Methodology: Serum samples from six BAVM patients and three control subjects were analyzed using enzyme-linked immunoabsorbent assay (ELISA) for OPN. A total of 10 BAVM patients and five control subjects were analyzed using Multiplex ELISA for MMPs. A total of 16 BAVM tissue samples and two normal brain tissue samples were analyzed using immunohistochemistry.
Result: MMP-2 and -9 were significantly higher in the serum of BAVM patients before and after treatment than in control patients. There were no significant differences of OPN and MMP-9 serum level in BAVM patients before and after treatment. MMP-2 showed a significant elevation after the treatment. Expression of OPN, MMP-2 and -9 proteins were seen in endothelial cells, perivascular cells and brain parenchyma of BAVM tissues.
Conclusion: Findings revealed that the level of MMP-2 and -9 in the serum correlated well with the expression in BAVM tissues in several cases. Knockdown studies will be required to determine the relationships and mechanisms of action of these markers in the near future. In addition, studies will be required to investigate the expression of these markers' potential applications as primary medical therapy targets for BAVM patients.
METHODS: For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall.
RESULTS: From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier.
CONCLUSION: Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques.
MATERIALS AND METHODS: Following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines, online searches of multiple databases were performed to retrieve articles from their inception until December 2017. Inclusion criteria included all English-based original articles of immunohistochemistry (IHC) studies investigating CAIX expression in human RCC tissue. Four articles were finally selected for meta-analysis with a total of 1964 patients. Standard meta-analysis methods were applied to evaluate the role of CAIX in RCC prognosis. The relative risk (RR) and its 95% confidence interval (CI) were recorded for the association between biomarker and prognosis, and data were analysed using MedCalc statistical software.
RESULTS: The meta-analysis showed that high CAIX expression was associated with low tumour stage (RR 0.90%, 95% CI 0.849-0.969, p= 0.004), low tumour grade (RR 0.835%, 95% CI 0.732-0.983, p= 0.028), absence of nodal involvement (RR 0.814%, 95% CI 0.712-0.931, p= 0.003) and better ECOS-PS index (RR 0.888%, 95% CI 0.818-0.969, p= 0.007). The high tissue CAIX expression in RCC is hence an indication of an early malignancy with a potential to predict favourable disease progression and outcome.
CONCLUSION: The measurement of this marker may be beneficial to determine the course of the illness. It is hoped that CAIX can be developed as a specific tissue biomarker for RCC in the near future.
METHOD: The study is a randomized, double-blind, placebo-controlled trial. In total, 40 patients were recruited. Patients were randomized to receive either microbial cell preparation (n = 20) or placebo (n = 20) for 7 days prior to elective surgery. The primary end point was the time to return of normal gut function, while the secondary end point was the duration of hospital stay.
RESULTS: The treatment group demonstrated significantly faster return of normal gut function with a median of 108.5 h (80-250 h) which was 48 h earlier than the placebo group at a median of 156.5 h (94-220 h), p = 0.022. The duration of hospital stay in the treatment group was also shorter at a median of 6.5 days (4-30 days), in comparison to the placebo group at 13 days (5-25 days), p = 0.012.
CONCLUSION: Pre-surgical administration of microbial cell preparation promotes the return of normal gut function in patients after colorectal cancer surgery, thus associated with faster recovery and shorter duration of hospital stay.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
METHODS: A double-blind randomized study was carried out with 140 colorectal cancer patients on chemotherapy. Subjects were separated into two groups to receive either placebo or MCP [30 billion colony-forming unit (CFUs) per sachet] at a dose of two sachets daily for 4 weeks, and omega-3 fatty acid at a dose of 2 g daily for 8 weeks. Outcomes measured were quality of life, side effects of chemotherapy and levels of inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and C-reactive protein.
RESULTS: The supplementation with MCP and omega-3 fatty acid improved the overall quality of life and alleviated certain side effects of chemotherapy. The supplementation with MCP and omega-3 fatty acid also managed to reduce the level of IL-6 (P = 0.002). There was a significant rise in the placebo group's serum TNF-α (P = 0.048) and IL-6 (P = 0.004).
CONCLUSION: The combined supplementation with MCP and omega-3 fatty acid may improve quality of life, reduce certain inflammatory biomarkers and relieve certain side effects of chemotherapy in colorectal patients on chemotherapy.