Qianjiang Evening News·Hours News Correspondent Xu Kaiyang
South America's butterflies with flapping their wings may bring an hurricane to the US Texas . A small change can affect the development of things. This is the butterfly effect that everyone is familiar with.
In medicine, the occurrence of complications is similar to this nonlinear physical effect. As the name suggests, complications are unpredictable diseases induced by disease, treatment or surgery, with strong uncertainty and variability.
"Due to the influence of the patient's physical fitness, living habits, living environment and other aspects, it is still difficult to accurately analyze and judge complications, and they mainly rely on the doctor's clinical experience, which has also posed a 'difficult problem' for many new doctors who lack clinical experience." Ningbo Second Hospital Professor Zhang Jingfeng said that how to predict disease complications more accurately and prepare targeted treatment plans has always been an urgent need in current clinical practice.
However, this problem is now expected to be solved by establishing big data model calculations.
Recently, Dr. Jiang Xiongfei, Ningbo University of Finance and Economics, and Professor Zheng Jianjun's team of Ningbo Second Hospital and Professor Zheng Bo's team of Zhejiang University, and Professor Zheng Bo's team of Zhejiang University, collaborated in the nonlinear physics top journal "Chaos, Solitons and Fractals" (influence factor 9.922, Chinese Academy of Sciences District 1), an article "Structure and dynamics of human complication-disease network" (structure and dynamics of human complication-disease network) was introduced in detail, using complex systems and clinical data to study the nonlinear dynamics behavior of biological networks, and quantitatively model disease complications, which proposes a new solution for future clinical disease prevention, diagnosis and treatment.
"According to an accidental academic exchange activity, I had the idea of using complexity theory to study complications." Jiang Xiongfei introduced that in early model verification, they found that there were many complications and sequelae caused by pneumonia. When they saw that common chronic diseases such as cardiovascular diseases were also complications, the research team was very confused and even doubted the accuracy of the model, and the research was once stagnant.
"This seems to be irrelevant, so we are not sure what is wrong." Jiang Xiongfei said, "It was not until a foreign scholar published a paper in JAMA in 2015 that demonstrated the conclusion that 'the clinical impact of pneumonia leads to an increase in the risk of cardiovascular disease', which coincides with our research conclusions, which proves that our model and conclusion are correct!"
Network diagram of human disease complications
has been "verified" by authoritative journals, and Jiang Xiongfei's team is more motivated.
"We collected 6715 diseases from the clinical medical knowledge database, and the complications of the disease are extracted from the disease description in the database. A node represents a disease, and there will be directed links between the nodes." Jiang Xiongfei introduced that based on this, they drew a directed graph called the Human Disease Complication Network (HDCN) to represent the causal relationship between upstream diseases and their downstream complications. "To put it concisely, through the analysis of a large number of clinical samples, we now set up a link relationship to the complications corresponding to each disease. As long as the sample data continues to expand, we can find out the rules and order of complications under different conditions, and match them to different patients in a targeted manner." The research team of
has calculated the complications of respiratory diseases such as pneumonia and chronic heart disease many times. They found that diseases such as upper gastrointestinal bleeding, and multi-organ failure, which seem to have no association with the respiratory tract are actually complications of theirs.
"The causes and relationships of these symptoms are extremely complex, so predicting complications is crucial for patients and doctors in clinical practice." Professor Zheng Jianjun, one of the corresponding authors of in the paper, and Professor Zheng Jianjun of Ningbo Second Hospital, introduced that these complications are far from the respiratory tract complications in public cognition. If the warning can be effectively predicted and warned, it will be of great help to clinical treatment.
is composed of complication system composed of viral myocarditis and acute pancreatitis , as well as boolean dynamic behavior, which shows a significant nonlinear effect.
It is understood that the current study will be carried out in multiple stages. The researchers have drawn a human disease complication network and studied its topological characteristics, labeled the disease module, and further simulated the dynamic behavior of the disease network with Boolean dynamics. It can reveal in detail that the motif has potential functions in the disease system, such as it can control the response to external stimuli and have an impact on the disease process, which may amplify the initial perturbation through a cascade coupling process and have serious consequences.
"The update rules of Boolean dynamics in the research are synchronous and homogeneous. However, in clinical practice, the progress of complications is obviously out of sync." Zheng Jianjun introduced that the next step will be to further expand the sample size and continuously improve the accuracy of model decisions. "We will conduct a comprehensive study of the large network dynamics of heterogeneous nodes and adopt asynchronous update rules. We also hope to develop a quantitative framework to trace the current disease to the possible original disease, and to bring the prediction accuracy to a new level."
"We also look forward to the future application of this technology to develop new disease prevention, diagnosis and treatment strategies." Jiang Xiongfei said.
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