scholarly journals Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingwei Zhang ◽  
Hong Chen ◽  
Bo Liang ◽  
Xuezhen Wang ◽  
Ning Gu ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Prognostic Value ◽  
Learning Algorithm ◽  
Systematic Assessment ◽  
Multiple Datasets ◽  
Confounding Variables ◽  
Tumorigenesis And Progression ◽  
Model Improvement ◽  
The One ◽  
Value Model

Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, the CIBERSORTx algorithm and ssGSEA were implemented for functional analysis. Patients with high mRNAsi/c_mRNAsi GBM showed better prognosis among the four GBM cohorts. After identifying the confounding variables, c_mRNAsi still maintained its prognostic value. Model evaluation showed that the c_mRNAsi-based model performed well. Patients with high c_mRNAsi exhibited significant immune suppression. Moreover, c_mRNAsi correlated negatively with infiltrating levels of immune-related cells. In addition, ssGSEA revealed that immune-related pathways were generally activated in patients with high c_mRNAsi. We comprehensively evaluated GBM stemness indices based on large cohorts and established a c_mRNAsi-based classifier for prognosis prediction.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

scholarly journals Prognostic value of the neutrophil-to-lymphocyte ratio in acute organophosphorus pesticide poisoning

2021 ◽  
Vol 16 (1) ◽  
pp. 703-710
Author(s):  
Yuhang Mu ◽  
Boqi Hu ◽  
Nan Gao ◽  
Li Pang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Organophosphorus Pesticide ◽  
Neutrophil To Lymphocyte Ratio ◽  
Pesticide Poisoning ◽  
Lymphocyte Ratio

Abstract This study investigates the ability of blood neutrophil-to-lymphocyte ratio (NLR) to predict acute organophosphorus pesticide poisoning (AOPP). Clinical data of 385 patients with AOPP were obtained within 24 h of admission, and NLR values were calculated based on neutrophil and lymphocyte counts. The patients were divided into two groups – good and poor – based on prognosis. Poor prognosis included in-hospital death and severe poisoning. The factors affecting prognosis were analyzed by logistic regression analysis, and the prognostic value of NLR was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that NLR levels, serum cholinesterase, and creatinine levels were good predictors of AOPP. Multivariate logistic regression analysis showed that high NLR was an independent risk factor for severe poisoning (adjusted odds ratio [AOR], 1.13; 95% CI, 1.10–1.17; p < 0.05) and in-hospital mortality (AOR, 1.07; 95% CI, 1.03–1.11; p < 0.05). NLR values >13 and >17 had a moderate ability to predict severe poisoning and in-hospital mortality, respectively (AUC of 0.782 [95% CI, 0.74–0.824] and 0.714 [95% CI, 0.626–0.803], respectively). Our results show that high NLR at admission is an independent indicator of poor prognosis in AOPP and can be used to optimize treatment and manage patients.

The Theory and Prevention of Undamped Aeroplane Nosewheel Shimmy

1956 ◽  
Vol 7 (3) ◽  
pp. 193-220
Author(s):  
D. Williams
Keyword(s):  
Mathematical Theory ◽  
The Other ◽  
Linkage Mechanism ◽  
Model Experiments ◽  
Good Feature ◽  
The One ◽  
Value Model ◽  
Elastic Constraint

SummaryThe mathematical theory of nosewheel shimmy is given, with particular reference to twin nosewheel assemblies. It is shown that a sovereign remedy for shimmy is to make the castor length greater than what is here called the “ creep distance,” which in practice is found to be approximately equal to the tyre radius. Lateral flexibility of the oleo leg is disadvantageous but elastic constraint at the pivot is a good feature. The one necessitates an increased castor for stability while the other allows a smaller castor. It is also shown how, by the use of a compact linkage mechanism, the effective castor length can be made independent of the wheel-leg offset and can have any desired value. Model experiments that confirm the theoretical conclusions are described.

scholarly journals Evaluation of serum ATX and LPA as potential diagnostic biomarkers in patients with pancreatic cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiang Chen ◽  
Hongyu Li ◽  
Wenda Xu ◽  
Xiaozhong Guo
Keyword(s):  
Pancreatic Cancer ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Potential Role ◽  
Early Stage ◽  
Serum Levels ◽  
Diagnostic Efficacy ◽  
Diagnostic Biomarkers ◽  
The Poor

