Food effect, also known as food-drug interactions, is a common phenomenon associated with orally administered medications and can be defined as changes in absorption rate or absorption extent. The mechanisms of food effect and their consequences can involve multiple factors, including human post-prandial physiology, properties of the drug, and how the drug is administered. Therefore, it is essential to have a thorough understanding of these mechanisms when recommending whether a specific drug should be taken with or without food. Food-drug interactions can be clinically relevant, especially when they must be avoided to prevent undesirable effects or exploited to optimize medication therapy. This review conducts a literature search that examined studies on food effect. We summarized the literature and identified and discussed common food effect mechanisms. Furthermore, we highlighted drugs that have a clinically significant food effect and discussed the corresponding mechanisms. In addition, this review analyzes the effects of high-fat food or standard meals on the oral drug absorption rate and absorption extent for 229 drugs based on the Biopharmaceutics Drug Disposition Classification System and demonstrates an association between Biopharmaceutics Drug Disposition Classification System class and food effect.
Summary: Three‐dimensional polyaniline (PANI) nanowire networks were synthesized in high yield using a “soft template” self‐assembled with hexadecyltrimethylammonium bromide and oxalic acid. The PANI nanowire networks had diameters from 35–100 nm depending on synthesis conditions and/or procedures. The networks and the “cross‐linking points” were clearly observed by field‐emission scanning electron microscopy and transmission electron microscopy. A possible mechanism for the formation of three‐dimensional PANI nanowire networks is discussed.
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.
Objective The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. Methods Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner’s Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. Results The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). Conclusions LSTM–based sequential deep learning models can accurately predict OUD using a patient’s history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
Summary: It was found that without additional photoinitiator, methyl methacrylate (MMA) dissolved in common solvent N,N‐dimethylformamide (DMF) could be photografted steadily on low‐density polyethylene (LDPE) film surface under UV irradiation. In short irradiation time (4 min) and at room temperature, high grafting efficiency (approaching 100%) and remarkable graft polymer amount (grafting percent is about 4.6%) was obtained. The possible reaction mechanism was based on the photosensitivity of DMF, which induced this photografting polymerization. This finding is useful to develop photoinitiator‐free grafting or photopolymerization system. Using SEM, special discrete globular structure was found on the MMA‐grafted LDPE film surface, and a possible model was proposed to interpret it.image
This article reports on a new sequential strategy to fabricate monolayer functional organosilane films on inorganic substrate surfaces, and subsequently, to pattern them by two new photochemical reactions. (1) By using UV light (254 nm) plus dimethylformamide (DMF), a functional silane monolayer film could be fabricated quickly (within minutes) under ambient temperature. (2) The organic groups of the formed films became decomposed in a few minutes with UV irradiation coupled with a water solution of ammonium persulfate (APS). (3) When two photochemical reactions were sequentially combined, a high-quality patterned functional surface could be obtained thanks to the photomask.
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