scholarly journals Detection for disease tipping points by landscape dynamic network biomarkers

2018 ◽  
Vol 6 (4) ◽  
pp. 775-785 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Siyang Leng ◽  
Hui Tang ◽  
Kazuyuki Aihara ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Good Prognosis ◽  
Single Sample ◽  
Physiological Data ◽  
Tipping Points ◽  
Virus Infections ◽  
Model Free ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
The Individual

ABSTRACT A new model-free method has been developed and termed the landscape dynamic network biomarker (l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers (i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.

scholarly journals Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

2017 ◽  
Vol 13 (7) ◽  
pp. e1005633 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Rui Liu ◽  
Xiangtian Yu ◽  
Luonan Chen ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Complex Diseases ◽  
Single Sample ◽  
Critical States ◽  
Network Biomarkers

Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

2020 ◽  
Vol 85 ◽  
pp. 107202 ◽  
Author(s):  
Yichen Sun ◽  
Hongqian Zhao ◽  
Min Wu ◽  
Junhua Xu ◽  
Shanshan Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dynamic Network ◽  
Critical States ◽  
Network Biomarkers ◽  
Landscape Dynamic

scholarly journals Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 676
Author(s):  
Jing Ge ◽  
Chenxi Song ◽  
Chengming Zhang ◽  
Xiaoping Liu ◽  
Jingzhou Chen ◽  
...  
Keyword(s):  
Early Warning ◽  
Coronary Atherosclerosis ◽  
Dynamic Network ◽  
Tipping Point ◽  
Warning Signals ◽  
Metabolomics Data ◽  
Early Warning Signals ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
Major Factors

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.

scholarly journals Emerging, crystalizing, and changing psychological contracts over time: Introducing the iPC-network model

2019 ◽  
Author(s):  
Tim Vantilborgh
Keyword(s):  
Network Model ◽  
Psychological Contract ◽  
Dynamic Network ◽  
Network Models ◽  
Psychological Contracts ◽  
Exchange Relationships ◽  
Contract Breach ◽  
Alternative Approach ◽  
The Individual ◽  
Over Time

This chapter introduces the individual Psychological Contract (iPC) network model as an alternative approach to study psychological contracts. This model departs from the basic idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network, in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, I propose that these networks evolve over time towards attractor states, both at the level of the network structure and at the level of the nodes (i.e., breach and fulfilment attractor states). I highlight how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explain how it may advance scholars understanding of exchange relationships. In particular, I illustrate how iPC-network models allow researchers to study the actual exchanges in the psychological contract over time, while acknowledging its idiosyncratic nature. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and explains how content and process of the psychological contract continuously influence each other.

scholarly journals Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Michael Platzer ◽  
Thomas Reutterer
Keyword(s):  
Mixed Type ◽  
Synthetic Data ◽  
Training Data ◽  
Privacy Risk ◽  
Individual Level ◽  
Empirical Assessment ◽  
Model Free ◽  
Private Data ◽  
Synthetic Datasets ◽  
The Individual

AI-based data synthesis has seen rapid progress over the last several years and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing. This is reflected by the growing availability of both commercial and open-sourced software solutions for synthesizing private data. However, despite these recent advances, adequately evaluating the quality of generated synthetic datasets is still an open challenge. We aim to close this gap and introduce a novel holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions for mixed-type tabular data. Measuring fidelity is based on statistical distances of lower-dimensional marginal distributions, which provide a model-free and easy-to-communicate empirical metric for the representativeness of a synthetic dataset. Privacy risk is assessed by calculating the individual-level distances to closest record with respect to the training data. By showing that the synthetic samples are just as close to the training as to the holdout data, we yield strong evidence that the synthesizer indeed learned to generalize patterns and is independent of individual training records. We empirically demonstrate the presented framework for seven distinct synthetic data solutions across four mixed-type datasets and compare these then to traditional data perturbation techniques. Both a Python-based implementation of the proposed metrics and the demonstration study setup is made available open-source. The results highlight the need to systematically assess the fidelity just as well as the privacy of these emerging class of synthetic data generators.

