Abstract： Diseases prognosis is often influenced by multiple factors, and some intricate non-linear relationships exist among those factors. Artificial neural network （ANN）, an artificial intelligence model, simulates the work mode of biological neurons and has a b capability to analyze multi-factor non-linear relationships. In recent years, ANN is increasingly applied in clinical medical fields, especially for the prediction of disease prognosis. This article focuses on the basic principles of ANN and its application in disease prognosis research.
Lung cancer is one of the tumors with the highest incidence rate and mortality rate in the world. It is also the malignant tumor with the fastest growing number of patients, which seriously threatens human life. How to improve the accuracy of diagnosis and treatment of lung cancer and the survival prognosis is particularly important. Machine learning is a multi-disciplinary interdisciplinary specialty, covering the knowledge of probability theory, statistics, approximate theory and complex algorithm. It uses computer as a tool and is committed to simulating human learning methods, and divides the existing content into knowledge structures to effectively improve learning efficiency and being able to integrate computer science and statistics into medical problems. Through the introduction of algorithm to absorb the input data, and the application of computer analysis to predict the output value within the acceptable accuracy range, identify the patterns and trends in the data, and finally learn from previous experience, the development of this technology brings a new direction for the diagnosis and treatment of lung cancer. This article will review the performance and application prospects of different types of machine learning algorithms in the clinical diagnosis and survival prognosis analysis of lung cancer.
ObjectiveTo reveal the potential mechanism of cisplatin resistance in non-small cell lung cancer A549 cells by comparing the expression profiles of wild-type A549 cells and cisplatin-resistant A549 cells (A549/DPP) through whole transcriptome sequencing analysis.MethodsThe cisplatin resistant A549 (A549/DDP) cell line was first established. Then, the whole-transcriptome analysis was conducted both on A549 and A549/DDP cells. Next, the differentially expressed RNAs of lncRNA-seq, circRNA-seq, and miRNA-seq data were identified, respectively, followed by functional enrichment analysis. Finally, a comprehensive analysis based on the whole transcriptome data was performed and the construction of the ceRNA network was carried out.ResultsA total of 4 517 lncRNA, 123 circRNA, and 145 miRNA were differentially expressed in A549/DDP cells compared with the A549 cell line. These different RNAs were significantly enriched in cancer-related pathways. The ceRNA network contained 12 miRNAs, 4 circRNAs, 23 lncRNAs, and 9 mRNA nodes, of which hsa-miR-125a-5p and hsa-miR-125b-5p were important miRNAs based on the topological analysis.ConclusionTumor necrosis factor signaling pathway and p53 signaling pathway are involved in A549/DPP resistance. Hsa-miR-125a-5p and hsa-miR-125b-5p may be potential targets for reversing cisplatin resistance.
Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.
Objective To explore the influencing factors of internet game addiction among middle school students. Methods Students from a certain district in Sichuan between September 2022 and March 2023 were included as participants. Basic information such as gender, age, whether the subjects were only children, place of residence, parental education, and subjective economic status were investigated. The nine-item Internet Gaming Disorder Scale-short form was used to investigate whether participants had internet game addiction, and the Berkman-Syme Social Network Index was used to evaluate the participants’ social level. Multiple linear regression analysis was used to conduct multivariate analysis to explore the influencing factors of internet game addiction. Results A total of 594 questionnaires were distributed, and 592 valid questionnaires were ultimately obtained. The detection rate of internet game addiction was 12.0%. Multiple linear regression analysis showed that gender (t=−8.281, P<0.001), age (t=3.211, P=0.001), subjective economic status in the region (t=2.025, P=0.043), and social level (t=−4.239, P<0.001) were the influencing factors of online game addiction. Due to the P value was close to the set test level (0.05), subjective economic status in the region was not considered an influencing factor of internet game addiction. Conclusion Teenagers with male gender, older age, and lower social skills are more likely to develop addiction to internet games.