Solid tumors develop abnormally at spatial and temporal scales, giving rise to biophysical barriers that impact anti-tumor chemotherapy. This may increase the expenditure and time for conventional drug pharmacokinetic and pharmacodynamic studies. In order to facilitate drug discovery, we propose a mathematical model that couples three-dimensional tumor growth and angiogenesis to simulate tumor progression for chemotherapy evaluation. This application-oriented model incorporates complex dynamical processes including cell- and vascular-mediated interstitial pressure, mass transport, angiogenesis, cell proliferation, and vessel maturation to model tumor progression through multiple stages including tumor initiation, avascular growth, and transition from avascular to vascular growth. Compared to pure mechanistic models, the proposed empirical methods are not only easy to conduct but can provide realistic predictions and calculations. A series of computational simulations were conducted to demonstrate the advantages of the proposed comprehensive model. The computational simulation results suggest that solid tumor geometry is related to the interstitial pressure, such that tumors with high interstitial pressure are more likely to develop dendritic structures than those with low interstitial pressure.

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Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

Certain gases in the breath are known to be indicators of the presence of diseases and clinical conditions. These gases have been identified as biomarkers using equipments such as gas chromatography (GC) and electronic nose (e-nose). GC is very accurate but is expensive, time consuming, and non-portable. E-nose has the advantages of low-cost and easy operation, but is not particular for analyzing breath odor and hence has a limited application in diseases diagnosis. This article proposes a novel system that is special for breath analysis. We selected chemical sensors that are sensitive to the biomarkers and compositions in human breath, developed the system, and introduced the odor signal preprocessing and classification method. To evaluate the system performance, we captured breath samples from healthy persons and patients known to be afflicted with diabetes, renal disease, and airway inflammation repectively and conducted experiments on medical treatment evaluation and disease identification. The results show that the system is not only able to distinguish between breath samples from subjects suffering from various diseases or conditions (diabetes, renal disease, and airway inflammation) and breath samples from healthy subjects, but in the case of renal failure is also helpful in evaluating the efficacy of hemodialysis (treatment for renal failure).

Hepatocellular carcinoma (HCC) is an aggressive tumor and the third leading cause of cancer-related death worldwide. Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) has been found frequently methylated in various cancers, including HCC. The aim of the present study was to investigate the role of OCIAD2 in HCC progression. We analyzed liver hepatocellular carcinoma patients' data from the Cancer Genome Atlas (TCGA), including data extracted from 371 HCC tissues and 50 adjacent normal liver tissues. The RNA sequencing and DNA methylation data revealed that OCIAD2 were significantly hypermethylated and its expression level in the tumor tissues was much lower than that in the corresponding adjacent normal tissues. The methylation level in the promoter was negatively correlated with the expression level of OCAID2. Treatment of HCC cell lines with the DNA methylation inhibitor 5-aza-2'-deoxycitydine (5-Aza) induced a significant increase in the OCIAD2 mRNA and protein. Knocking-down OCIAD2 led to an increased colony formation, migration and invasion dramatically, accompanying with an enhanced expression of MMP9 and activation of AKT and FAK. Inhibition of AKT signaling restored OCIAD2-mediated changes in HCC cell clonogenic growth, migration and invasion. Survival analysis of HCC patient's data indicated patients with a higher expression ratio of OCIAD2/MMP9 had a shorter overall survival than those with a lower expression ratio of OCIAD2/MMP9. Overall, our data indicate that reduced expression of OCIAD2 by DNA hypermethylation plays an important role in HCC tumor growth and invasion. Hypermethylation of OCIAD2 may contribute to HCC treatment development.