On Sustaining Dynamic Adaptation of Context-Aware Services

The modern human is getting more and more mobile having access to online services by using mobile cutting-edge computational devices. In the last decade, the field of context-aware services had led to emerge several works. However, most of the proposed approaches have not provided clear adaptation strategies in case of unforeseen contexts. Dealing with this last at runtime is also another crucial need that has been ignored in their proposals. This paper aims to propose a generic dynamic adaptation process as a phase in a model-driven development life-cycle for context-aware services using the MAPE-K control loop to meet the runtime adaptation. This process is validated by implementing an illustrative application on FraSCAti platform. The main benefit of the proposed process is to sustain the self-reconfiguration of such services at model and code levels by enabling successive dynamic adaptations depending on the changing context

Development of new benzil-hydrazone derivatives as anticholinesterase inhibitors: synthesis, X-ray analysis, DFT study and <i>in vitro</i>/<i>in silico</i> evaluation

Alzheimer’s disease (AD) is a complex neurodegenerative disorder affecting the central nervous system. Current drugs for AD have limited effectiveness and often come with side effects. Consequently, there is a pressing need to develop new, safe, and more effective treatments for Alzheimer’s disease. In this work, two novel benzil-hydrazone compounds, abbreviated 2-ClMHB and 2-ClBHB, were synthesized for the first time by refluxing the benzil with 2-Chloro phenyl hydrazine and they have been tested for their in vitro anti-cholinesterase activities and in silico acetyl and butyryl enzymes inhibition. The resulting products were characterized using UV-Vis and IR spectroscopy, while the single-crystal X-ray diffraction investigation was successful in establishing the structures of these compounds. DFT calculations have been successfully made to correlate the experimental data. According to biological studies, the synthesized hydrazones significantly inhibited both butyrylcholinesterase (2-ClMHB: 20.95 ± 1.29 µM and 2-ClBHB: 31.21 ± 1.50 µM) and acetylcholinesterase (2-ClMHB: 21.80 ± 1.10 µM and 2-ClBHB: 10.38 ± 1.27 µM). Moreover, molecular docking was also employed to locate the molecule with the optimum interaction and stability as well as to explain the experimental findings. The compound’s dynamic nature, binding interaction, and protein-ligand stability were investigated using molecular dynamics (MD) simulations. Analyzing parameters such as RMSD and RMSF indicated that the compound remained stable throughout the 100 ns MD simulation. Finally, the drugs displayed high oral bioavailability, as per projected ADME and pharmacokinetic parameters. Communicated by Ramaswamy H. Sarma</p

Severe asthma care trajectories: the French RAMSES cohort

International audienceBackground The French RAMSES study is an observational prospective multicentre real-life cohort including severe asthmatic subjects. The objective of the study was to compare the characteristics of patients, in terms of phenotype and asthma care trajectories, between those managed by tertiary referral centres (TRCs) or secondary care centres (SCCs).Methods Patients were prospectively recruited and enrolled for a 5-year follow-up. Patients’ characteristics were analysed at inclusion and compared between TRCs and SCCs.Results 52 centres (24 TRCs and 28 SCCs) included 2046 patients: 1502 (73.4%) were included by a TRC and 544 (26.6%) by a SCC. Patients were mainly women (62%), 53±15 years old, 67% with Asthma Control Test <20; at inclusion, 14% received oral corticosteroids (OCS) and 66% biologics. Compared with the SCC group, the TRC group had more frequent comorbidities and lower blood eosinophil counts (262 versus 340 mm −3 ; p=0.0036). OCS and biologics use did not differ between groups, but patients in the TRC group benefited more frequently from an educational programme (26% versus 18%; p=0.0008) and received more frequently two or more sequential lines of biologics (33% versus 24%; p=0.0105). In-depth investigations were more frequently performed in the TRC group (allergy tests: 74% versus 62%; p<0.0001; exhaled nitric oxide fraction: 56% versus 21%; p<0.0001; induced sputum: 6% versus 3%; p=0.0390).Conclusions Phenotypes and care trajectories differed in the RAMSES cohort between SCCs and TRCs, probably related to different levels of asthma severity and differences in medical resources and practices among centres. This highlights the need for standardisation of severe asthma care

A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry

BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the TERAVOLT registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure and a tree-based model to screen and optimize a broad panel of demographics, clinical COVID-19 and cancer characteristics. RESULTS: As of April 15, 2021, 1491 consecutive evaluable patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened approximately 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection then identified seven major determinants of death ECOG-PS (OR 2.47 1.87-3.26), neutrophil count (OR 2.46 1.76-3.44), serum procalcitonin (OR 2.37 1.64-3.43), development of pneumonia (OR 1.95 1.48-2.58), c-reactive protein (CRP) (OR 1.90 1.43-2.51), tumor stage at COVID-19 diagnosis (OR 1.97 1.46-2.66) and age (OR 1.71 1.29-2.26). The ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 0.75 - 0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90% and the tree-based model recognized ECOG-PS, neutrophil count and CRP as the major determinants of prognosis. CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated the strongest association with poor outcome from COVID-19. With our analysis we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19

A definitive prognostication system for patients with thoracic malignancies diagnosed with COVID-19: an update from the TERAVOLT registry

Background: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far. Methods: Capitalizing data from the TERAVOLT registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure and a tree-based model to screen and optimize a broad panel of demographics, clinical COVID-19 and cancer characteristics. Results: As of April 15, 2021, 1491 consecutive evaluable patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened approximately 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection then identified seven major determinants of death ECOG-PS (OR 2.47 1.87-3.26), neutrophil count (OR 2.46 1.76-3.44), serum procalcitonin (OR 2.37 1.64-3.43), development of pneumonia (OR 1.95 1.48-2.58), c-reactive protein (CRP) (OR 1.90 1.43-2.51), tumor stage at COVID-19 diagnosis (OR 1.97 1.46-2.66) and age (OR 1.71 1.29-2.26). The ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 0.75 - 0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90% and the tree-based model recognized ECOG-PS, neutrophil count and CRP as the major determinants of prognosis. Conclusion: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated the strongest association with poor outcome from COVID-19. With our analysis we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19