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.
Objective The article introduces the present status of the application of comparative proteomics in study of tumor marker. Methods This essay review the present status and advances of the application of comparative proteomics in study of tumor marker through refer considerable literatures about proteome, proteomics and tumor marker. Results Follow the study of human genome deepening; the paradox between the finiteness of genes’ number and stability of genes’ structure and the variety of the life phenomena is more conspicuous. Then, the study of proteomics was pushed to the advancing front of life science research. The application of comparative proteomics to tumor research becomes a hot spot nowadays. Conclusion Screening tumor marker via comparative proteomics is an extremely promising research.
With the development of life sciences and informatics, bioinformatics is developing as an interdisciplinary subject. Its main application is the relationship between genes and proteins and their expression. With the help of genomics, proteomics, transcriptomics, and metabolomics, researchers introduce bioinformatics research methods into fundus disease research. A series of gratifying research results have been achieved including the screening of genetic susceptibility genes, the screening of diagnostic markers, and the exploration of pathogenesis. Genomics has the characteristics of high efficiency and accuracy. It has been used to detect new mutation sites in retinoblastoma and retinal pigment degeneration research, which helps to further improve the pathogenesis of retinal genetic diseases. Transcriptomics, proteomics, and metabolomics have high throughput characteristics. They are used to analyze changes in the expression profiles of RNA, proteins, and metabolites in intraocular fluid or isolated cells in disease states, which help to screen biomarkers and further elucidate the pathogenesis. With the advancement of technology, bioinformatics will provide new ideas for the study of ocular fundus diseases.
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.
ObjectiveTo construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). MethodsA retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. ResultsAmong the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. ConclusionsThe multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.
Based on the principles and methods of systematic review of randomized controlled clinical trials, systematic review of economic analyses can integrate information from multiple economic studies which focus on the same clinical questions. It can also provide important insights by systematically examining how differences among studies lead to different results. Generally, there are seven steps to conduct such a review: 1) formulating questions; 2) establishing eligibility criteria; 3) searching and selecting eligible economic analyses; 4) assessing the validity of economic analyses; 5) acquiring data; 6) analyzing and synthesizing data; and 7) presenting results. Owing to the specificity of economic analyses, many methodological challenges exist, including the varieties of economic models, analytic perspectives, time horizons, and uncertainty and sensitivity analysis among different economic analyses. This may cause difficulties for critical assessment of the economic analyses.
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.
ObjectiveTo systematically review the economic evaluation research of anti-novel coronavirus infection drugs at home and abroad, so as to promote clinical rational drug use. MethodsThe PubMed, Cochrane Library, EMbase, Web of Science, INAHTA, SinoMed, WanFang Data, and CNKI databases were systematically searched from January 1, 2020 to March 25, 2023, to collect economic evaluation studies related to anti-novel coronavirus infection drugs. ResultsA total of 22 articles were included, among which 11 studies were conducted from the perspective of health system, and most of the studies performed cost estimation on direct medical costs. The overall compliance rate of the included studies ranged from 42% to 70%, with deficiencies in model setting, incomplete uncertainty analysis, and lack of stakeholder participation. The results showed that immunotherapy drugs (Dexamethasone, Tocilizumab), neutralizing antibody (REGEN-COV antibody), small molecule drugs (Baricitinib, Nirmatrelvir/Ritonavir, Molnupiravir, Favipiravir) and statin were cost-effective. There was some variation in the results of the economic evaluation of Remdesivir. ConclusionAt present, there are few studies on the economic evaluation of drug interventions in COVID-19. Existing studies have pointed out that most drug interventions are cost-effective. It is suggested that more standardized pharmacoeconomic evaluation studies based on the actual situation of China epidemic should be carried out in the future.
ObjectiveTo analyze the differences in proteins between aneurysm/dissection patients and healthy subjects, and subsequently figure out differential proteins related to medial degeneration of aortic aneurysm/dissection.MethodsAortic wall samples were collected from 6 male aortic aneurysm patients (an aortic aneurysm group, mean age 56.50±8.19 years), 6 male aortic dissection patients (an aortic dissection group, mean age 54.17±6.68 years) and 6 male healthy subjects (a normal group, mean age 40.50±9.31 years) between December 2019 and May 2020 in West China Hospital of Sichuan University. Quantitative proteomics was performed using tandem mass tag (TMT) techniques, followed by gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.ResultsA total of 63 differential proteins were obtained both in the aortic aneurysm group and the aortic dissection group compared with the normal group, with 30 up-regulating and 33 down-regulating. The differential proteins were involved in multiple biological processes and clusted on peroxisome proliferators-activated receptor (PPAR) signaling pathway, extracellular matrix-receptor interaction signaling pathway and complement and coagulation cascades signaling pathway.ConclusionThe identified proteins may help to demonstrate new molecular mechanisms related to medial degeneration of aortic aneurysm/dissection.
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.