Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.
Background: Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI. Objectives: This study tries to predict postoperative AKI using interpretable machine learning models. Methods: For this study, the information of 1435 patients was collected from multiple centers. The gathered data are in six categories: demographic characteristics and type of surgery, past medical history (PMH), drug history (DH), laboratory information, anesthesia and surgery information, and postoperative variables. Machine learning methods, including support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), random forest (RF), logistic regression, XGBoost, and AdaBoost, were used to predict postoperative AKI. Local interpretable model-agnostic explanations (LIME) and the Shapley methods were then leveraged to check the interpretability of models. Results: Comparing the area under the curves (AUCs) obtained for different machine learning models show that the RF and XGBoost methods with values of 0.81 and 0.80 best predict postoperative AKI. The interpretations obtained for the machine learning models show that creatinine (Cr), cardiopulmonary bypass time (CPB time), blood sugar (BS), and albumin (Alb) have the most significant impact on predictions. Conclusions: The treatment team can be informed about the possibility of postoperative AKI before cardiac surgery using machine learning models such as RF and XGBoost and adjust the treatment procedure accordingly. Interpretability of predictions for each patient ensures the validity of obtained predictions.
Background: Bipolar disorder is a biological brain disorder which is associated with debilitating fluctuation in mood and adverse effects on patients, their families and society. The importance of genetics and its role in bipolar disorder is a controversial issue to discuss. Evidence indicates a relation between the risk of bipolar disorder and specific genes. Amongst the genes whose role has been established in bipolar disorder, the most notable gene is BDNF (Brain-derived neurotrophic factor). Methods: The study is based on a case-control methodology. During 18 months, the blood samples of patients diagnosed with bipolar mood disorder who were admitted to Farshchian hospital of Hamadan from March 2011 to September 2012 and for the control group, the blood samples of patients admitted to other parts of Farshchian hospital except psychiatric ward were taken and DNA extraction from white blood cells was performed. In general, 84 patients and 85 controls were examined in this study and an expert in vials containing EDTA anticoagulant collected 4ml of blood samples. These samples were sent to the molecular biology lab of Hamadan University of Medical Science to determine their genetic polymorphisms. Genomic DNA was extracted from peripheral blood cells using the real extraction DNA kit (DNP Tm kit, Cat# DN8115C, CinnaGen co., Iran). The allele specific polymerase chain reaction technique was used to determine the frequencies of listed genotype. Considering the different variations for each gene, primers design was carried out using the Allele ID software (Allele ID 6, premier Bio soft Int, USA). For this purpose, 401 nucleotide sequences of targeted gene polymorphisms was chosen as the control sequence and desired primers for this sequence was designed and ordered (Takapouzist Co., Iran). Finally, using the mentioned method the sequences were amplified and examined on 2% agarose gel during electrophoresis. The young mania rating scale (YMRS) was used to evaluate manic symptoms. A written consent was obtained from each individual patient during the study. In addition, all patients in this study were anonymous and ethical considerations were taken into account. Statistical data analysis was performed using SPSS Software and Chi-square test was used to analyze their significance. Results:The results of this study, which was conducted on 84 patients in the case group and 85 patients in the control group indicated that the frequencies of evaluated alleles in the case and control groups for AA genotype were 4 and 4, for GA genotype were 23 and 28, and for GG genotype were 53 and 53, respectively. Conclusions: According to the obtained data, there is no significant relationship between genetic and bipolar disorder. Some studies in this field have also confirmed this issue.
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