Adrenal function recovery after durable oral corticosteroid sparing with benralizumab in the PONENTE study

Background Oral corticosteroid (OCS) dependence among patients with severe eosinophilic asthma can cause adverse outcomes, including adrenal insufficiency. PONENTE's OCS reduction phase showed that, following benralizumab initiation, 91.5% of patients eliminated corticosteroids or achieved a final dosage ≤5 mg·day-1 (median (range) 0.0 (0.0-40.0) mg). Methods The maintenance phase assessed the durability of corticosteroid reduction and further adrenal function recovery. For ~6 months, patients continued benralizumab 30 mg every 8 weeks without corticosteroids or with the final dosage achieved during the reduction phase. Investigators could prescribe corticosteroids for asthma exacerbations or increase daily dosages for asthma control deteriorations. Outcomes included changes in daily OCS dosage, Asthma Control Questionnaire (ACQ)-6 and St George's Respiratory Questionnaire (SGRQ), as well as adrenal status, asthma exacerbations and adverse events. Results 598 patients entered PONENTE; 563 (94.1%) completed the reduction phase and entered the maintenance phase. From the end of reduction to the end of maintenance, the median (range) OCS dosage was unchanged (0.0 (0.0-40.0) mg), 3.2% (n=18/563) of patients experienced daily dosage increases, the mean ACQ-6 score decreased from 1.26 to 1.18 and 84.5% (n=476/563) of patients were exacerbation free. The mean SGRQ improvement (-19.65 points) from baseline to the end of maintenance indicated substantial quality-of-life improvements. Of patients entering the maintenance phase with adrenal insufficiency, 32.4% (n=104/321) demonstrated an improvement in adrenal function. Adverse events were consistent with previous reports. Conclusions Most patients successfully maintained maximal OCS reduction while achieving improved asthma control with few exacerbations and maintaining or recovering adrenal function

Gene network inference via sparse structural equation modeling with genetic perturbations

Structural equation models (SEMs) have been recently proposed to infer gene regulatory network using gene expression data and genetic perturbations. However, lack of efficient inference method for SEMs prevents practical use of SEMs in the inference of relatively large gene networks. In this paper, relying on the sparsity of gene networks, we develop an efficient SEM-based method for inferring gene networks using both gene expression and expression quantitative trait locus (eQTL) data. Simulated tests demonstrate that the novel method significantly outperform state-of-the-art methods in the field

Sensing time and power allocation for cognitive radios using distributed Q-learning

In cognitive radios systems, the sparse assigned frequency bands are opened to secondary users, provided that the aggregated interferences induced by the secondary transmitters on the primary receivers are negligible. Cognitive radios are established in two steps: the radios firstly sense the available frequency bands and secondly communicate using these bands. In this article, we propose two decentralized resource allocation Q-learning algorithms: the first one is used to share the sensing time among the cognitive radios in a way that maximize the throughputs of the radios. The second one is used to allocate the cognitive radio powers in a way that maximizes the signal on interference-plus-noise ratio (SINR) at the secondary receivers while meeting the primary protection constraint. Numerical results show the convergence of the proposed algorithms and allow the discussion of the exploration strategy, the choice of the cost function and the frequency of execution of each algorithm.info:eu-repo/semantics/publishe