Use of an in silico knowledge discovery approach to determine mechanistic studies of silver nanoparticles-induced toxicity from in vitro to in vivo

Background Silver nanoparticles (AgNPs) are considered a double-edged sword that demonstrates beneficial and harmful effects depending on their dimensions and surface coating types. However, mechanistic understanding of the size- and coating-dependent effects of AgNPs in vitro and in vivo remains elusive. We adopted an in silico decision tree-based knowledge-discovery-in-databases process to prioritize the factors affecting the toxic potential of AgNPs, which included exposure dose, cell type and AgNP type (i.e., size and surface coating), and exposure time. This approach also contributed to effective knowledge integration between cell-based phenomenological observations and in vitro/in vivo mechanistic explorations. Results The consolidated cell viability assessment results were used to create a tree model for generalizing cytotoxic behavior of the four AgNP types: SCS, LCS, SAS, and LAS. The model ranked the toxicity-related parameters in the following order of importance: exposure dose > cell type > particle size > exposure time ≥ surface coating. Mechanistically, larger AgNPs appeared to provoke greater levels of autophagy in vitro, which occurred during the earlier phase of both subcytotoxic and cytotoxic exposures. Furthermore, apoptosis rather than necrosis majorly accounted for compromised cell survival over the above dosage range. Intriguingly, exposure to non-cytotoxic doses of AgNPs induced G2/M cell cycle arrest and senescence instead. At the organismal level, SCS following a single intraperitoneal injection was found more toxic to BALB/c mice as compared to SAS. Both particles could be deposited in various target organs (e.g., spleen, liver, and kidneys). Morphological observation, along with serum biochemical and histological analyses, indicated that AgNPs could produce pancreatic toxicity, apart from leading to hepatic inflammation. Conclusions Our integrated in vitro, in silico, and in vivo study revealed that AgNPs exerted toxicity in dose-, cell/organ type- and particle type-dependent manners. More importantly, a single injection of lethal-dose AgNPs (i.e., SCS and SAS) could incur severe damage to pancreas and raise blood glucose levels at the early phase of exposure.

NETME: on-the-fly knowledge network construction from biomedical literature

Background The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

An Interview with Dr. Shipeng Yu, Winner of ACM SIGKDD 2021 Service Award

Shipeng Yu, Ph.D. is the recipient of the 2021 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Yu was honored for his years of service and many accomplishments as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. Dr. Yu is Director of AI Engineering, Head of the Growth AI team at LinkedIn, the world's largest professional network. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 2006, the benefits and drawbacks of virtual conferences, his work at LinkedIn, and KDD's place in the field of machine learning, data science and artificial intelligence.