RESUMEN
The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide ÐApp developers with concrete insights that can help them provide and maintain high levels of QoS.
RESUMEN
Biochemical screening is a major source of lead generation for novel targets. However, during the process of small molecule lead optimization, compounds with excellent biochemical activity may show poor cellular potency, making structure-activity relationships difficult to decipher. This may be due to low membrane permeability of the molecule, resulting in insufficient intracellular drug concentration. The Cell Squeeze platform increases permeability regardless of compound structure by mechanically disrupting the membrane, which can overcome permeability limitations and bridge the gap between biochemical and cellular studies. In this study, we show that poorly permeable Janus kinase (JAK) inhibitors are delivered into primary cells using Cell Squeeze, inhibiting up to 90% of the JAK pathway, while incubation of JAK inhibitors with or without electroporation had no significant effect. We believe this robust intracellular delivery approach could enable more effective lead optimization and deepen our understanding of target engagement by small molecules and functional probes.
