiltration in Gastric Cancer

2021 ◽  
Author(s):  
Maoshu Bai ◽  
Xin Liu
Keyword(s):  
Gastric Cancer ◽  
Area Under The Curve ◽  
Enrichment Analysis ◽  
Progression Free Survival ◽  
Roc Curves ◽  
Diagnostic Value ◽  
Expression Level ◽  
Diagnostic Biomarker ◽  
Immune Infiltration ◽  
The Relationship

Abstract Background: Kinesin family member 23 (KIF23), an index of tumor proliferation, can serve as a prognostic marker in numerous tumors. However, the relationship between KIF23 expression and immune infiltration and the diagnostic value of KIF23 remain unclear in GC (gastric cancer). This study aimed to explored the diagnostic value of KIF23 and its interactions with tumor-infiltrating immune cells in GC by bioinformatics analysis. Mothods: The relationship between clinicopathologic features and KIF23 expression was also analyzed using the Wilcoxon rank-sum test and logistic regression. And the expression level of KIF23 was validated by IHC and GEO databases, which was consistent with informatics results. Receiver operating characteristic (ROC) curves were generated to evaluate the value of KIF23 as a binary classifier using the area under the curve (AUC value).Results: High expression of KIF23 was significantly associated with longer overall survival and progression-free survival in GC. The mutations of KIF23 in GC were analyzed using cBioPortal and the Catalogue of Somatic Mutations in Cancer database. Enrichment analysis of co-expressed genes and KIF23 analysis was performed using LinkedOmics. By using STRING and GeneMANIA databases, we investigated the protein-coding genes related to KIF23 and its co-expression genes in GC tissues. Then, the relationship between KIF23 expression and immune infiltration in GC was investigated using Timer and GEPIA. We found that KIF23 might be used as a potential diagnostic biomarker in GC. Subsequently, KIF23 expression level was correlated with the infiltration levels of CD8 + T cells, macrophages, neutrophils, and more obviously with B cells and dendritic cells. In addition, KIF23 expression was significantly associated with T cell exhaustion (CTLA-4 and GZMB). KIF23 expression showed correlations with the infiltration of diverse immune markers in GC. Conclusions: Our findings suggest KIF23 can serve as a marker for immune infiltration and diagnostic in GC, making it a potential treatment of target.

scholarly journals Evaluation of Severity of Illness Scores in the Pediatric ECMO Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Venessa L. Pinto ◽  
Danielle Guffey ◽  
Laura Loftis ◽  
Melania M. Bembea ◽  
Philip C. Spinella ◽  
...  
Keyword(s):  
Retrospective Cohort ◽  
Pediatric Patients ◽  
Area Under The Curve ◽  
Severity Of Illness ◽  
Roc Curves ◽  
Operating Characteristics ◽  
Logistic Organ Dysfunction ◽  
Risk Of Mortality ◽  
Pediatric Icu ◽  
Pediatric Index Of Mortality

Though commonly used for adjustment of risk, severity of illness and mortality risk prediction scores, based on the first 24 h of intensive care unit (ICU) admission, have not been validated in the pediatric extracorporeal membrane oxygenation (ECMO) population. We aimed to determine the association of Pediatric Index of Mortality 2 (PIM2), Pediatric Risk of Mortality Score III (PRISM III) and Pediatric Logistic Organ Dysfunction (PELOD) scores with mortality in pediatric patients on ECMO. This was a retrospective cohort study of children ≤18 years of age included in the Pediatric ECMO Outcomes Registry (PEDECOR) from 2014 to 2018. Logistic regression and Receiver Operating Characteristics (ROC) curves were used to calculate the area under the curve (AUC) to evaluate association of mortality with the scores. Of the 655 cases, 289 (44.1%) did not survive until hospital discharge. AUCs for PIM2, PRISM III, and PELOD predicting mortality were 0.52, 0.52, and 0.51 respectively. PIM2, PRISM III, and PELOD scores are not associated with odds of mortality for pediatric patients receiving ECMO. These scores for a general pediatric ICU population should not be used for prognostication or risk stratification of a select population such as ECMO patients.

Similarity and Confidence in Artificial Grammar Learning

2010 ◽  
Vol 57 (2) ◽  
pp. 160-168 ◽  
Author(s):  
Richard J. Tunney
Keyword(s):  
Roc Curves ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
Regression Analyses ◽  
Operating Characteristics ◽  
Test Items ◽  
Grammar Learning ◽  
Similarity Ratings ◽  
The Relationship ◽  
Confidence Ratings

Three experiments examined the relationship between similarity ratings and confidence ratings in artificial grammar learning. In Experiment 1 participants rated the similarity of test items to study exemplars. Regression analyses revealed these to be related to some of the objective measures of similarity that have previously been implicated in categorization decisions. In Experiment 2 participants made grammaticality decisions and rated either their confidence in the accuracy of their decisions or the similarity of the test items to the study items. Regression analyses showed that the grammaticality decisions were predicted by the similarity ratings obtained in Experiment 1. Points on the receiver operating characteristics (ROC) curves for the similarity and confidence ratings were closely matched. These data suggest that meta-cognitive judgments of confidence are predicated on structural knowledge of similarity. Experiment 3 confirmed this by showing that confidence ratings to median similarity probe items changed according to the similarity of preceding items.

scholarly journals Serum Exosomal MicroRNAs Predict Acute Respiratory Distress Syndrome Events in Patients with Severe Community-Acquired Pneumonia

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Wu ◽  
Chengzhi Wu ◽  
Wenyu Gu ◽  
Haiying Ji ◽  
Lei Zhu
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Community Acquired Pneumonia ◽  
Operating Characteristics ◽  
Predictive Values ◽  
Severe Community Acquired Pneumonia

Background. Severe community-acquired pneumonia (SCAP) requiring intensive care unit (ICU) treatment commonly causes acute respiratory distress syndrome (ARDS) with high mortality. This study was aimed at evaluating whether microRNAs (miRNAs) in circulating exosomes have the predictive values for patients at risk of developing ARDS due to SCAP. Methods. ARDS/ALI-relevant miRNAs were obtained by literature search. Exosomes in serum were isolated by ultracentrifugation method and identified by Transmission Electron Microscopy. Then the miR profiling in the exosomes using real-time PCR was analyzed in SCAP patients with or without ARDS. Moreover, multivariate Cox proportional regression analysis was performed to estimate the odds ratio of miRNA for the occurrence of ARDS and prognosis. The receiver operating characteristics (ROC) curves were calculated to discriminate ARDS cases. Finally, the Kaplan-Meier curve using log-rank method was performed to test the equality for survival distributions with different miRNA classifiers. Results. A total of 53 SCAP patients were finally recruited. Ten miRNAs were picked out. Further, a subset of exosomal miRNAs, including the miR-146a, miR-27a, miR-126, and miR-155 in ARDS group exhibited significantly elevated levels than those in non-ARDS group. The combined expression of miR-126, miR-27a, miR-146a, and miR-155 predicted ARDS with an area under the curve of 0.909 (95 % CI 0.815 –1). Only miR-126 was selected to have potential to predict the 28-day mortality (OR=1.002, P=0.024) with its median value classifier. Conclusions. The altered levels of circulating exosomal microRNAs may be useful biologic confirmation of ARDS in patients with SCAP.

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