Abstract Background Pancreatic cancer (PC) is a devastating disease that has a poor prognosis and a total 5-year survival rate of around 5%. The poor prognosis of PC is due in part to a lack of suitable biomarkers that can allow early diagnosis. The lysophospholipase autotaxin (ATX) and its product lysophosphatidic acid (LPA) play an essential role in disease progression in PC patients and are associated with increased morbidity in several types of cancer. In this study, we evaluated both the potential role of serum LPA and ATX as diagnostic markers in PC and their prognostic value for PC either alone or in combination with CA19-9. Methods ATX, LPA and CA19-9 levels were evaluated using ELISA of serum obtained from PC patients (n = 114) healthy volunteers (HVs: n = 120) and patients with benign pancreatic diseases (BPDs: n = 94). Results Serum levels of ATX, LPA and CA19-9 in PC patients were substantially higher than that for BPD patients or HVs (p < 0.001). The sensitivity of LPA in early phase PC was 91.74% and the specificity of ATX was 80%. The levels of ATX, LPA and CA19-9 were all substantially higher for early stage PC patients compared to levels in serum from BPD patients and HVs. The diagnostic efficacy of CA19-9 for PC was significantly enhanced by the addition of ATX and LPA (p = 0.0012). Conclusion Measurement of LPA and ATX levels together with CA19-9 levels can be used for early detection of PC and diagnosis of PC in general.

scholarly journals Estimation of the presence of small dense lipoprotein cholesterol in acute ischemic stroke

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Yasuhiro Manabe ◽  
Ryuta Morihara ◽  
Kosuke Matsuzono ◽  
Yumiko Nakano ◽  
Yoshiaki Takahashi ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Poor Prognosis ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Standard Laboratory ◽  
Laboratory Methods ◽  
Established Risk Factor ◽  
The One ◽  
Lipid Parameters

Small dense low-density lipoprotein (sdLDL) is an established risk factor in ischemic heart disease. However, its clinical significance in acute ischemic stroke (AIS) is uncertain. This study evaluates the prognostic value of the presence of sdLDL in patients with AIS by determining whether it contributes to clinical outcome or not. We studied 530 consecutive patients admitted within the first 48 hours after onset of ischemic stroke and 50 corresponding controls. Serum lipid parameters were measured on admission by standard laboratory methods. The percentage of AIS patients with sdLDL was significantly higher than the one of matched controls with sdLDL. Concerning comparisons between AIS patients with or without sdLDL, the percentages of males and patients with histories of smoking, hypertension, and cardiovascular disease were significantly higher in AIS patients with sdLDL. Concerning the grade of severity, modified Rankin Scale (mRS) on discharge was significantly higher in AIS patients with sdLDL. On logistic regression analysis, age (OR=2.29, P3). Our study showed that the presence of sdLDL might be independently associated with a poor prognosis after AIS.

scholarly journals The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

2020 ◽  
Author(s):  
xuyang ma ◽  
Ying Ding ◽  
Li Zeng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Normal Liver ◽  
Diagnostic Value ◽  
The Relationship ◽  
Diagnostic And Prognostic Value

Abstract Background: The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods: The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result: Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage (grade) and T stage (tumor stage) of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion: Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

scholarly journals Circulating tumor cells: a valuable marker of poor prognosis for advanced nasopharyngeal carcinoma

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
Guoping Ou ◽  
Shan Xing ◽  
Jianpei Li ◽  
Lin Zhang ◽  
Shulin Chen
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Roc Curve ◽  
Poor Prognosis ◽  
Regression Models ◽  
Prognostic Value ◽  
Stage Iv ◽  
Stage Iii ◽  
Ebv Dna

Abstract Purpose To evaluate the prognostic value of circulating tumor cells (CTCs) in nasopharyngeal carcinoma (NPC). Methods Cox’s proportional hazards regression models were used to identify whether CTCs was a poor prognostic factor for NPC. Chi-square tests were used to analyze and compare the distribution characteristics of CTCs in NPC. ROC curve was used to estimate the cut-off point of CTCs. Kaplan-Meier survival analyses were used to observe the prognostic value of CTCs alone and in combined with Epstein-Barr Virus DNA (EBV-DNA). Results CTCs was confirmed to be an independent risk factor for poor prognosis of NPC by Cox’s regression models that enrolled 370 NPC cases and took age, gender, EBV-DNA and CTCs as variables. The proportion of CTCs in stage IV NPC was statistically different from that in stage III; the cut-off point of CTCs between stage IV (288 cases) and stage III (70 cases) NPC estimated by ROC curve was 0.5. The prognosis of advanced NPC patients became worse with the increase of CTCs count. The combined detection of CTCs and EBV-DNA could better predict the prognosis of NPC compared with the single detection of EBV-DNA.

Non-Judicial, Advisory, Yet Impactful? The Aarhus Convention Compliance Committee as a Gateway to Environmental Justice

2020 ◽  
Vol 9 (2) ◽  
pp. 211-238
Author(s):  
Gor Samvel
Keyword(s):  
Quantitative Assessment ◽  
Soft Law ◽  
Binding Effect ◽  
Systematic Assessment ◽  
Aarhus Convention ◽  
Legal Effect ◽  
Domestic Practice ◽  
The One ◽  
The Impact ◽  
Auxiliary Role

