Fabrication of plane-type axon guidance substrates by applying diamond-like carbon thin film deposition

Abstract This research aims to fabricate plane-type substrates for evaluating the axon behaviors of neuronal cells in vitro toward the development of brain-on-chip models by applying the functions of diamond-like carbon (DLC) thin film deposition, which helped to eliminate the costly and time-consuming lithography process by utilizing a shadow mask. The DLC thin films were partially deposited on stretched polydimethylsiloxane (PDMS) substrates covered with a metal mask by the plasma chemical vaper deposition method, and using the substrates culture teats with human neuroblastoma cells (SH-SY5Y) were performed. Three patterns of interconnection structures of axons were created on the substrates with disordered and regular linear wrinkle structures with several μm size formed by the depositions. The patterns were characterized by the structure that the aggregations of axons formed on the linear DLC thin film deposited areas were separately placed in regular intervals and connected each other by plenty of axons, which were individually taut in a straight line at about 100 to over 200 μm in length. The substrates expected of uses for evaluation of axon behaviors are available without fabricating guiding grooves by conventional soft lithographic methods requiring multiple stages and their treating times

NRLMFβ: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug–target interaction prediction

Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug–target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug–target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the number of target proteins for a drug). This study aimed to address this issue by proposing NRLMF with beta distribution rescoring (NRLMFβ), which is an algorithm to improve the score of NRLMF. The score of NRLMFβ is equivalent to the value of the original NRLMF score when the concentration of the beta distribution becomes infinity. The beta distribution is known as a conjugative prior distribution of the Bernoulli distribution and can reflect the amount of interaction information to its shape based on Bayesian inference. Therefore, in NRLMFβ, the beta distribution was used for rescoring the NRLMF score. In the evaluation experiment, we measured the average values of area under the receiver operating characteristics and area under precision versus recall and the 95% confidence intervals. The performance of NRLMFβ was found to be better than that of NRLMF in the four types of benchmark datasets. Thus, we concluded that NRLMFβ improved the prediction accuracy of NRLMF. The source code is available at https://github.com/akiyamalab/NRLMFb. Keywords: Drug–target interaction prediction, Neighborhood regularized logistic matrix factorization, Beta distribution, Rescoring, Bayesian optimization, Bayesian inferenc