ABSTRACT
Background: Patients with systemic lupus erythematosus (SLE) are at an increased risk of infection relative to the general population. We aimed to describe the frequency and risk factors for serious infections in patients with moderate-to-severe SLE treated with rituximab, belimumab, and standard of care therapies in a large national observational cohort. Method(s): The British Isles Lupus Assessment Group Biologics Register (BILAG-BR) is a UK-based prospective register of patients with SLE. Patients were recruited by their treating physician as part of their scheduled care from 64 centres across the UK by use of a standardised case report form. Inclusion criteria for the BILAG-BR included age older than 5 years, ability to provide informed consent, a diagnosis of SLE, and starting a new biological therapy within the last 12 months or a new standard of care drug within the last month. The primary outcome for this study was the rate of serious infections within the first 12 months of therapy. Serious infections were defined as those requiring intravenous antibiotic treatment, hospital admission, or resulting in morbidity or death. Infection and mortality data were collected from study centres and further mortality data were collected from the UK Office for National Statistics. The relationship between serious infection and drug type was analysed using a multiple-failure Cox proportional hazards model. Finding(s): Between July 1, 2010, and Feb 23, 2021, 1383 individuals were recruited to the BILAG-BR. 335 patients were excluded from this analysis. The remaining 1048 participants contributed 1002.7 person-years of follow-up and included 746 (71%) participants on rituximab, 119 (11%) participants on belimumab, and 183 (17%) participants on standard of care. The median age of the cohort was 39 years (IQR 30-50), 942 (90%) of 1048 patients were women and 106 (10%) were men. Of the patients with available ethnicity data, 514 (56%) of 911 were White, 169 (19%) were Asian, 161 (18%) were Black, and 67 (7%) were of multiple-mixed or other ethnic backgrounds. 118 serious infections occurred in 76 individuals during the 12-month study period, which included 92 serious infections in 58 individuals on rituximab, eight serious infections in five individuals receiving belimumab, and 18 serious infections in 13 individuals on standard of care. The overall crude incidence rate of serious infection was 117.7 (95% CI 98.3-141.0) per 1000 person-years. Compared with standard of care, the serious infection risk was similar in the rituximab (adjusted hazard ratio [HR] 1.68 [0.60-4.68]) and belimumab groups (1.01 [0.21-4.80]). Across the whole cohort in multivariate analysis, serious infection risk was associated with prednisolone dose (>10 mg;2.38 [95%CI 1.47-3.84]), hypogammaglobulinaemia (<6 g/L;2.16 [1.38-3.37]), and multimorbidity (1.45 [1.17-1.80]). Additional concomitant immunosuppressive use appeared to be associated with a reduced risk (0.60 [0.41-0.90]). We found no significant safety signals regarding atypical infections. Six infection-related deaths occurred at a median of 121 days (IQR 60-151) days from cohort entry. Interpretation(s): In patients with moderate-to-severe SLE, rituximab, belimumab, and standard immunosuppressive therapy have similar serious infection risks. Key risk factors for serious infections included multimorbidity, hypogammaglobulinaemia, and increased glucocorticoid doses. When considering the risk of serious infection, we propose that immunosupppressives, rituximab, and belimumab should be prioritised as mainstay therapies to optimise SLE management and support proactive minimisation of glucocorticoid use. Funding(s): None.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
ABSTRACT
In April 2020, by the start of isolation all around the world to counter the spread of COVID-19, an increase in violence against women and kids has been observed such that it has been named The Shadow Pandemic. To fight against this phenomenon, a Canadian foundation proposed the "Signal for Help” gesture to help people in danger to alert others of being in danger, discreetly. Soon, this gesture became famous among people all around the world, and even after COVID-19 isolation, it has been used in public places to alert them of being in danger and abused. However, the problem is that the signal works if people recognize it and know what it means. To address this challenge, we present a workflow for real-time detection of "Signal for Help” based on two lightweight CNN architectures, dedicated to hand palm detection and hand gesture classification, respectively. Moreover, due to the lack of a "Signal for Help” dataset, we create the first video dataset representing the "Signal for Help” hand gesture for detection and classification applications which includes 200 videos. While the hand-detection task is based on a pre-trained network, the classifying network is trained using the publicly available Jesture dataset, including 27 classes, and fine-tuned with the "Signal for Help” dataset through transfer learning. The proposed platform shows an accuracy of 91.25% with a video processing capability of 16 fps executed on a machine with an Intel i9-9900K@3.6 GHz CPU, 31.2 GB memory, and NVIDIA GeForce RTX 2080 Ti GPU, while it reaches 6 fps when running on Jetson Nano NVIDIA developer kit as an embedded platform. The high performance and small model size of the proposed approach ensure great suitability for resource-limited devices and embedded applications which has been confirmed by implementing the developed framework on the Jetson Nano Developer Kit. A comparison between the developed framework and the state-of-the-art hand detection and classification models shows a negligible reduction in the validation accuracy, around 3%, while the proposed model required 4 times fewer resources for implementation, and inference has a speedup of about 50% on Jetson Nano platform, which make it highly suitable for embedded systems. The developed platform as well as the created dataset are publicly available. © 2022