Liver cancer risk quantification through an artificial neural network based on personal health data.

BACKGROUND: Liver cancer is one of the most common types of cancer and the third leading cause of cancer-related deaths globally. The most common type of primary liver cancer is called hepatocellular carcinoma (HCC) which accounts for 75-85% of cases. HCC is a malignant disease with aggressive progression and limited therapeutic options. While the exact cause of liver cancer is not known, habits/lifestyles may increase the risk of developing the disease. MATERIAL AND METHODS: This study is designed to quantify the liver cancer risk through a multi-parameterized artificial neural network (ANN) based on basic health data including habits/lifestyles. In addition to input and output layers, our ANN model has three hidden layers having 12, 13, and 14 neurons, respectively. We have used the health data from the National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) datasets to train and test our ANN model. RESULTS: We have found the best performance of the ANN model with an area under the receiver operating characteristic curve of 0.80 and 0.81 for training and testing cohorts, respectively. CONCLUSION: Our results demonstrate a method that can predict liver cancer risk with basic health data and habits/lifestyles. This novel method could be beneficial to high-risk populations by enabling early detection.

Rheotaxis-based microfluidic device for selecting sperm from samples infected with a virus.

OBJECTIVE: To investigate whether the presented rheotaxis-based microfluidic device could be used to separate spermatozoa from viruses (i.e., Zika) in the infected semen sample during the selection and washing process. DESIGN: Quantitative and experimental study of the sperm washing/selection process through the microfluidic platform exploiting the positive rheotaxis of sperm. SETTING: None. PATIENT(S): None. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Human sperm were purchased from a sperm bank. The raw semen sample was mixed with viruses and loaded into a microfluidic device. Experiments were performed with 2 different flow rates (0 and 25 µL/minute) to investigate the washing efficiency of the device in the sperm selection process. The sperm sample was collected after 45 minutes and analyzed to check whether the collected sample is free of any infections (viruses) after isolation. RESULT(S): Fluorescent microscopy and quantitative polymerase chain reaction-based analysis showed that the sperm selected with the presented rheotaxis-based microfluidic device at the optimal flow rate (25 µL/minute) was free of any viruses. CONCLUSION(S): We have developed a simple, cost-effective microfluidic device that mimics the conditions of the female genital tract while washing out the raw semen efficiently during the selection process for assisted reproductive technology.