Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches.

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.

Association of neutrophil to lymphocyte ratio (NLR) with angiographic SYNTAX score in patients with non-ST-Segment elevation acute coronary syndrome (NSTE-ACS).

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.

Abu Al-Qasim Al-Zahrawi (936-1013 CE), Icon of Medieval Surgery.

Abu al-Qasim Al-Zahrawi (936-1013 common era [CE]), also known in the West as Albucasis, was a great Arab physician and surgeon of the late 10th and early 11th centuries CE. He is best known for his surgical knowledge and expertise. His greatest contribution to medicine is the Kitab al-Tasrif, which includes thirty treatises on medical sciences. His early and great contributions to the field of surgery were seminal. For his endeavors in this field, a number of surgeons and scholars have dubbed him the "Father of Operative Surgery".