Gremlin Language for Querying the BiographDB Integrated Biological Database

Author(s):  
Antonino Fiannaca ◽  
Laura La Paglia ◽  
Massimo La Rosa ◽  
Antonio Messina ◽  
Riccardo Rizzo ◽  
...  
Keyword(s):  
2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 54.1-54
Author(s):  
S. Benamar ◽  
C. Lukas ◽  
C. Daien ◽  
C. Gaujoux-Viala ◽  
L. Gossec ◽  
...  

Background:Polypharmacy is steadily increasing in patients with rheumatoid arthritis (RA). They may interfere with treatment response and the occurrence of serious adverse events. Medications taken by a patient may reflect active comorbidities, whereas comorbidity indices usually used include past or current diseases.Objectives:To evaluate whether polypharmarcy is associated with treatment response and adverse events in an early RA cohort and to establish whether polypharmacy could represent a substitute of comorbidities.Methods:We used data from the French cohort ESPOIR, including 813 patients with early onset arthritis. Patients included the current study had to start their first disease modifying anti-rheumatic drug (DMARD) within 24 months of inclusion in the cohort. Disease activity data were collected at one, five and ten years from the initiation of the first DMARD. For each patient, treatments were collected at baseline and at five years. Medications count included all specialties other than background RA therapy, analgesics/NSAIDs and topicals. Polypharmacy was defined as a categorical variable based on the median and tertiles of distribution in the cohort. Treatment response was assessed by achieving DAS28 ESR remission (REM) at 1 year, 5 years and 10 years from the initiation of the first DMARD. The occurrence of severe adverse events (SAE) was measured by the occurrence of severe infection, hospitalization, or death during the 10-year follow-up. The association between patient’s characteristics and achievement of REM and occurrence of SAE were tested in univariate analysis. A logistic regression model was used to evaluate associations between polypharmacy and REM at 1 year, 5 years and 10 years (we used baseline polypharmacy for the 1-year analysis and five years polypharmacy for the 5- and 10-years analyses). Multivariate adjustment was made for age, sex, BMI, duration of disease, initial DAS28 ESR, initial HAQ, smoking status, rheumatic disease comorbidity index (RDCI).Results:The proportion of patients who achieved REM one year after the initiation of the first DMARD was 32.1% in the polypharmacy according to the median group (patients taken ≥2 medication) versus 67.9% in the non-polypharmacy group (p=0.07). At 5 years after the first DMARD, the proportion of patients with REM was 45.0% in the polypharmacy group versus 56.3% in the non-polypharmacy group (p=0.03). At 10 years the proportion of patients with REM was 32.5% in the polypharmacy group versus 67.5% (p=0.06). Patients who take greater or equal to 2 medications had a 40% lower probability of achieving REM (OR = 0.60 [0.38-0.94] p = 0.03) at 5 years from the first DMARD (if RDCI index was not included in the model). At 10 years, patients receiving multiple medications had a 43% lower probability of achieving REM (OR = 0.57 [0.34-0.94] p = 0.02). SAE incidence was 61 per 1000 patient-years. For patients who developed SAE all causes 71.4% where in the polypharmacy group versus 57.8% were in the non-polypharmacy group (p = 0.03; univariate analysis). These results are no longer significant after adjustment for comorbidities indices.Conclusion:In this early RA cohort, polypharmacy is associated with a poorer treatment response and increased risk of adverse events. Polypharmacy may represent a good substitute of comorbidities for epidemiological studies.Acknowledgements:We are grateful to Nathalie Rincheval (Montpellier) who did expert monitoring and data management and all theinvestigators who recruited and followed the patients (F. Berenbaum, Paris-Saint Antoine; MC. Boissier, Paris-Bobigny; A. Cantagrel, Toulouse; B. Combe, Montpellier; M. Dougados, Paris-Cochin; P. Fardellone and P. Boumier, Amiens; B. Fautrel, Paris-La Pitié; RM. Flipo, Lille; Ph. Goupille, Tours; F. Liote, Paris- Lariboisière; O. Vittecoq, Rouen; X. Mariette, Paris-Bicêtre; P. Dieude, Paris Bichat; A. Saraux, Brest; T. Schaeverbeke, Bordeaux; and J. Sibilia, Strasbourg).The work reported on in the manuscript did not benefit from any financial support. The ESPOIR cohort is sponsored by the French Society for Rheumatology. An unrestricted grant from Merck Sharp and Dohme (MSD) was allocated for the first 5 years. Two additional grants from INSERM were obtained to support part of the biological database. Pfizer, Abbvie, Lilly and more recently Fresenius and Biogen also supported the ESPOIR cohort.Disclosure of Interests:Soraya Benamar: None declared, Cédric Lukas Speakers bureau: Abbvie, Amgen, Janssen, Lilly, MSD, Novartis, Pfizer, Roche-Chugai, UCB, Consultant of: Abbvie, Amgen, Janssen, Lilly, MSD, Novartis, Pfizer, Roche-Chugai, UCB, Grant/research support from: Pfizer, Novartis and Roche-Chugai, Claire Daien Speakers bureau: AbbVie, Abivax, BMS, MSD, Roche, Chugai, Novartis, Pfizer, Sandoz, Lilly, Consultant of: AbbVie, Abivax, BMS, MSD, Roche, Chugai, Novartis, Pfizer, Sandoz, Lilly, Cécile Gaujoux-Viala Speakers bureau: Abbvie, BMS, Celgene, Janssen, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi, Roche-Chugai, UCB, Consultant of: Abbvie, BMS, Celgene, Janssen, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi, Roche-Chugai, UCB, Grant/research support from: Pfizer, Laure Gossec Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sandoz, Sanofi-Aventis et UCB, Consultant of: AbbVie, Amgen, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sandoz, Sanofi-Aventis et UCB, Anne-Christine Rat Speakers bureau: Pfizer, Lilly, Consultant of: Pfizer, Lilly, Bernard Combe Speakers bureau: AbbVie; Bristol-Myers Squibb; Gilead; Janssen; Lilly; Merck; Novartis; Pfizer; Roche-Chugai; and Sanofi;, Consultant of: AbbVie; Bristol-Myers Squibb; Gilead; Janssen; Lilly; Merck; Novartis; Pfizer; Roche-Chugai; and Sanofi;, Grant/research support from: Novartis, Pfizer, and Roche-Chugai., Jacques Morel Speakers bureau: Abbvie, BMS, Lilly, Médac, MSD, Nordic Pharma, Pfizer, UCB, Consultant of: Abbvie, BMS, Lilly, Médac, MSD, Nordic Pharma, Pfizer, UCB, Grant/research support from: BMS, Pfizer


