Table1_EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data.XLSX

Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.</p

Table2_EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data.XLSX

Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.</p

Table_1_How does people-centered integrated care in medical alliance in China promote the continuity of healthcare for internal migrants: The moderating role of respect.XLS

BackgroundContinuity is crucial to the health care of the internal migrant population and urgently needs improvements in China. Chinese government is committed to promoting healthcare continuity by improving the people-centered integrated care (PCIC) model in medical alliances. However, little is known about the driving mechanisms for continuity.MethodsWe created the questionnaire for this study by processes of a literature research, telephone interviews, two rounds of Delphi consultation. Based on the combination of quota sampling and judgment sampling, we collected 765 valid questionnaires from developed region and developing region in Zhejiang Province. Structural equation models were used to examined whether the attributes of PCIC (namely coordination, comprehensiveness, and accessibility of health care) associated with continuity, and explored the moderated mediating role of respect.ResultsThe result of SEM indicated that coordination had direct effect on continuity, and also had mediating effect on continuity via comprehensiveness and accessibility. The hierarchical linear regression analysis showed that the interactive items of coordination and respect had a positive effect on the comprehensiveness (β = 0.132), indicating that respect has positive moderating effect on the relationship between coordination and comprehensiveness. The simple slope test indicated that in the developed region, coordination had a significant effect on comprehensiveness for both high respect group(β = 0.678) and low respect group (β = 0.508). The moderated mediation index was statistically significant in developed areas(β = 0.091), indicating that respect had moderated mediating effect on the relationship between coordination and continuity via comprehensiveness of healthcare in the developed region; however, the moderated mediation effect was not significant in the developing region.ConclusionSuch regional differences of the continuity promoting mechanism deserve the attention of policy-makers. Governments and health authorities should encourage continuity of healthcare for migrants through improving the elements of PCIC—coordination, comprehensiveness and accessibility of healthcare, shaping medical professionalism of indiscriminate respect, and empowering migrants to have more autonomy over selection of services and decisions about their health.</p

NMR Study of the Hydrolysis and Dehydration of Inulin in Water: Comparison of the Catalytic Effect of Lewis Acid SnCl₄ and Brønsted Acid HCl

Various NMR techniques were employed to study the catalytic performance of the Lewis acid SnCl₄ and the Brønsted acid HCl in the conversion of inulin to value-added compounds by hydrolysis and subsequent dehydration. The hydrolysis of inulin was examined to reveal the catalytic abilities of SnCl₄ besides its intrinsic acidity by in situ ¹H and ¹³C NMR at 25 °C. The dehydration reaction of inulin with SnCl₄ as catalyst was followed by high temperature in situ ¹H NMR at 80 °C. The fructose moieties were dehydrated to 5-(hydroxy­methly)­furfural (5-HMF), but the glucose fragment of inulin was inactive for dehydration reaction under this condition. The formation of 5-HMF and its transformation into formic acid and levulinic acid through a rehydration reaction could be monitored by in situ NMR spectroscopy. Moreover, diffusion ordered spectroscopy NMR revealed that the Lewis acid ion, Sn⁴⁺ interacts with the inulin model compounds, i.e., sucrose and fructose. The synergistic effects of complexation and acidity from the hydrolysis of SnCl₄ results in a higher catalytic ability of this Lewis acid catalyst compared with a Brønsted acid

Deep Eutectic Solvents: Green Solvents and Catalysts for the Preparation of Pyrazine Derivatives by Self-Condensation of d‑Glucosamine

Deep eutectic solvents (DESs) exhibit similar physicochemical properties to the ionic liquids. They are inexpensive, renewable, nontoxic, and environmentally benign solvents and have gradually attracted attention in several fields, for example, biorefinery. Here choline chloride-based DESs have been used as solvents and catalysts for the preparation of deoxyfructosazine (DOF) through a self-condensation reaction of d-glucosamine (GlcNH₂). The catalytic performances of a “green cocatalyst”, amino acids, and the reaction mechanism were also studied. The results displayed that choline chloride/urea was capable to convert GlcNH₂ efficiently, with a 13.5% yield of DOF at low temperature and with a short reaction time (100 °C, 150 min). Among the screened amino acids, arginine showed the highest activity and gave the highest yield of DOF (30.1%) under the optimized reaction conditions. Nuclear magnetic resonance (NMR) studies revealed a strong hydrogen bond interaction between GlcNH₂ and arginine. Moreover, a detectable intermediate, namely dihydrofructosazine, in the condensation of GlcNH₂ to DOF/fructosazine (FZ) was captured by in situ NMR technique

DataSheet1_Nrf2−/− regulated lung DNA demethylation and CYP2E1 DNA methylation under PM2.5 exposure.docx

Cytochrome P450 (CYP450) can mediate fine particulate matter (PM2.5) exposure leading to lung injury. Nuclear factor E2-related factor 2 (Nrf2) can regulate CYP450 expression; however, the mechanism by which Nrf2−/− (KO) regulates CYP450 expression via methylation of its promoter after PM2.5 exposure remains unclear. Here, Nrf2−/− (KO) mice and wild-type (WT) were placed in a PM2.5 exposure chamber (PM) or a filtered air chamber (FA) for 12 weeks using the real-ambient exposure system. The CYP2E1 expression trends were opposite between the WT and KO mice following PM2.5 exposure. After exposure to PM2.5,CYP2E1 mRNA and protein levels were increased in WT mice but decreased in KO mice, and CYP1A1 expression was increased after exposure to PM2.5 in both WT and KO mice. CYP2S1 expression decreased after exposure to PM2.5 in both the WT and KO groups. We studied the effect of PM2.5 exposure on CYP450 promoter methylation and global methylation levels in WT and KO mice. In WT and KO mice in the PM2.5 exposure chamber, among the methylation sites examined in the CYP2E1 promoter, the CpG2 methylation level showed an opposite trend with CYP2E1 mRNA expression. The same relationship was evident between CpG3 unit methylation in the CYP1A1 promoter and CYP1A1 mRNA expression, and between CpG1 unit methylation in the CYP2S1 promoter and CYP2S1 mRNA expression. This data suggests that methylation of these CpG units regulates the expression of the corresponding gene. After exposure to PM2.5, the expression of the DNA methylation markers ten-eleven translocation 3 (TET3) and 5-hydroxymethylcytosine (5hmC) was decreased in the WT group but significantly increased in the KO group. In summary, the changes in CYP2E1, CYP1A1, and CYP2S1 expression in the PM2.5 exposure chamber of WT and Nrf2−/− mice might be related to the specific methylation patterns in their promoter CpG units. After exposure to PM2.5, Nrf2 might regulate CYP2E1 expression by affecting CpG2 unit methylation and induce DNA demethylation via TET3 expression. Our study revealed the underlying mechanism for Nrf2 to regulate epigenetics after lung exposure to PM2.5.</p

Identification process for eligible studies.

<p>Identification process for eligible studies.</p

Comparison of overall survival between combined inhibition therapy and single inhibition therapy.

<p>Comparison of overall survival between combined inhibition therapy and single inhibition therapy.</p

Comparison of progression-free survival between combined inhibition therapy and single inhibition therapy.

<p>Comparison of progression-free survival between combined inhibition therapy and single inhibition therapy.</p

Characteristics of included trials.

<p>Abbreviations: NR, not reported; PD, disease progression; PT, prohibitive toxicity.</p