Effects of virtual reality for psychological health of ICU patients: a study protocol for systematic review and meta-analysis.

INTRODUCTION: Virtual reality (VR) has been shown to have a certain influence on the psychological health of intensive care unit (ICU) patients. However, its specific effects-particularly on psychological health problems, such as psychological well-being, quality of life (QOL) and patient satisfaction-remain unclear. METHOD AND ANALYSIS: This study follows the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols guidelines. Electronic data search is carried out on PubMed, Web of Science, CINAHL, EBSCO, EMBASE, Cochrane Library, PsycINFO, China National Knowledge Infrastructure, Wan Fang, VIP and Chinese Biology Medicine Database. The inclusion criteria follow the PICO principle, wherein ICU patients who have been hospitalised for 24 hours or more are included. Studies using VR-based interventions to improve the psychological health of ICU patients, compared with waitlist controls or traditional therapy groups; outcome assessments containing psychological well-being, QOL and patient satisfaction; and those designed as randomised controlled trials (RCTs) and quasi-experimental research are included. Search time is from inception of each database to July 2023. No language restriction is considered. Studies for inclusion are screened by two independent reviewers for data extraction. Any dispute is resolved through discussion. Unresolved disputes are decided on by consulting a third author. For the risk of bias assessment in RCTs and non-RCTs, the Cochrane risk-of-bias tool for randomised trials and risk of bias in non-randomised studies of interventions tool are used, respectively. For meta-analysis, RevMan V.5.3 is used. ETHICS AND DISSEMINATION: This protocol study does not include clinical research and thus does not require ethical approval. Research findings will be released in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42023400428.

Integrated Strategy From In Vitro, In Situ, In Vivo to In Silico for Predicting Active Constituents and Exploring Molecular Mechanisms of Tongfengding Capsule for Treating Gout by Inhibiting Inflammatory Responses.

The study of screening active constituents from traditional Chinese medicine (TCM) is important for explicating the mechanism of action of TCM and further evaluating the safety and efficacy effectively. However, detecting and identifying the active constituents from complicated biological samples still remain a challenge. Here, a practical, quick, and novel integrated strategy from in vitro, in situ, in vivo to in silico for rapidly screening the active constituents was developed. Firstly, the chemical profile of TCM in vitro was identified using UPLC-Q Exactive-Orbitrap HRMS. Secondly, the in situ intestinal perfusion with venous sampling (IPVS) method was used to investigate the intestinal absorption components. Thirdly, after intragastric administration of the TCM extract, the in vivo absorbed prototype components were detected and identified. Finally, the target network pharmacology approach was applied to explore the potential targets and possible mechanisms of the absorbed components from TCM. The reliability and availability of this approach was demonstrated using Tongfengding capsule (TFDC) as an example of herbal medicine. A total of 141 compounds were detected and identified in TFDC, and among them, 64 components were absorbed into the plasma. Then, a total of 35 absorbed bioactive components and 50 related targets shared commonly by compounds and gout were integrated via target network pharmacology analysis. Ultimately, the effects of the absorbed components on metabolism pathways were verified by experiments. These results demonstrated that this original method may provide a practical tool for screening bioactive compounds from TCM treating particular diseases. Furthermore, it also can clarify the potential mechanism of action of TCM and rationalize the application of TFDC as an effective herbal therapy for gout.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy.

We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.

Mechanism of Paeoniae Radix Alba in the Treatment of Non-alcoholic Fatty Liver Disease Based on Sequential Metabolites Identification Approach, Network Pharmacology, and Binding Affinity Measurement.

Screening functional food ingredients (FFI) from medicinal and edible plants (MEP) has still remained a great challenge due to the complexity of MEP and its obscure function mechanisms. Herein, an integrated strategy based on sequential metabolites identification approach, network pharmacology, molecular docking, and surface plasmon resonance (SPR) analysis was proposed for quickly identifying the active constituents in MEP. First, the sequential biotransformation process of MEP, including intestinal absorption and metabolism, and hepatic metabolism, was investigated by oral gavage, and intestinal perfusion with venous sampling method. Then the blood samples were analyzed by UPLC-Q Exactive Orbitrap HRMS. Second, the network pharmacology approach was used to explore the potential targets and possible mechanisms of the in vivo metabolites of MEP. Third, molecular docking and SPR approaches were used to verify the specific interactions between protein targets and representative ingredients. The proposed integrated strategy was successfully used to explore the heptoprotective components and the underlying molecular mechanism of Paeoniae Radix Alba (PRA). A total of 44 compounds were identified in blood samples, including 17 porotypes and 27 metabolites. The associated metabolic pathways were oxidation, methylation, sulfation, and glucuronidation. After further screening, 31 bioactive candidates and 377 related targets were obtained. In addition, the bioactive components contained in PRA may have therapeutic potentials for non-alcoholic fatty liver disease (NAFLD). The above results demonstrated the proposed strategy may provide a feasible tool for screening FFI and elaborating the complex function mechanisms of MEP.

