DDI Domination Directory International Issue 66 Brittany Andrews Like New

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National Heart Lung and Blood Institute. Quality assessment tool for observational cohort and cross-sectional studies. https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools. Accessed 5 Jan 2022. Fig. 2 An overview of the proposed SA-DDI for DDI prediction. The model takes a pair of drugs as input and then feeds them into a feed-forward layer, followed by a D-MPNN equipped with the substructure attention to extract the size- and shape-adaptive substructures. The directed message passing network updates the node-level features with T iterations where T is 6 in this example. The extracted substructures are then fed into the SSIM to learn the substructure–substructure interactions. Finally, the model predicts DDI based on the result of substructure–substructure interactions.

How does substruction attention solve over-smoothing problems? Theoretically, a GNN with more layers/iterations would be more aware of the graph structure. 33 However, increasing the depth of the GNN may cause an over-smoothing representation of vertices. Our demand for a model that is more expressive and aware of the graph structure (by adding more layers/iterations so that vertices can have a large receptive field) could be transformed into a demand for a model that treats vertices all the same ( i.e., features at vertices within each connected component converging to the same value). 33 Bjerrum L, Gonzalez Lopez-Valcarcel B, Petersen G. Risk factors for potential drug interactions in general practice. Eur J Gen Pract. 2008;14:23–9. https://doi.org/10.1080/13814780701815116. where each indicates the importance of the substructures with a radius of t. The final representation of a bond e i→ j, which captures the substructure information with different radii, is given by the weighted sum of bond-level hidden features across all steps according to the following: H. Yu, K.-T. Mao, J.-Y. Shi, H. Huang, Z. Chen, K. Dong and S.-M. Yiu, BMC Syst. Biol., 2018, 12, 101–110 CrossRef PubMed . Table 1 summarizes the included studies’ main study characteristics and results. The majority of studies ( n = 16) investigated the relationship between COC and MARO [ 43, 44, 45, 46, 47, 51, 56, 57, 58, 60, 61, 63, 64, 66, 68, 69]. Seven studies focused on the relationship between COC and polypharmacy [ 48, 53, 54, 62, 65, 67], and four studies investigated both the relationship between COC and MARO and between COC and polypharmacy [ 49, 50, 52, 59].Polypharmacy was mostly defined as having five or more medications prescribed (binary variable) [ 49, 50, 52, 55, 59, 62, 65, 67]. Some studies (additionally) included extreme/excessive polypharmacy (≥10 medications prescribed) [ 50, 52, 53, 54, 55, 62]. One study operationalized multiclass psychotropic polypharmacy as taking two or more psychotropic medications from different drug classes for 60 days or more [ 48]. Observational periods varied from 2 weeks to 1 year; two studies also considered persistent (>181 days) polypharmacy [ 50, 62] (Table 2; Table S1, see ESM). 3.2.3 Operationalization of Medication Appropriateness-Related Outcomes (MARO) Cold start for a pair of drugs (new ↔ new) is also a cold start scenario where both drugs in a drug pair in the test set are inaccessible in the training set. Cold start for a single drug (new ↔ old) is a cold start scenario in which one drug in a drug pair in the test set is inaccessible in the training set. We further considered two settings in this scenario, as follows: (1) the drugs are split randomly; and (2) the drugs are split according to their structures. Drugs in the training and test sets are structurally different ( i.e., the two sets have guaranteed minimum distances in terms of structure similarity). We used Jaccard distance on binarized ECFP4 features to measure the distance between any two drugs in accordance with the method described in a previous study. 42 Tommelein E, Mehuys E, Petrovic M, Somers A, Colin P, Boussery K. Potentially inappropriate prescribing in community-dwelling older people across Europe: a systematic literature review. Eur J Clin Pharmacol. 2015;71:1415–27. https://doi.org/10.1007/s00228-015-1954-4.

Green JL, Hawley JN, Rask KJ. Is the number of prescribing physicians an independent risk factor for adverse drug events in an elderly outpatient population? Am J Geriatr Pharmacother. 2007;5:31–9. https://doi.org/10.1016/j.amjopharm.2007.03.004. For example, your primary phone number might be 01202 551000, and you ask for a range of 20 direct-dial-in numbers. Your provider would therefore issue a range such as this: Kerse N, Buetow S, Mainous AG, Young G, Coster G, Arroll B. Physician-patient relationship and medication compliance: a primary care investigation. Ann Fam Med. 2004;2:455–61. https://doi.org/10.1370/afm.139.

