{'fraction_non_alphanumeric': 0.023278370514064017, 'fraction_numerical': 0.0, 'mean_word_length': 5.254545454545455, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Model-based Analysis for target drug delivery on Internet of Bio-Health Thing in term of cardiovascular disease

The internet of bio-health things(IoBHTs) is derived from synthetic biology and Molecular Communication Networks (MCN) for controlling the embedded devices in robust healthcare industry (HCI). The problem of coordination between MCN and Internet/Cloud is still to be solved. Therefore, in this study, we propose a model-based analysis and comparison to study the effects and differences of drug concentrations in vivo network through IoBHT, taking into account the characteristics of target cells and the ligand receptor (Rx) binding mechanism in term cardiovascular diseases (CVD). The proposed model for the outside of the human body and inside of human body link is obtainable. The results show that the model can improve the communication ability of target cells to therapeutic drugs and minimize the side effects of healthy cells. A deep learning method for estimating biological interface parameters is proposed.