Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approacires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. NVP-ADW742 Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.Introduction Systemic Lupus Erythematosus (SLE) is a chronic B cell-mediated autoimmune disease which can potentially involve several organs and systems. The development of SLE is associated with a complexity of genetic, hormonal and environmental factors leading to immune deregulation and production of autoantibodies. Therefore, novel therapies have focused on B cells as key effectors of SLE pathogenesis. Belimumab is a fully humanized monoclonal antibody that antagonizes B-lymphocyte stimulator (BLyS); it is the first and the only biological drug approved for SLE in over 50 years.Areas covered In this review we discuss the pharmacological properties of belimumab, new recommendations for its use in clinical practice and its evidence of efficacy and safety based on clinical trial and real-life data.Expert opinion Efficacy and safety of belimumab in clinical practice have been well established. To date, it is known that early introduction of belimumab in SLE can maximize the efficacy of the drug. A number of questions are still open, such as the timing of belimumab discontinuation and its possible association with other biological drugs, which need to be assessed in future studies.

Restarting planned surgery during the COVID-19 pandemic is a clinical and societal priority, but it is unknown whether it can be done safely and include high-risk or complex cases. We developed a Surgical Prioritization and Allocation Guide (SPAG). Here, we validate its effectiveness and safety in COVID-free sites.

A multidisciplinary surgical prioritization committee developed the SPAG, incorporating procedural urgency, shared decision-making, patient safety, and biopsychosocial factors; and applied it to 1,142 adult patients awaiting orthopaedic surgery. Patients were stratified into four priority groups and underwent surgery at three COVID-free sites, including one with access to a high dependency unit (HDU) or intensive care unit (ICU) and specialist resources. Safety was assessed by the number of patients requiring inpatient postoperative HDU/ICU admission, contracting COVID-19 within 14 days postoperatively, and mortality within 30 days postoperatively.

A total of 1,142 patients were included, 47 k. This model, which includes assessment of biopsychosocial factors alongside disease severity, can assist in equitably prioritizing the substantial list of patients now awaiting planned orthopaedic surgery worldwide. Cite this article

2021;2(2)134-140.

Our widely generalizable model enabled the restart of planned surgery during the COVID-19 pandemic, without compromising patient safety or excluding high-risk or complex cases. Patients classified as Urgent or P2 were most likely to undergo surgery, including those deemed high-risk. This model, which includes assessment of biopsychosocial factors alongside disease severity, can assist in equitably prioritizing the substantial list of patients now awaiting planned orthopaedic surgery worldwide. Cite this article Bone Jt Open 2021;2(2)134-140.

To investigate the effects of listening demands and motivation on listening effort (LE) in a novel speech recognition task.

We manipulated listening demands and motivation using vocoded speech and financial reward, respectively, and measured task performance (correct response rate) and indices of LE (response times (RTs), subjective ratings of LE and likelihood of giving up). Effects of inter-individual differences in cognitive skills and personality on task performance and LE were also assessed within the context of the Cognitive Energetics Theory (CET).

Twenty-four participants with normal-hearing (age range 19 - 33 years, 6 male).

High listening demands decreased the correct response rate and increased RTs, self-rated LE and self-rated likelihood of giving up. High financial reward increased subjective LE ratings only. Mixed-effects modelling showed small fixed effects for competitiveness on LE measured using RTs. Small fixed effects were found for cognitive skills (lexical decision RTs and backwards digit span) on LE measured using RTs and correct response rate, respectively.

The effects of listening demands on LE in the speech recognition task aligned with CET, whereas predictions regarding the influence of motivation, cognitive skills and personality were only partially supported.

The effects of listening demands on LE in the speech recognition task aligned with CET, whereas predictions regarding the influence of motivation, cognitive skills and personality were only partially supported.