Introduction: Considering the role of inflammation in pathogenesis of atherosclerosis, we aimed to investigate the association of presentation neutrophil to lymphocyte ratio (NLR) with complexity of coronary artery lesions determined by SYNTAX score in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS). Methods: From March 2018 to March 2019, we recruited 202 consecutive patients, who were hospitalized for NSTE-ACS and had undergone percutaneous coronary intervention in our hospital. The association of presentation NLR with SYNTAX score was determined in univariate and multivariate linear regression analysis. Results: Higher NLR was significantly associated with higher SYNTAX score (beta= 0.162, P=0.021). In addition, older age, having hypertension, higher TIMI score, and lower ejection fraction on echocardiographic examination were significantly associated with higher SYNTAX score. TIMI score had the largest beta coefficient among the studied variables (TIMI score beta=0.302, P<0.001). In two separate multivariate linear regression models, we assessed the unique contribution of NLR in predicting SYNTAX score in patients with NSTE-ACS. In the first model, NLR was significantly contributed to predicting SYNTAX score after adjustment for age, sex, and hypertension as covariates available on patient presentation (beta=0.142, P=0.040). In the second model, NLR was not an independent predictor of SYNTAX score after adjustment for TIMI score (beta=0.121, P=0.076). Conclusion: In NSTE-ACS, presentation NLR is associated with SYNTAX score. However, NLR does not contribute significantly to the prediction of SYNTAX score after adjustment for TIMI score. TIMI risk score might be a better predictor of the SYNTAX score in comparison to NLR.

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Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC.

Objective: Emergency departments and hospital emergency departments are important due to their critical role in providing urgent medical care to patients in dire need of medical interventions. Checking bottlenecks in new conditions and planning to reduce bed occupancy and hospitalization is needed. The purpose of this study is to investigate the relationship between the patient’s chief complaint and their departure to the emergency room. Methods: From non-traumatic patients referred to the emergency department of Imam Reza Hospital during 2018, about 57000 patients were selected and enrolled in the study. Then, age, sex, initial diagnosis, time of the final decision, and time of departure from the emergency department as well as hospitalization ward were included in the checklist. Patients whose documentation was incomplete were excluded. Data were entered into SPSS software version 15.0 and descriptive statistics (normal distribution, average of time, minimum time and maximum time, confidence interval, mode, and median, etc) were used for descriptive analysis and linear regression was used to analyze the correlation among findings. Results: There was a significant relationship between chief complaint and the length of stay in the emergency department (P = 0.046) and patients with dyspnea due to heart disease, bloody vomit, bloody stool, constipation, jaundice, anemia, decreased level of consciousness, diabetes, complications of diabetes, shortness of breath and kidney injury stayed longer in the emergency room compared to other complaints. Conclusion: The patient’s manner of expressing and chief complaint has an impact on the length of time they wait to leave the emergency room. Also, most patients with problems related to internal medicine have the longest time in the emergency room; in particular gastrointestinal patients have the longest stay in the emergency room.