The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors.

Using a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output.

The correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed.

ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

Auditory brainstem responses (ABRs) offer a unique opportunity to assess the neural integrity of the peripheral auditory nervous system in individuals presenting with listening difficulties. ABRs are typically recorded and analyzed by an audiologist who manually measures the timing and quality of the waveforms. The interpretation of ABRs requires considerable experience and training, and inappropriate interpretation can lead to incorrect judgments about the integrity of the system. Machine learning (ML) techniques may be a suitable approach to automate ABR interpretation and reduce human error.

The main objective of this paper was to identify a suitable ML technique to automate the analysis of ABR responses recorded as a part of the electrophysiological testing in the Auditory Processing Disorder clinical test battery.

ABR responses recorded during routine clinical assessment from 136 children being evaluated for auditory processing difficulties were analyzed using several common ML algorithms Support Vl be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

The findings of the present study demonstrate that it is possible to develop accurate ML models to automate the process of analyzing ABR waveforms recorded at suprathreshold levels. There is currently no ML-based application to screen children with listening difficulties. Therefore, it is expected that this work will be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

The hybrid artificial pancreas regulates glucose levels in people with type 1 diabetes. It delivers (i) insulin boluses at meal times based on the meals’ carbohydrate content and the carbohydrate ratios (CRs) and (ii) insulin basal, between meals and at night, continuously modulated around individual-specific programmed basal rate. The CRs and programmed basal rate significantly vary between individuals and within the same individual with type 1 diabetes, and using suboptimal values in the hybrid artificial pancreas may degrade glucose control. We propose a reinforcement learning algorithm to adaptively optimize CRs and programmed basal rate to improve the performance of the hybrid artificial pancreas.

The proposed reinforcement learning algorithm was designed using the Q-learning approach. The algorithm learns the optimal actions (CRs and programmed basal rate) by applying them to the individual’s state (previous day’s glucose levels and insulin delivery) based on an exploration and exploitation trade-ofat the proposed algorithm has the potential to improve glucose control in people with type 1 diabetes using the hybrid artificial pancreas. The proposed algorithm is a key in making the hybrid artificial pancreas adaptive for the long-term real life outpatient studies.The pregnane X receptor (PXR) is one of the major transcription factors that regulate the expression of different drug-metabolizing enzymes and transporters. Adenosine-to-inosine RNA editing, the most frequent nucleotide conversion on RNA, which is catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes, may modulate gene expression and function. Here, we investigated the potential regulation of human PXR expression by adenosine-to-inosine RNA editing. Knockdown of ADAR1 increased PXR mRNA level, and the knockdown of ADAR1 or ADAR2 significantly increased PXR protein level in HepaRG cells. In HepG2 cells, the knockdown of ADAR1 or ADAR2 significantly increased PXR mRNA and protein levels. 3-TYP in vivo The increase in the PXR protein by ADAR1 knockdown resulted in increased cytochrome P450 3A4 (CYP3A4) transactivity and CYP3A4 and UDP-glucuronosyltransferase 1A1 (UGT1A1) expression. A reporter assay revealed that the 3′-untranslated region (UTR) of PXR mRNA, especially from +3371 to +3440, is responsible for the ADAR-mediated post-transcriptional control of PXR expression, despite the lack of RNA edited sites in this region. Collectively, we found that PXR is negatively regulated by ADAR1 via an indirect mechanism, which facilitates the degradation of PXR mRNA. We could demonstrate that ADAR1 can cause interindividual variability in hepatic drug metabolism potencies.Alkali-mediated disintegration is efficient to improve the anaerobic digestion of waste activated sludge (WAS). In the present study, the role and potential of refinery spent caustic (RSC), an alkaline hazardous waste, in enhancing the disintegration of refinery waste activated sludge (RWAS) was investigated. The high alkalinity and free ammonia of RSC destroyed the microbial cell wall and promoted the release of intracellular substances. The contents of N-acetylglucosamine and proteins in the disintegrated liquid greatly increased to 0.41 mg/L and 1147 mg/L, respectively, relative to no disintegration (0.04 mg/L and 3.3 mg/L). The methane production (66.1 mL/g-VS) from RWAS anaerobic digestion increased by 226% compared to without disintegration (20.3 mL/g-VS). This study provides a newly developed “wastes-treat-wastes” management approach of refinery wastewater using combined treatment processes for RWAS and RSC using a cost-efficient and environmentally friendly disintegration of RWAS.