ObjectiveTo review and summarize the role of helper T cell (Th) in the pathogenesis of osteoarthritis (OA) and research progress of Th cell-related treatment for OA.MethodsThe domestic and foreign literature in recent years was reviewed. The role of Th cells [Th1, Th2, Th9, Th17, Th22, and follicular helper T cell (Tfh)] and related cytokines in the pathogenesis of OA and the latest research progress of treatment were summarized.ResultsTh cells play an important role in the pathogenesis of OA. Th1, Th9, and Th17 cells are more important than Th2, Th22, and Tfh cells in the pathogenesis of OA. Cytokines such as tumor necrosis factor α and interleukin 17 can cause damage to articular cartilage significantly.ConclusionAt present, the role of Th cells in the pathogenesis of OA has been played in the spotlight. The specific mechanism has not been clear. Regulating the Th cell-associated cytokines, intracellular and extracellular signals, and cellular metabolism is a potential method for prevention and treatment of OA.
Transcranial direct current stimulation (tDCS) is a non-invasive technique that uses constant low-intensity direct current (1 to 2 mA) to regulate neuronal activity in the cerebral cortex. In recent years, tDCS has received more and more attention as a tool to explore human brain function and treat various neurological diseases. However, there is still a lack of systematic and comprehensive reviews in the tDCS treatment of post-stroke dysfunction. This article reviews the treatment of post-stroke dysfunction with tDCS, integrates relevant basic research and clinical research in recent years, summarizes and discusses the theoretical mechanism and application effect of tDCS in the treatment of post-stroke dysfunction, so as to provide a basis for further research.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
ObjectivesTo systematically review the prevalence and disease burden of knee osteoarthritis (KOA) in China.MethodsPubMed, EMbase, CNKI, WanFang Data and VIP databases were searched to collect cross-sectional studies about the prevalence and disease burden of KOA in China from January 1st 1995 to August 31st 2017. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then meta-analysis was performed by using R statistical software.ResultsA total of thirty-three studies were included. The results of meta-analysis showed the prevalance rate of KOA was 18% (95%CI 14% to 22%), and it was higher in women (19%, 95%CI 16% to 23%) than in men (11%, 95%CI 9% to 13%) (P<0.05). The prevalence rates of KOA in different regions were as follows: 11% (95%CI 8% to 14%) in north, 17% (95%CI 15% to 20%) in north-east, 21% (95%CI 13% to 32%) in east, 21% (95%CI 13% to 33%) in north-west, 22% (95%CI 6% to 57%) in south-west, and 18% (95%CI 13% to 23%) in south-central, respectively.ConclusionsThe prevalence of KOA in China is high, and the disease burden is heavy. Due to the quantity and quality of included studies, more high-quality studies are required to verify the above conclusions in future.
Glioma is the most common malignant brain tumor and classification of low grade glioma (LGG) and high grade glioma (HGG) is an important reference of making decisions on patient treatment options and prognosis. This work is largely done manually by pathologist based on an examination of whole slide image (WSI), which is arduous and heavily dependent on doctors’ experience. In the World Health Organization (WHO) criteria, grade of glioma is closely related to hypercellularity, nuclear atypia and necrosis. Inspired by this, this paper designed and extracted cell density and atypia features to classify LGG and HGG. First, regions of interest (ROI) were located by analyzing cell density and global density features were extracted as well. Second, local density and atypia features were extracted in ROI. Third, balanced support vector machine (SVM) classifier was trained and tested using 10 selected features. The area under the curve (AUC) and accuracy (ACC) of 5-fold cross validation were 0.92 ± 0.01 and 0.82 ± 0.01 respectively. The results demonstrate that the proposed method of locating ROI is effective and the designed features of density and atypia can be used to predict glioma grade accurately, which can provide reliable basis for clinical diagnosis.
Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.