Improvement of peripheral artery disease with Sildenafil and Bosentan combined therapy in a patient with limited cutaneous systemic sclerosis A case report

International audienceRationale: Sildenafil, a phosphodiesterase-5-inhibitor and Bosentan, an endothelin-1-receptor antagonist combined therapy could have beneficial effect in systemic sclerosis (SSc) patients with peripheral artery disease. Patient concerns: We report a case of a 48-year-old Black woman, who developed severe left limb claudication and walking limitation following a left femoropopliteal bypass occlusion in 2014. She was a heavy smoker and had a history of right middle cerebral artery ischemic stroke and bilateral Raynaud phenomenon. Diagnoses: According to the American College of Rheumatology/European League Against Rheumatism-2013 criteria, diagnosis of limited cutaneous SSc was retained with macrovascular lesions. She was referred for investigation of left limb claudication on treadmill using transcutaneous oxygen pressure measurement during exercise to argue for the vascular origin of the walking impairment. She had a severe left limb ischemia and the maximum walking distance (MWD) she reached was 118 m in March 2015 despite the medical optimal treatment and walking rehabilitation. Interventions: Sildenafil, 20mg tid, was introduced due to active digital ulcers. In July 2015, the MWD increased to 288 m, then to 452 m in December 2015. Adding Bosentan to Sildenafil to prevent recurrent digital ulcers resulted in an MWD of 1576 m. Outcomes: Recently, the patient is treated with the combined therapy. She has no more pain during walking and his quality of life has improved. Lessons: Sildenafil and Bosentan combined therapy was associated in our case with an improvement of MWD without adverse effect. Further clinical trials are necessary to confirm our original observation

A Comprehensive Benchmark of Neural Networks for System Identification

This paper compares a wide variety of neural network architectures applied in the context of black-box modeling for robotics and control. We compare six different architectural concepts and four activation functions, with over three hundred different models. Those models were applied to three robotics datasets to show the differences in performance between the architectures along with their limitations

Importance Sampling for Deep System Identification

International audienceThis paper revisit the methodology of system identification and shows how new paradigms from machine learning can be used to improve the model identification performance in the case of non-linear systems observed with noisy and unbalanced dataset. We prove that using importance sampling schemes in system identification can provide significant performance boost on a wide variety of systems, in particular when some of the system dynamic is only exhibited by relatively rare events. The performance of the approaches is evaluated on a real and simulated drone and two standard datasets from real robotic systems. Our approach consistently outperforms baseline approaches on these datasets, all the more when the datasets are noisy and unbalanced

Evaluation of prioritized deep system identification on a path following task

International audienceThis paper revisits system identification and shows how new paradigms from machine learning can be used to improve it in the case of non-linear systems modeling from noisy and unbalanced dataset. We show that using importance sampling schemes in system identification can provide a significant performance boost in modeling, which is helpful to a predictive controller. The performance of the approach is first evaluated on simulated data of a Unmanned Surface Vehicle (USV). Our approach consistently outperforms baseline approaches on this dataset. Moreover we demonstrate the benefits of this identification methodology in a control setting. We use the model of the Unmanned Surface Vehicle (USV) in a Model Predictive Path Integral (MPPI) controller to perform a track following task. We discuss the influence of the controller parameters and show that the prioritized model outperform standard methods. Finally, we apply the Model Predictive Path Integral (MPPI) on a real system using the know-how developed here

Potential impact of introducing vaccines against COVID-19 under supply and uptake constraints in France: A modelling study.

BackgroundThe accelerated vaccine development in response to the COVID-19 pandemic should lead to a vaccine being available early 2021, albeit in limited supply and possibly without full vaccine acceptance. We assessed the short-term impact of a COVID-19 immunization program with varying constraints on population health and non-pharmaceutical interventions (NPIs) needs.MethodsA SARS-CoV-2 transmission model was calibrated to French epidemiological data. We defined several vaccine implementation scenarios starting in January 2021 based on timing of discontinuation of NPIs, supply and uptake constraints, and their relaxation. We assessed the number of COVID-19 hospitalizations averted, the need for and number of days with NPIs in place over the 2021-2022 period.ResultsAn immunisation program under constraints could reduce the burden of COVID-19 hospitalizations by 9-40% if the vaccine prevents against infections. Relaxation of constraints not only reduces further COVID-19 hospitalizations (30-39% incremental reduction), it also allows for NPIs to be discontinued post-2021 (0 days with NPIs in 2022 versus 11 to 125 days for vaccination programs under constraints and 327 in the absence of vaccination).ConclusionFor 2021, COVID-19 control is expected to rely on a combination of NPIs and the outcome of early immunisation programs. The ability to overcome supply and uptake constraints will help prevent the need for further NPIs post-2021. As the programs expand, efficiency assessments will be needed to ensure optimisation of control policies post-emergency use

Maximum acceleration and acceleration time as markers to assess severity of peripheral artery disease

International audienc

A Bridge over a Hamiltonian Path

status: publishe

Screening for cervical cancer in India: How much will it cost? A trial based analysis of the cost per case detected.

The cost and cost effectiveness of screening previously unscreened women by VIA, cytology or HPV testing was investigated within a large cluster randomised trial involving 131,178 women in rural India. All resources involved in implementation, training, management, recruitment, screening and diagnosis were identified and costed. We estimated the total costs and detection rates for each cluster and used these data to calculate an average cluster cost and detection rate for each screening approach. These estimates were combined to estimate a cost per case of cervical intraepithelial neoplasia grade 2/3 or invasive cancer (CIN 2/3+) detected. The average total costs per 1,000 women eligible for screening were US dollar 3,917, US dollar 6,609 and US dollar 11,779 with VIA, cytology and HPV respectively. The cost of detecting a case of CIN2/3+ using VIA was dollar 522 (95% CI dollar 429- dollar 652). Our results suggest that more CIN2/3+ cases would be detected in the same population if cytology were used instead of VIA and each additional case would cost US dollar 1065 (95% CI dollar 713- dollar2175). Delivering cervical cancer screening is potentially expensive in a low-income country although costs might be lower outside a trial setting. We found screening with VIA to be the least expensive option, but it also detected fewer cases of CIN2/3+ than other methods; its long-term cost-effectiveness will depend on the long-term benefits of early detection. Cytology was more effective at detecting cases than VIA but was also more expensive. Our findings indicate that HPV may not be a cost effective screening strategy in India at current consumable prices