ObjectivesTo conduct a bibliometric analysis to research the status of disease burden domestically and overseas so as to understand the status of diseases burden, and to provide scientific and reasonable reference for health disease prevention, control strategies formulation and future research.MethodsPubMed, Web of Science, EMbase, The Cochrane Library, WanFang Data, CBM and CNKI databases were electronically searched to collect literature on disease burden from inception to October, 2018. Two reviewers independently screened literature and extracted data. EndNote X7 software was used for literature management, Excel 2016 software and VOS viewer software were also used to analyze data. Literature was classified by the aspects of literature publication characteristics, diseases, background areas, influencing factors, evaluation indicators and poverty caused by illness.ResultsA total of 325 studies were included in the bibliometric analysis. 41 articles (12.6%) were published in journals indexed by SCIE; original research evidence accounted for 97.0% (315 articles); 272 articles were from China (83.7%). The main diseases involved were malignant tumors (58 articles, 17.8%), diabetes (29 articles, 8.9%) and hypertension (24 articles, 7.4%). Factors affecting the disease burden primarily included hospitalization days (9 articles, 2.8%), complications (5 articles, 1.5%), delays in treatment (5 articles, 1.5%), and economic income (4 articles, 1.2%). Sixity-one articles (18.8%) reported poverty due to illness, and related diseases were chronic obstructive pulmonary disease (12 articles, 3.7%), hypertension (10 articles, 3.1%), diabetes (10 articles, 3.1%), malignant tumors (9 articles, 2.8%) and hepatitis B (6 articles, 1.8%).ConclusionsAt present, the disease burden research are focusing more on the burden of chronic non-communicable diseases such as malignant tumors, hypertension, diabetes, cardiovascular and cerebrovascular diseases in developing countries and regions. Medical costs vary from different diseases and treatment, different demographic characteristics of patients, and the coverage medical security of different population are the primary reasons for the " expensive in medical treatment” of current residents and the heavy burden of disease. DALY and total direct medical expenses are the main evaluation indexes of epidemiological burden and economic burden of disease, respectively. Future researches should focus on strengthening the scientific nature of study design to improve the quality of research, as well as paying more attention to diseases and aspects that are rarely involved, such as major diseases caused by poverty due to illness, comprehensive analysis of multiple diseases and aspects of health investment measurement, and comprehensively use the evaluation indicators of disease burden to strengthen the research on the comparability index of disease economic burden.
ObjectiveTo establish a model for predicting microvascular invasion (MVI) of hepatocellular carcinoma based on magnetic resonance imaging (MRI) radiomics features.MethodsThe clinical and pathological datas of 190 patients with hepatocellular carcinoma who received surgical treatment in our hospital from September 2017 to May 2020 were prospectively collected. The patients were randomly divided into training group (n=158) and test group (n=32) with a ratio of 5∶1. Gadoxetate disodium (Gd-EOB-DTPA) -enhanced MR images of arterial phase and hepatobiliary phase were used to select radiomics features through the region of interest (ROI). The ROI included the tumor lesions and the area dilating to 2 cm from the margin of the tumor. Based on a machine learning algorithm logistic, a radiomics model for predicting MVI of hepatocellular carcinoma was established in the training group, and the model was evaluated in the test group.ResultsSeven radiomics features were obtained. The area under the receiver operating characteristic curve (AUC) of the training group and the test group were 0.830 [95%CI (0.669, 0.811)] and 0.734 [95%CI (0.600, 0.936)], respectively.ConclusionThe model based on MRI radiomics features seems to be a promising approach for predicting the microvascular invasion of hepatocellular carcinoma, which is of clinical significance for the management of hepatocellular carcinoma treatment.