AbstractIn accordance with Article 15 of the Aarhus Convention, the first meeting of the parties to this Convention established a non-judicial and consultative Compliance Committee to consider, among other matters, individual cases concerning compliance by parties with their obligations. The Committee is traditionally viewed as a non-judicial, soft mechanism and its rulings as non-binding, soft law. In recent years, however, to support the claim that rulings of the Committee have an impact and legal effects, some scholars have departed from the traditional perspective and characterized the Committee as a more judicialized mechanism, which issues legally binding rulings.This characterization assumes a correlation between judicialization and binding effect on the one hand, and legal effect on the other. The latter claim, however, has not been supported by a systematic assessment of the impact of the Committee's rulings on domestic practice. Against this background, the article assesses the impact of Article 9-related rulings of the Committee, issued between 2004 and 2012, on national legal orders. The assessment reveals that in fewer than 41% of the cases parties recorded some degree of compliance with the rulings of the Committee, whereas in 59% they recorded no progress. The quantitative assessment and respective qualitative insights, among other factors, suggest that the normative character of the Committee and its rulings play an auxiliary role in the process of ensuring compliance with the provisions of the Aarhus Convention. The decision of parties to comply is determined typically by the substance of the rulings as they stand in relation to domestic circumstances rather than by the institutional features of the Committee and binding effect of its rulings.

scholarly journals Analysis on the Expression and Prognostic Value of LncRNA FAF in Patients with Coronary Heart Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hai Xu ◽  
Xiwen Zhang ◽  
Kun Yu ◽  
Gang Zhang ◽  
Yafei Shi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
High Sensitivity ◽  
Control Group ◽  
Coronary Lesion ◽  
Tnf Α ◽  
Factor Α

Objective. To investigate the expression and prognostic value of LncRNA FAF in patients with coronary heart disease. Patients and Methods. 97 patients with coronary heart disease who came to our hospital were selected as the research group (RG), and 97 healthy people who came to our hospital for physical examination during the same period were selected as the control group (CG). The serum LncRNA FAF, plasma homocysteine (HCY), lipoprotein A (Lp-a), serum tumor necrosis factor α (TNF-α), and high-sensitivity C-reactive protein (hsCRP) in the two groups of patients were detected, and their correlations were analyzed. Then, the predictive value and risk factors of FAF for poor prognosis of patients with coronary heart disease were analyzed. Results. The expression of LncRNA FAF in the serum of patients in the RG was significantly lower than that in the CG, and the expressions of HCY, Lp-a, TNF-α, and hsCRP were significantly higher than those in the CG (p <0.05). The AUC of FAF in the diagnosis of coronary heart disease was more than 0.9. FAF was negatively correlated with the coronary lesion vessels, HCY, Lp-a, TNF-α, and hsCRP expressions in patients with coronary heart disease ( p < 0.05 ). The ROC of FAF for predicting poor prognosis in patients with coronary heart disease was greater than 0.9. Low expression of FAF; high expressions of HCY, Lp-a, and hsCRP; and increase of coronary lesion vessels were independent risk factors for poor prognosis in patients with coronary heart disease. Conclusions. LncRNA FAF was lowly expressed in the serum of patients with coronary heart disease, and it was of high value in the diagnosis and prediction of poor prognosis of coronary heart disease. It was also an independent risk factor for poor prognosis of patients with coronary heart disease and may be a potential target for diagnosis and treatment of coronary heart disease.

scholarly journals 153 Multiparametric assessment of diastolic function in heart failure

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
T Uejima ◽  
J Cho ◽  
H Hayama ◽  
L Takahashi ◽  
J Yajima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cluster Analysis ◽  
Diastolic Function ◽  
Primary Endpoint ◽  
Prognostic Value ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm

Abstract Background The assessment of diastolic function is still challenging in the setting of heart failure (HF). We tested the hypothesis that applying a machine learning algorithm would detect heterogeneity in diastolic function and improve risk stratification in HF population. Methods This study included consecutive 279 patients with clinically stable HF referred for echocardiographic assessment, for whom diastolic function variables were measured according to the current guidelines. Cluster analysis, an unsupervised machine learning algorithm, was undertaken on these variables to form homogeneous groups of patients with similar profiles of the variables. Sequential Cox models paralleling the clinical sequence of HF assessment were used to elucidate the benefit of cluster-based classification over guidelines-based classification. The primary endpoint was a hospitalization for worsening HF. Results Cluster analysis identified 3 clusters with distinct properties of diastolic function that shared similarities with guidelines-based classification. The clusters were associated with brain natriuretic peptide level (p &lt; 0.001, figure A). During follow-up period of 2.6 ± 2.0 years, 62 patients (22%) experienced the primary endpoint. Cluster-based classification exhibited a significant prognostic value (c2 = 20.3, p &lt; 0.001, figure B), independent from and incremental to an established clinical risk score for HF (MAGGIC score) and left ventricular end-diastolic volume (hazard ratio = 1.677, p = 0.017, model c2: from 47.5 to 54.1, p = 0.015, figure D). Although guideline-based classification showed a significant prognostic value (c2 = 13.1, p = 0.001, figure C), it did not significantly improve overall prognostication from the baseline (model c2: from 47.5 to 49.9, p = 0.199, figure D). Conclusion Machine learning techniques help grading diastolic function and stratifying the risk for decompensation in HF. Abstract 153 Figure.

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