scholarly journals EV-Associated miRNAs from Peritoneal Lavage are a Source of Biomarkers in Endometrial Cancer

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 839 ◽  
Author(s):  
Berta Roman-Canal ◽  
Cristian Pablo Moiola ◽  
Sònia Gatius ◽  
Sarah Bonnin ◽  
Maria Ruiz-Miró ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Operating Characteristic ◽  
Peritoneal Lavage ◽  
Characteristic Curve ◽  
Unmet Need ◽  
Classification Performance ◽  
Novel Approach ◽  
Highly Sensitive ◽  
Aggressive Histology ◽  
Mirna Content

Endometrial cancer (EC) is the sixth most common cancer in women worldwide and is responsible for more than 89,000 deaths every year. Mortality is associated with presence of poor prognostic factors at diagnosis, i.e., diagnosis at an advanced stage, with a high grade and/or an aggressive histology. Development of novel approaches that would permit us to improve the clinical management of EC patients is an unmet need. In this study, we investigate a novel approach to identify highly sensitive and specific biomarkers of EC using extracellular vesicles (EVs) isolated from the peritoneal lavage of EC patients. EVs of peritoneal lavages of 25 EC patients were isolated and their miRNA content was compared with miRNAs of EVs isolated from the ascitic fluid of 25 control patients. Expression of the EV-associated miRNAs was measured using the Taqman OpenArray technology that allowed us to detect 371 miRNAs. The analysis showed that 114 miRNAs were significantly dysregulated in EC patients, among which eight miRNAs, miRNA-383-5p, miRNA-10b-5p, miRNA-34c-3p, miRNA-449b-5p, miRNA-34c-5p, miRNA-200b-3p, miRNA-2110, and miRNA-34b-3p, demonstrated a classification performance at area under the receiver operating characteristic curve (AUC) values above 0.9. This finding opens an avenue for the use of EV-associated miRNAs of peritoneal lavages as an untapped source of biomarkers for EC.

Three plasma-based microRNAs as potent diagnostic biomarkers for endometrial cancer

2021 ◽  
pp. 1-12
Author(s):  
Xingchen Fan ◽  
Minmin Cao ◽  
Cheng Liu ◽  
Cheng Zhang ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Diagnostic Value ◽  
Diagnostic Biomarkers ◽  
Non Invasive ◽  
Polymerase Chain ◽  
Public Datasets ◽  
Plasma Mirnas

BACKGROUND: MicroRNAs (miRNAs), with noticeable stability and unique expression pattern in plasma of patients with various diseases, are powerful non-invasive biomarkers for cancer detection including endometrial cancer (EC). OBJECTIVE: The objective of this study was to identify promising miRNA biomarkers in plasma to assist the clinical screening of EC. METHODS: A total of 93 EC and 79 normal control (NC) plasma samples were analyzed using Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) in this four-stage experiment. The receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic value. Additionally, the expression features of the identified miRNAs were further explored in tissues and plasma exosomes samples. RESULTS: The expression of miR-142-3p, miR-146a-5p, and miR-151a-5p was significantly overexpressed in the plasma of EC patients compared with NCs. Areas under the ROC curve of the 3-miRNA signature were 0.729, 0.751, and 0.789 for the training, testing, and external validation phases, respectively. The diagnostic performance of the identified signature proved to be stable in the three public datasets and superior to the other miRNA biomarkers in EC diagnosis. Moreover, the expression of miR-151a-5p was significantly elevated in EC plasma exosomes. CONCLUSIONS: A signature consisting of 3 plasma miRNAs was identified and showed potential for the non-invasive diagnosis of EC.

scholarly journals IoT information theft prediction using ensemble feature selection

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Joffrey L. Leevy ◽  
John Hancock ◽  
Taghi M. Khoshgoftaar ◽  
Jared M. Peterson
Keyword(s):  
Feature Selection ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Feature Reduction ◽  
Security Risk ◽  
Precision Recall Curve ◽  
Iot Devices ◽  
Feature Selection Techniques ◽  
Better Than