ObjectivesTo review the pharmacoeconomic evaluation of rheumatoid arthritis patients with an inadequate efficacy or intolerance with conventional synthetic disease modifying antirheumatic drugs (csDMARDs).MethodsCNKI, WanFang Data, VIP, PubMed, EMbase, Web of Science and The Cochrane Library were electronically searched to collect pharmacoeconomic studies about rheumatoid arthritis patients with an inadequate efficacy or intolerance with csDMARDs from inception to February 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of the included studies, then, descriptive analysis was performed.ResultsA total of 16 studies were included, where most compared the economics of different treatment methods from the perspective of the payer by cohort or individual model. The economic costs in the studies were primarily on direct cost. Sensitivity analyses were used to prove the robustness of the main analysis in each study. Biological disease-modifying antirheumatic drugs (bDMARDs) might be more cost-effective than csDMARDs. In addition, compared with the bDMARDs, new-marketed targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) might be more cost-effective.ConclusionsIt could be considered to implement more new marketed tsDMARDs to improve patients’ condition to reduce the economic burden and optimize the allocation of health care resources.
Objective To explore the effects on quality of life (QOL), the targeted rates of metabolic parameters and cost-effectiveness in newly diagnosed type 2 diabetic patients who underwent multifactorial intensive intervention. Methods One hundred and twenty seven cases in an intensive intervention and 125 cases in a conventional intervention group were investigated by using the SF-36 questionnaire. The comparison of QOL and the targeted rates of metabolic parameters between the two groups were made. We assessed the influence factors of QOL by stepwise regression analysis and evaluated the efficiency by pharmacoeconomic cost-effectiveness analysis. Results The targeted rates of blood glucose, blood lipid and blood pressure with intensive policies were significantly higher than those with conventional policy (P＜0.05). The intensive group’s role limitations due to physical problems (RP), general health (GH), vitality (VT), role limitation due to emotional problems (RE) and total scores after 6 months intervention were significantly higher than those of baseline (P＜0.05). The vitality scores and health transition (HT) of the intensive group were better than those of the conventional group after 6 months intervention. But the QOL scores of the conventional group were not improved after intervention. The difference of QOL’s total scores after intervention was related to that of HbA1c. The total cost-effectiveness rate of blood glucose, blood lipid, blood pressure control and the total cost-effectiveness rate of QOL with intensive policy were higher than those with the conventional policy. Conclusions Quality of life and the targeted rates of blood glucose, blood lipid and blood pressure in newly diagnosed type 2 diabetic patients with multifactorial intensive intervention policy are better and more economic than those with conventional policy.
ObjectiveTo characterize proteomic profile in aqueous humor of patients with pathologic myopia (PM) using quantitative proteomic analysis, which may provide new clues to understand the mechanisms and possible treatments of PM.MethodsA cross-sectional study. From January 2019 to August 2019, aqueous humor samples (32 cataract patients) were collected for quantitative proteomic analysis using liquid chromatography tandem mass spectrometry at Tianjin Medical University Eye Hospital. There were 11 males and 21 females. They were 58-76 years old with an average age of 68.41±6.09 years old. Sixteen patients with PM were regarded as PM group, 16 patients without myopia were regarded as the control group. The aqueous humor samples (100-150 μl ) were collected from all patients before cataract surgery. Using protein quantification and non-labeled liquid chromatography tandem mass spectrometry analysis, differentially expressed proteins were obtained. Five different proteins were randomly selected for ELISA verification. The differentially expressed proteins were further analyzed by gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes, which were validated using ELISA in the other twenty samples of each group.ResultsA total of 583 proteins were identified and 101 proteins were found to be differentially expressed, including 63 up-regulated proteins and 38 down-regulated proteins. ELISA verification results showed that the expression trend of the 5 differentially expressed proteins between the PM group and the control group was consistent with the results of Label-free quantitative proteomics analysis. The main classifications of these differentially expressed proteins were protein-binding activity modulator, defense/immunity protein, protein modifying enzyme, metabolite interconversion enzyme, extracellular matrix protein, transfer/carrier protein and so on. The bioinformatics analysis suggested that PM was closely associated with inflammation and immune interactions, and remodeling of extracellular matrix.ConclusionsCompared with the control group, the protein expression profile of PM patients' aqueous humor specimens has obvious changes. These differences indicate that PM is closely related to inflammation and immune interaction and extracellular matrix remodeling.
ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
The classification of lung tumor with the help of computer-aided diagnosis system is very important for the early diagnosis and treatment of malignant lung tumors. At present, the main research direction of lung tumor classification is the model fusion technology based on deep learning, which classifies the multiple fusion data of lung tumor with the help of radiomics. This paper summarizes the commonly used research algorithms for lung tumor classification, introduces concepts and technologies of machine learning, radiomics, deep learning and multiple data fusion, points out the existing problems and difficulties in the field of lung tumor classification, and looks forward to the development prospect and future research direction of lung tumor classification.
Diabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus causing severe visual impairment, and it is the main cause of blindness in adults. Metabolic abnormalities play an important role in the occurrence and development of DR, including the abnormal levels of glucose metabolism, lipid metabolism, amino acid metabolism and purine metabolism, which indicate that there are disorders of phosphopentose pathway, arginine metabolism pathway, polyol pathway and ascorbic acid pathway in the progression of DR. Metabolomics has great advantages in exploring the pathogenesis and diagnosis of DR, helping to identify the characteristic metabolic changes of DR And discover potential biomarkers. However, the existing metabolomics studies on DR have some limitations, such as the potential biomarkers found in some studies are difficult to verify in other studies due to differences in race, age, gender and sample size. There are few studies on biomarkers at different stages of DR. Therefore, in the future, multi-center and large-scale clinical studies are needed to screen out biomarkers with practical clinical diagnostic value.
ObjectiveTo detect the protein expression change in the proliferation of human retinal microvascular endothelial cells (hRMECs) stimulated with 4-Hydroxynonenal (4-HNE).MethodshRMECs were in a logarithmic growth phase, and then were separated into 4-HNE-stimulated group and negative control group. The concentration of 4-HNE included 5, 10, 20 and 50 μmol/L in 4-HNE-stimulated group, while the negative control group was added in the same volume of ethanol (the solvent of 4-HNE). Then the cells were stimulated with 4-HNE for 24 hours following by CCK-8 kits incubating for 4 hours to detect absorbance. It was found that 10 μmol/L 4-HNE had the most obvious effect on the proliferation of hRMECs. Therefore, the cellular proteins from 10 μmol/L 4-HNE-stimulated group and negative control group were acquired and prepared by FASP sample preparation method. Data independent acquisition was used for data acquisition, and the GO analysis and pathway enrichment were performed for analysis of differentially expressed proteins.ResultsCCK-8 kits detection results showed that the A value of the 10 and 20 μmol/L 4-HNE-stimulated groups were significantly higher than negative control group and 5 μmol/L 4-HNE-stimulated group (F=25.42, P＜0.01), while there were no differences between 10 and 20 μmol/L 4-HNE-stimulated groups, and the A value of 50 μmol/L 4-HNE-stimulated groups was lower than negative control. A total of 2710 quantifiable proteins were identified by peoteomics, and 118 proteins were differentially expressed (fold change＞1.5, P＜0.05). Seventy-two proteins were up-regulated after 4-HNE stimulation, whereas 46 proteins were down-regulated. Particularly, the expressions of Heme oxygenase-1, Sulfoxdoxin-1, Heat shock protein A1B, Thioredoxin reductase-1, Glutathione reductase, ATPase and prothrombin were increased when cells were added in 4-HNE, whereas the expressions of apolipoprotein A1 and programmed cell death protein 4 were decreased. These differentially expressed proteins were mainly involved in the biological processes such as oxidative stress, cell detoxification, and ATPase-coupled membrane transport.ConclusionAfter stimulated with 4-HNE, the oxidative stress, cell detoxification, and ATPase-coupled membrane transport protein expression may change in hRMECs in order to regulate oxidative stress and growth situation.
Biliary tract cancer is characterized by occult onset, highly malignancy and poor prognosis. Traditional medical imaging is an important tool for surgical strategies and prognostic assessment, but it can no longer meet the urgent need for accurate and individualized treatment in patients with biliary tract cancer. With the advent of the digital imaging era, the advancement of artificial intelligence technology has given a new vitality to digital imaging, and provided more possibilities for the development of medical imaging in clinical applications. The application of radiomics in the diagnosis and differential diagnosis of benign and malignant tumors of biliary tract, assessment of lymph node status, early recurrence and prognosis assessment provides new means for the diagnosis and treatment of patients with biliary tract cancer.