Author(s):  
Zina Ben Miled ◽  
Yang Liu ◽  
Nianhua Li ◽  
Omran Bukhres

Author(s):  
Shenggang Guo ◽  
Zhiling Yuan ◽  
Fenghe Wu ◽  
Yongxin Li ◽  
Shaoshuai Wang ◽  
...  

The selection of biomimetic prototypes mostly depends on the subjective observation of a designer. This research uses TRIZ to explore some inferential steps in bionic design of the heavy machine tool column. Conflict resolution theory of TRIZ is applied to describe improved and deteriorated parameters and a contradiction matrix is used to obtain recommended inventive principles. A reference table of solutions corresponding to the biological phenomenon and TRIZ solutions is formed to expedite retrieving the biomimetic object. Based on the table, herbaceous hollow stem is selected to imitate column structure. Four kinds of plant are chosen from the biological database. To select the best from four candidates, a bionic ideality evaluation index is proposed based on similarity analysis and ideality evaluation theory in TRIZ. Thus, the bionic effect can be described and compared quantitatively. Bionic configuration is then evolved concerning manufacturing requirements. Size optimization of stiffener thicknesses is implemented finally, and satisfactory results of the lightweight effect is obtained.


Author(s):  
Jayapriya J. ◽  
Michael Arock

In bioinformatics, sequence alignment is the heart of the sequence analysis. Sequence can be aligned locally or globally depending upon the biologist's need for the analysis. As local sequence alignment is considered important, there is demand for an efficient algorithm. Due to the enormous sequences in the biological database, there is a trade-off between computational time and accuracy. In general, all biological problems are considered as computational intensive problems. To solve these kinds of problems, evolutionary-based algorithms are proficiently used. This chapter focuses local alignment in molecular sequences and proposes an improvised hybrid evolutionary algorithm using particle swarm optimization and cellular automata (IPSOCA). The efficiency of the proposed algorithm is proved using the experimental analysis for benchmark dataset BaliBase and compared with other state-of-the-art techniques. Using the Wilcoxon matched pair signed rank test, the significance of the proposed algorithm is explicated.


Sign in / Sign up

Export Citation Format

Share Document