AbstractThe recent years have seen a proliferation of Internet of Things (IoT) devices and an associated security risk from an increasing volume of malicious traffic worldwide. For this reason, datasets such as Bot-IoT were created to train machine learning classifiers to identify attack traffic in IoT networks. In this study, we build predictive models with Bot-IoT to detect attacks represented by dataset instances from the Information Theft category, as well as dataset instances from the data exfiltration and keylogging subcategories. Our contribution is centered on the evaluation of ensemble feature selection techniques (FSTs) on classification performance for these specific attack instances. A group or ensemble of FSTs will often perform better than the best individual technique. The classifiers that we use are a diverse set of four ensemble learners (Light GBM, CatBoost, XGBoost, and random forest (RF)) and four non-ensemble learners (logistic regression (LR), decision tree (DT), Naive Bayes (NB), and a multi-layer perceptron (MLP)). The metrics used for evaluating classification performance are area under the receiver operating characteristic curve (AUC) and Area Under the precision-recall curve (AUPRC). For the most part, we determined that our ensemble FSTs do not affect classification performance but are beneficial because feature reduction eases computational burden and provides insight through improved data visualization.

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

scholarly journals A New Risk Index Combining d-Dimer, Fibrinogen, HE4, and CA199 Differentiates Suspecting Endometrial Cancer From Patients With Abnormal Vaginal Bleeding or Discharge

2020 ◽  
Vol 19 ◽  
pp. 153303381990111 ◽  
Author(s):  
Lili Ge ◽  
Guangquan Liu ◽  
Kai Hu ◽  
Ke Huang ◽  
Mi zhang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Vaginal Bleeding ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Risk Index ◽  
Abnormal Vaginal Bleeding ◽  
D Dimer ◽  
Receiver Operating

Purpose: To establish an efficient new risk index for screening patients with endometrial cancer from patients with abnormal vaginal bleeding or discharge. Method: A total of 254 patients with abnormal vaginal bleeding or discharge were included in this study. Several candidate markers, including HE4, CA125, CA199, CA153, AFP, CEA, d-dimer, and fibrinogen, were employed. A new risk index for endometrial cancer screening was established by binary logistic regression. The diagnostic value of the candidate markers and the new risk index were assessed by a receiver operating characteristic curve, sensitivity, and specificity. Results: The most valuable diagnostic indicator for endometrial cancer was HE4, followed by d-dimer and then fibrinogen (area under the receiver operating characteristic curve: HE4 = 0.794, d-dimer = 0.717, fibrinogen = 0.690). The new risk index was superior to a single application of markers and a widely used combination (HE4 and CA125). At the ideal cutoff level, the sensitivity and specificity were 91.34% and 70.08%, respectively. In addition, only patients without organic disease served as controls, which further increase its performance (area under the receiver operating characteristic curve = 0.932, sensitivity = 94.49%, and specificity = 77.42%). Conclusions: The new risk index combining HE4, d-dimer, fibrinogen, and CA199 was the ideal combination for the screening of endometrial cancer. As a simple, rapid, nondestructive detection method, the new risk index is worth promotion in clinical practice, especially in primary medical institutions.

scholarly journals Serum procacitonin as a diagnostic marker of bacterial infection in febrile children

2017 ◽  
Vol 4 (4) ◽  
pp. 1381
Author(s):  
Brindha K. ◽  
Kumar Manickam ◽  
Santhakumari Ulagaratchagan ◽  
Mohan Kumar ◽  
Sowmya Sampath ◽  
...  
Keyword(s):  
Bacterial Infection ◽  
Bacterial Infections ◽  
Operating Characteristic ◽  
Diagnostic Marker ◽  
Characteristic Curve ◽  
Reactive Protein ◽  
Highly Sensitive ◽  
Hs Crp ◽  
Better Than

