Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches.

Prostate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to investigate how obesity influences the noncutaneous malignancies of PCa. By comparing the core signaling pathways of lean and obese patients with PCa, we are able to investigate the relationships between obesity and pathogenic mechanisms and identify significant biomarkers as drug targets for drug discovery. Regarding drug design specifications, we take drug-target interaction, drug regulation ability, and drug toxicity into account. One deep neural network (DNN)-based drug-target interaction (DTI) model is trained in advance for predicting drug candidates based on the identified biomarkers. In terms of the application of the DNN-based DTI model and the consideration of drug design specifications, we suggest two potential multiple-molecule drugs to prevent PCa (covering lean and obese PCa) and obesity-specific PCa, respectively. The proposed multiple-molecule drugs (apigenin, digoxin, and orlistat) not only help to prevent PCa, suppressing malignant metastasis, but also result in lower production of fatty acids and cholesterol, especially for obesity-specific PCa.

Using cognitive modelling to investigate the psychological processes of the Go/NoGo discrimination task in male abstinent heroin misusers.

AIMS: To use cognitive modelling to investigate psychological processes underlying decision-making in male abstinent heroin misusers (AHMs). DESIGN: A case-control study design. SETTING: A drug misuse treatment centre in Taiwan. PARTICIPANTS: Eighty-eight male AHMs and 48 male controls. MEASUREMENTS: Four parameters representing the attention to wins, learning rate, response sensitivity and incentive of heroin-related stimuli from the modified Go/NoGo discrimination task. FINDINGS: A modified cue-dependent learning (CD) model with four parameters representing attention to wins, learning rate, response sensitivity and incentive of heroin-related stimuli had a lower value of the sum of Bayesian information criterion (showing a better fit) than the original CD model (9555.50 versus 11,192.22, P < 0.001). The AHM group had a higher value of the heroin-incentive parameter than the control group (0.26 versus -1.66, P < 0.05). The attention to wins and heroin-incentive parameters were associated positively with total commission rate and negatively with total omission rate in the AHM group (P < 0.001). CONCLUSIONS: Male abstinent heroin misusers appear to be more influenced by heroin-related stimuli during decision-making than males with no history of heroin misuse.