Accurate quantitative determination of affinity and binding kinetics for tight binding inhibition of xanthine oxidase.

The accurate quantitative determination of affinity and binding kinetics (BK) for tight binding inhibition is extraordinary important from both the continuous optimization of compounds, particularly in developing structure-activity relationships (SAR), and the prediction of in vivo target occupancy (TO). Due to the unique properties for tight binding inhibition that the inhibitors are characterized by the ultrahigh-affinity, relatively fast association to the target enzyme combined with extremely slow dissociation of the inhibitor-enzyme binary complex, the classical steady state equilibrium methods are no longer valid. Here, we made several recommendations of how to design the optimal experiments and apply special mathematical calculation approaches to quantitatively evaluate the accurate affinity and BK as the examples of two tight binding inhibitors against the xanthine oxidase (XO), as well as compared the differences in the results calculated from the different data analytical methods and analyzed the influence of these differences on the XO engagement in human. Analysis of the results displayed that the accurate apparent dissociation constant (Ki*,app) was 0.2 ± 0.06 nM for topiroxotstat and was 0.45 ± 0.2 nM for febuxostat; that on-rate (kon) was (4.3 ± 1.1) × 106 M-1s-1 for topiroxotstat and was(133.3 ± 3.5) × 106 M-1s-1 for febuxostat, and off-rate (koff) was (1.0±0.2) × 10-5 s-1 for topiroxotstat and was ≤ 0.16 × 10-5 s-1for febuxostat. Moreover, there were significant differences in the Ki*,app and koff values estimated using the appropriate specialized methods for tight binding inhibition versus classical steady state equilibrium methods, with the substantial differences of 14-fold and 32-fold reduction for topiroxostat, respectively, and of 9.6-fold and ≥ 213-fold reduction for febuxostat, while the kon values remain the moderate differences for the two inhibitors. The obvious greater AUC of XO engagement time courses and longer durations of above 70% engagement by the appropriate specialized methods for tight binding inhibition were observed that the results display the differences of 70.1% and 88%, respectively for topiroxostat and of 38.1% and 35.0%, respectively for febuxostat in human liver cell than by classical steady state equilibrium methods. Again, our studies provide several valuable recommendations of the optimal experiment protocols and appropriate analytical approaches for accurately quantitatively assessing the affinity and BK parameters as well as demonstrate the ability of our recommended methods to generate reliable data for tight binding inhibitors against XO.

[Study on drug-target binding kinetics profiles of flavonoids in Chrysanthemum morifolium and xanthine oxidase].

Based on the target occupancy mathematical model, the binding kinetic process of potential active ingredients of lowering uric acid in Chrysanthemum morifolium with xanthine oxidase(XOD) was evaluated. The potential active ingredients of lowering uric acid in Ch. morifolium were screened by UPLC-Q-Exactivems MS technology, reference substance identification and in vitro enzymatic kinetics experiments. The binding kinetic parameters of xanthine oxidase and potential inhibitor in Ch. morifolium were determined by surface plasma resonance(SPR). The verified mathematical model of the XOD target occupancy evaluated the kinetic binding process of inhibitors and xanthine oxidase in vivo. According to UPLC-Q-Exactive MS and reference substance identification, 39 potential uric acid-lowering active ingredients in Ch. morifolium extracts were identified and the inhibitory activities of 23 compounds were determined. Three potential xanthine oxidase inhibitors were screened, namely genistein, luteolin, and apigenin. whose IC_(50 )were 1.23, 1.47 and 1.59 µmol·L~(-1), respectively. And the binding rate constants(K_(on)) were 1.26×10~6, 5.23×10~5 and 6.36×10~5 mol·L~(-1)·s~(-1), respectively. The dissociation rate constants(K_(off)) were 10.93×10~(-2), 1.59×10~(-2), and 5.3×10~(-2 )s~(-1), respectively. After evaluation by different administration methods, the three selected compounds can perform rapid and sustained inhibition of xanthine oxidase in vivo under combined administration. This study comprehensively evaluated the target occupancy process of three effective components in different ways of administration in vivo by UPLC-MS, concentration-response method, SPR technology and xanthine oxidase target occupancy model, which would provide a new research idea and method for screening active ingredients in traditional Chinese medicine.

KG2Vec: A node2vec-based vectorization model for knowledge graph.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.