Background: Early and accurate diagnosis of bacterial infections in children is important as the outcome is dependent on it. Various tests and biomarkers have been used for this among which serum procalcitonin shows a lot of promise. The aim of the study was to determine the role of serum procalcitonin as a diagnostic marker of bacterial infection in febrile children.Methods: All acutely febrile children between 6 months to 12 years of age were enrolled in this prospective study. The efficacy of procalcitonin (PCT), highly sensitive C-reactive protein (hs CRP) and absolute neutrophil count (ANC) in diagnosing bacterial infections was compared.Results: Among the three parameters, PCT has the highest area under the receiver operating characteristic curve (AUC) (O.755), followed by CRP (AUC 0.717) and ANC (AUC 0.628).Conclusions: In summary, our study showed that PCT performs better than hs CRP and ANC in detecting bacterial infection in febrile children. 

scholarly journals Detecting web attacks using random undersampling and ensemble learners

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Richard Zuech ◽  
John Hancock ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Operating Characteristic ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Class Imbalance ◽  
Classification Performance ◽  
Web Attacks ◽  
Random Undersampling ◽  
Precision Recall Curve ◽  
Research Questions ◽  
Recall Curve

AbstractClass imbalance is an important consideration for cybersecurity and machine learning. We explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 dataset. This study considers a total of eight random undersampling (RUS) ratios: no sampling, 999:1, 99:1, 95:5, 9:1, 3:1, 65:35, and 1:1. Additionally, seven different classifiers are employed: Decision Tree (DT), Random Forest (RF), CatBoost (CB), LightGBM (LGB), XGBoost (XGB), Naive Bayes (NB), and Logistic Regression (LR). For classification performance metrics, Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC) are both utilized to answer the following three research questions. The first question asks: “Are various random undersampling ratios statistically different from each other in detecting web attacks?” The second question asks: “Are different classifiers statistically different from each other in detecting web attacks?” And, our third question asks: “Is the interaction between different classifiers and random undersampling ratios significant for detecting web attacks?” Based on our experiments, the answers to all three research questions is “Yes”. To the best of our knowledge, we are the first to apply random undersampling techniques to web attacks from the CSE-CIC-IDS2018 dataset while exploring various sampling ratios.

Shabaz–Urvashi Link Prediction (SULP): A Novel Approach to Predict Future Friends in a Social Network

2020 ◽  
pp. 097325862097418
Author(s):  
Mohammad Shabaz ◽  
Urvashi Garg
Keyword(s):  
Link Prediction ◽  
Operating Characteristic ◽  
Minimum Degree ◽  
Characteristic Curve ◽  
Research Work ◽  
Empirical Theory ◽  
Statistical Measures ◽  
Novel Approach ◽  
Prediction Technique ◽  
Prediction Techniques

With the growth of social networks, the problem of linking the isolated or missing nodes appears. Thus, link prediction comes into existence to resolve this problem. Link prediction may be defined as an approach to predict an optimistic relationship that may exist or is likely to exist between nodes. Predicting the prospect link formed in future between nodes either in a dense or sparse network, the number of techniques exist intending to establish a link based on a certain similarity between the nodes. After conducting in-depth research on almost every link prediction technique, we reach the conclusion that every technique evaluates the probability score to predict future links. This research work discusses almost every previous technique and puts forward a comparatively similar technique for link prediction. The proposed technique is named Shabaz–Urvashi Link Prediction (SULP), which is based on a formula derived from an empirical theory after making a node matrix and altering the position of the neighbouring nodes, which states, ‘A node is predicted to establish a friendship if it has a maximum degree in its common neighbouring row and a minimum degree in its common neighbouring column’. SULP is tested using established datasets and compared with other link prediction techniques on the statistical measures such as Area Under Receiver Operating characteristic Curve (AUROC), precision and recall. SULP performs better as compared to other link prediction techniques on most of the testing datasets.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

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