Lead toxicosis in cats—a review

2003 ◽  
Vol 5 (5) ◽  
pp. 249-255 ◽  
Author(s):  
TE Knight ◽  
MSA Kumar
Keyword(s):  
Diagnostic Tests ◽  
Aminolevulinic Acid ◽  
Clinical Signs ◽  
Body Burden ◽  
Blood Lead ◽  
Good Prognosis ◽  
Metabolic Effects ◽  
Neuronal Necrosis ◽  
The Individual ◽  
High Index Of Suspicion

Although the incidence of lead toxicosis in small animals continues to decrease, it remains a significant malady. We have reviewed the literature of the past 45 years, which revealed 70 cases involving cats. Sources, signs, diagnosis, pathology and treatment of feline lead toxicosis are reviewed. In 84% of these cases the source of lead was old paint usually from home renovation. The most common signs in cats are anorexia, vomiting, and seizures. The younger individuals seem more likely to show CNS signs. Since signs are often vague, lead toxicosis may be significantly under diagnosed in cats. The gold standard of diagnostic tests is blood lead concentration, although it does not necessarily correlate with total body burden of lead or with metabolic effects including clinical signs. Diagnostic tests including erythropoietic protoporphyrin (EPP), urine aminolevulinic acid, and others are discussed. Gross findings on necropsy are few and include a yellow-brown discoloration of the liver often with a nutmeg-like appearance. Histological examination may reveal pathognomonic inclusion bodies in liver and renal tissues. Characteristic histological changes in the CNS include neuronal necrosis and demyelination. Treatment of lead toxicosis in cats, as in any species, involves removing the exposure, decontaminating the individual and the environment, supportive care and chelation therapy. The most recently available chelator is succimer (meso 2,3-dimercaptosuccinic acid). Succimer given orally is well tolerated and has a wide margin of safety. A high index of suspicion of lead toxicosis is warranted in cats since they often present with vague and non-specific signs. With any consistent history owners need to be asked about home renovation. Early diagnosis and treatment affords a good prognosis.

scholarly journals Identifying Dysregulated lncRNA-Associated ceRNA Network Biomarkers in CML Based on Dynamical Network Biomarkers

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Junhua Xu ◽  
Min Wu ◽  
Yichen Sun ◽  
Hongqian Zhao ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Myeloid Leukemia ◽  
Clinical Medicine ◽  
Critical Index ◽  
Dynamic Network ◽  
Disease Outbreaks ◽  
Chronic Phase ◽  
Cerna Network ◽  
Network Biomarkers ◽  
Timely Treatment ◽  
Competitive Endogenous Rna

The incidence of chronic myeloid leukemia (CML) is increasing year by year, which is a serious threat to human health. Early diagnosis can reduce mortality and improve prognosis. LncRNAs have been shown to be effective biomarkers for a variety of diseases and can act as competitive endogenous RNA (ceRNA). In this study, the dysregulated lncRNA-associated ceRNA networks (DLCN) of the chronic phase (CP), accelerated phase (AP), and blastic crisis (BC) for CML are constructed. Then, based on dynamic network biomarkers (DNB), some dysregulated lncRNA-associated ceRNA network biomarkers of CP, AP, and BC for CML are identified according to DNB criteria. Thus, a lncRNA (SNHG5) is identified from DLCN_CP, a lncRNA (DLEU2) is identified from DLCN_AP, and two lncRNAs (SNHG3, SNHG5) are identified from DLCN_BC. In addition, the critical index (CI) used to detect disease outbreaks shows that CML occurs in CP, which is consistent with clinical medicine. By analyzing the functions of the identified ceRNA network biomarkers, it has been found that they are effective lncRNA biomarkers directly or indirectly related to CML. The result of this study will help deepen the understanding of CML pathology from the perspective of ceRNA and help discover the effective biomarkers of CP, AP, and BC for CML in the future, so as to help patients get timely treatment and reduce the mortality of CML.

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