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Development is a symphony of cells differentiation in which different signaling pathways are orchestrated at specific times and periods to form mature and functional cells from undifferentiated cells. The similarity of the gene expression profile in malignant and undifferentiated cells is an interesting topic that has been proposed for many years and gave rise to the differentiation-therapy concept, which appears a rational insight and should be reconsidered. Hepatocellular carcinoma (HCC), as the sixth common cancer and the third leading cause of cancer death worldwide, is one of the health-threatening complications in communities where hepatotropic viruses are endemic. Sedentary lifestyle and high intake of calories are other risk factors. HCC is a complex condition in which various dimensions must be addressed, including heterogeneity of cells in the tumor mass, high invasiveness, and underlying diseases that limit the treatment options. Under these restrictions, recognizing, and targeting common signaling pathways during liver development and HCC could expedite to a rational therapeutic approach, reprograming malignant cells to well-differentiated ones in a functional state. Accordingly, in this review, we

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Laser-assisted in situ keratomileusis (LASIK) is a unique corneal stromal laser ablation method that uses an excimer laser to reach beneath corneal dome-shaped tissues. In contrast, surface ablation methods, such as photorefractive keratectomy, include removing epithelium and cutting off the Bowman’s layer and the stromal tissue of the anterior corneal surface. Dry eye disease (DED) is the most common complication after LASIK. DED is a typical multi-factor disorder of the tear function and ocular surface that occurs when the eyes fail to produce efficient or adequate volumes of tears to moisturize the eyes. DED influences quality of life and visual perception, as symptoms often interfere with daily activities such as reading, writing, or using video display monitors. Generally, DED brings about discomfort, symptoms of visual disturbance, focal or global tear film instability with possible harm to the ocular surface, the increased osmolarity of the tear film, and subacute inflammation of the ocular surface. Almost all patients develop a degree of dryness in the postoperative period. Detection of preoperative DED and committed examination and treatment in the preoperative period, and continuing treatments postoperatively lead to rapid healing, fewer complications, and improved visual outcomes. To improve patient comfort and surgical outcomes, early treatment is required. Therefore, in this study, we aim to comprehensively review studies on the management and current treatment options for post-LASIK DED.

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