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BackgroundLicensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. ObjectiveThis paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. MethodsBased on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. ResultsWe collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. ConclusionsOntological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.

With the increasingly strict international GHG (greenhouse gas) emission regulations, higher requirements are placed on the propulsion system design of conventional ships. Playing an important role in ship design, construction and operation, ship–engine–propeller matching dominantly covers the CO2 emission of the entire ship. In this paper, firstly, a ship propulsion system matching platform based on the ship–engine–propeller matching principle and its application on WinGD 5X52 marine diesel engine have been investigated. Meeting the energy efficiency design index (EEDI) regulation used to calculate the ship CO2 emission is essential and ship–engine–propeller matching has to be carried out with EEDI into consideration. Consequently, a procedure is developed combining the system matching theory and EEDI calculation, which can provide the matching results as well as the corresponding EEDI value to study the relationship between EEDI and ship–engine–propeller matching. Furthermore, a comprehensive analysis is performed to obtain the relationship of EEDI and system matching parameters, such as ship speed, effective power and propeller diameter, reflecting the trend and extent of EEDI when changing these three parameters. The results of system matching parameters satisfying different EEDI phases indicate the initial value selection in matching process to provide reference for the design of ship, engine and propeller under the EEDI regulations. [ABSTRACT FROM AUTHOR]

Lanxangia tsaoko (L. tsaoko) is a natural medicine which could be used to treat type 2 diabetes mellitus (T2DM). However, there is no systematic and comprehensive research on the its active compounds and mechanism. This study aimed to investigate the active ingredients and potential mechanism of L. tsaoko for the treatment of T2DM. The chemical constituents of L. tsaoko were identified by UPLC-Q-Exactive Orbitrap/MS. The active compounds and mechanism of L. tsaoko were predicted by network pharmacology. Then the docking modes of key components and core targets were analyzed by molecular docking. Finally, animal experiments were conducted to verify the efficacy and targets of L. tsaoko in T2DM treatment. 70 compounds from L. tsaoko were identified. We obtained 37 active components, including quercetin, genistein and kaempferol, 5 core targets were AKT1, INS, TP53, TNF and IL-6. Mainly involved in PI3K/Akt, MAPK, RAGE/AGE, HIF-1, FoxO signaling pathways. Molecular docking results showed that the L. tsaoko had good binding potential to TNF. Therefore, we took the inflammatory mechanism as the prediction target for experimental verification. Animal experiments showed that L. tsaoko could alleviated colon injury of T2DM mice, improve glucose metabolism and decrease inflammatory levels. L. tsaoko exerted therapeutic effects on T2DM through multi-component, multi-target and multi-pathway regulation. Its action mechanisms were related to PI3K/Akt, MAPK, RAGE/AGE, HIF-1 and FoxO signaling pathways. This study provided new insights for the clinical treatment of T2DM. [Display omitted] [ABSTRACT FROM AUTHOR]