Hajjar ER, Hanlon JT, Sloane RJ, Lindblad CI, Pieper CF, Ruby CM, et al. Unnecessary drug use in frail older people at hospital discharge. J Am Geriatr Soc. 2005;53:1518–23. https://doi.org/10.1111/j.1532-5415.2005.53523.x.

where y i = 1 indicates that an interaction exists between d x and d y, and vice versa; and p i is the predictive interaction probability of a DDI tuple ( i.e., eqn (12)). 3 Results and discussion 3.1 Dataset We evaluated the model performance in two real-world datasets—DrugBank and TWOSIDES. The interindividual variability for the pharmacokinetic parameter affected by the covariate was lower compared to the model without the covariate relationship.The population pharmacokinetic meta-analysis performed was in healthy individuals. It should be noted that due to sometimes narrow inclusion criteria in clinical pharmacology studies, the intrinsic and extrinsic factors of the population studied do not always fully reflect the ones in the patient population. Existing computational methods can be divided into two categories, namely, text mining-based and machine learning-based methods. 2 Text mining-based methods extract drug–drug relations between various entities from scientific literature, 3–7 insurance claim databases, electronic medical records, 8 and the FDA Adverse Event Reporting System; 9 these methods are efficient in building DDI-related datasets. However, they cannot detect unannotated DDIs or potential DDIs before a combinational treatment is made. 10 Conversely, machine learning-based methods have the potential to identify unseen DDIs for downstream experimental validations by generalizing the learned knowledge to unannotated DDIs. A second surprising observation from the present study is that, even when differences in drug concentrations were taken into account, the effects of treatments combining two NRTIs could not be predicted from the individual effects of each analogue (Fig. ​ (Fig.1 1 to ​ to3; 3; Tables ​ Tables2 2 and ​ and4). 4). For example, whereas 3TC alone, AZT alone, or d4T alone each significantly decreased hepatic mtDNA, the administration of 3TC in combination with either AZT or d4T had no significant effects on hepatic mtDNA (Table ​ (Table2). 2). Although concentrations of d4T and AZT in plasma were lower after the dual treatments, 3TC concentrations in plasma were similar after all 3TC-containing treatments (Table ​ (Table1) 1) and would have been expected to also decrease hepatic mtDNA. Therefore, complex interactions seem to occur between different NRTIs. By the same token, Roche et al. ( 47) recently showed that the antiadipogenic effects of AZT in a murine preadipocyte cell line were eliminated when 3TC was added to AZT. Effect of NRTIs on the in vivo formation of 14CO 2 from [U- 14C]palmitate in mice. Mice were treated or not for 2 weeks with AZT (100 mg/kg/day), 3TC (50 mg/kg/day), ddI (66 mg/kg/day), d4T (13.5 mg/kg/day), ddC (0.36 mg/kg/day), or three combinations of two NRTIs (same doses as for the single-drug treatments) and fasted for the last 48 h of treatment. A tracer dose of [U- 14C]palmitate was administered, and 14CO 2 exhalation was measured for 120 min. Values for treated animals were expressed as percentages of the values for the corresponding controls. Each of the eight different control groups included 7 to 11 mice. Results for treated animals are means ± SEMs for 8 to 12 mice. The asterisk indicates a significant difference from results for the corresponding controls ( P< 0.05).

Z. Yang, L. Zhao, S. Wu and C. Y.-C. Chen, IEEE J. Biomed. Health Inform., 2021, 25, 1864–1872 Search PubMed . ROCHA, José Manuel (30 de outubro de 1999). «O número que marco foi alterado». Público . Consultado em 25 de junho de 2015 J. Y. Ryu, H. U. Kim and S. Y. Lee, Proc. Natl. Acad. Sci. U. S. A., 2018, 115, E4304–E4311 CrossRef CAS PubMed . Our findings have significant implications for health care research and practice. Concerning the operationalization and measurement of COC, our methodological findings highlight that researchers should (i) ensure that all three dimensions of COC (relational, informational, and management continuity) are covered by the COC measures used, (ii) use and compare different COC measures of the same type, (iii) use a combination of subjective and objective COC measures, and (iv) draw from a combination of claims data and patient-reported survey data when doing so. These steps will help researchers better understand and use the various tools available for measuring COC. In particular, future research should aim to identify or develop an appropriate and agreed-upon operationalization of COC, polypharmacy, and MARO to ensure the comparability of results. Researchers investigating the link between COC and outcomes such as polypharmacy or MARO should use longitudinal study designs where possible and give particular regard to the relative timing of exposures and outcomes. Nyborg G, Straand J, Brekke M. Inappropriate prescribing for the elderly—a modern epidemic? Eur J Clin Pharmacol. 2012;68:1085–94. https://doi.org/10.1007/s00228-012-1223-8.

Chu H-Y, Chen C-C, Cheng S-H. Continuity of care, potentially inappropriate medication, and health care outcomes among the elderly: evidence from a longitudinal analysis in Taiwan. Med Care. 2012;50:1002–9. https://doi.org/10.1097/MLR.0b013e31826c870f. where ⊙ represents dot product, is a weight vector for step t, and σ is an activation function. We chose the tanh function as the activation function, because it works fairly well in practice. To make coefficients easily comparable across different steps, we normalize e ( t) across all steps using the softmax function Fig. 5 The accuracy and F1-score of different methods for each interaction type in the (a) DrugBank dataset and (b) TWOSIDES dataset. Nicolet A, Al-Gobari M, Perraudin C, Wagner J, Peytremann-Bridevaux I, Marti J. Association between continuity of care (COC), healthcare use and costs: what can we learn from claims data? A rapid review. BMC Health Serv Res. 2022;22:658. https://doi.org/10.1186/s12913-022-07953-z.