The SEE-IT Trial: emergency medical services Streaming Enabled Evaluation In Trauma: a feasibility randomised controlled trial

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/BACKGROUND: Use of bystander video livestreaming from scene to Emergency Medical Services (EMS) is becoming increasingly common to aid decision making about the resources required. Possible benefits include earlier, more appropriate dispatch and clinical and financial gains, but evidence is sparse. METHODS: A feasibility randomised controlled trial with an embedded process evaluation and exploratory economic evaluation where working shifts during six trial weeks were randomised 1:1 to use video livestreaming during eligible trauma incidents (using GoodSAM Instant-On-Scene) or standard care only. Pre-defined progression criteria were: (1) ≥ 70% callers (bystanders) with smartphones agreeing and able to activate live stream; (2) ≥ 50% requests to activate resulting in footage being viewed; (3) Helicopter Emergency Medical Services (HEMS) stand-down rate reducing by ≥ 10% as a result of live footage; (4) no evidence of psychological harm in callers or staff/dispatchers. Observational sub-studies included (i) an inner-city EMS who routinely use video livestreaming to explore acceptability in a diverse population; and (ii) staff wellbeing in an EMS not using video livestreaming for comparison to the trial site. RESULTS: Sixty-two shifts were randomised, including 240 incidents (132 control; 108 intervention). Livestreaming was successful in 53 incidents in the intervention arm. Patient recruitment (to determine appropriateness of dispatch), and caller recruitment (to measure potential harm) were low (58/269, 22% of patients; 4/244, 2% of callers). Two progression criteria were met: (1) 86% of callers with smartphones agreed and were able to activate livestreaming; (2) 85% of requests to activate livestreaming resulted in footage being obtained; and two were indeterminate due to insufficient data: (3) 2/6 (33%) HEMS stand down due to livestreaming; (4) no evidence of psychological harm from survey, observations or interviews, but insufficient survey data from callers or comparison EMS site to be confident. Language barriers and older age were reported in interviews as potential challenges to video livestreaming by dispatchers in the inner-city EMS. CONCLUSIONS: Progression to a definitive RCT is supported by these findings. Bystander video livestreaming from scene is feasible to implement, acceptable to both 999 callers and dispatchers, and may aid dispatch decision-making. Further assessment of unintended consequences, benefits and harm is required. TRIAL REGISTRATION: ISRCTN 11449333 (22 March 2022). https://www.isrctn.com/ISRCTN11449333.Peer reviewe

Unique Transcriptional Profile of Sustained Ligand-Activated Preconditioning in Pre- and Post-Ischemic Myocardium

BACKGROUND: Opioidergic SLP (sustained ligand-activated preconditioning) induced by 3–5 days of opioid receptor (OR) agonism induces persistent protection against ischemia-reperfusion (I-R) injury in young and aged hearts, and is mechanistically distinct from conventional preconditioning responses. We thus applied unbiased gene-array interrogation to identify molecular effects of SLP in pre- and post-ischemic myocardium. METHODOLOGY/PRINCIPAL FINDINGS: Male C57Bl/6 mice were implanted with 75 mg morphine or placebo pellets for 5 days. Resultant SLP did not modify cardiac function, and markedly reduced dysfunction and injury in perfused hearts subjected to 25 min ischemia/45 min reperfusion. Microarray analysis identified 14 up- and 86 down-regulated genes in normoxic hearts from SLP mice (≥1.3-fold change, FDR≤5%). Induced genes encoded sarcomeric/contractile proteins (Myh7, Mybpc3,Myom2,Des), natriuretic peptides (Nppa,Nppb) and stress-signaling elements (Csda,Ptgds). Highly repressed genes primarily encoded chemokines (Ccl2,Ccl4,Ccl7,Ccl9,Ccl13,Ccl3l3,Cxcl3), cytokines (Il1b,Il6,Tnf) and other proteins involved in inflammation/immunity (C3,Cd74,Cd83, Cd86,Hla-dbq1,Hla-drb1,Saa1,Selp,Serpina3), together with endoplasmic stress proteins (known: Dnajb1,Herpud1,Socs3; putative: Il6, Gadd45g,Rcan1) and transcriptional controllers (Egr2,Egr3, Fos,Hmox1,Nfkbid). Biological themes modified thus related to inflammation/immunity, together with cellular/cardiovascular movement and development. SLP also modified the transcriptional response to I-R (46 genes uniquely altered post-ischemia), which may influence later infarction/remodeling. This included up-regulated determinants of cellular resistance to oxidant (Mgst3,Gstm1,Gstm2) and other forms of stress (Xirp1,Ankrd1,Clu), and repression of stress-response genes (Hspa1a,Hspd1,Hsp90aa,Hsph1,Serpinh1) and Txnip. CONCLUSIONS: Protection via SLP is associated with transcriptional repression of inflammation/immunity, up-regulation of sarcomeric elements and natriuretic peptides, and modulation of cell stress, growth and development, while conventional protective molecules are unaltered

Modelling of a Two-Signal SFQ Detection Scheme for the Readout of Superconducting Nanowire Single Photon Detectors

We present a two-signal single flux quantum (SFQ) detection scheme for the purpose of reading out two pixels of a superconducting nanowire single photon detector (SNSPD). The circuit is based on a coincidence buffer element which is able to output a signal when both of its input lines are triggered. The circuit model for the SNSPD element is simulated in SPICE and optimized to match the experimental SNSPD response data. The two-signal detection scheme is simulated using JSIM which allows for the simulation of Josephson junction elements in a circuit. We demonstrate a model of the two-signal circuit operating with two simulated SNSPD pixel inputs and investigate the response of the scheme when a phase shift is applied to one of the inputs. The scheme shows potential as a useful coincidence detector of single photons. We also present preliminary experimental results of nanobridge-based Josephson junctions to be used in the realization of the coincidence detector circuit. Evidence of the nanobridges exhibiting Josephson behavior (SQUID modulation) are presented

Assessing the Efficacy of Nano- and Micro-Sized Magnetic Particles as Contrast Agents for MRI Cell Tracking

Iron-oxide based contrast agents play an important role in magnetic resonance imaging (MRI) of labelled cells in vivo. Currently, a wide range of such contrast agents is available with sizes varying from several nanometers up to a few micrometers and consisting of single or multiple magnetic cores. Here, we evaluate the effectiveness of these different particles for labelling and imaging stem cells, using a mouse mesenchymal stem cell line to investigate intracellular uptake, retention and processing of nano- and microsized contrast agents. The effect of intracellular confinement on transverse relaxivity was measured by MRI at 7 T and in compliance with the principles of the ‘3Rs’, the suitability of the contrast agents for MR-based cell tracking in vivo was tested using a chick embryo model. We show that for all particles tested, relaxivity was markedly reduced following cellular internalisation, indicating that contrast agent relaxivity in colloidal suspension does not accurately predict performance in MR-based cell tracking studies. Using a bimodal imaging approach comprising fluorescence and MRI, we demonstrate that labelled MSC remain viable following in vivo transplantation and can be tracked effectively using MRI. Importantly, our data suggest that larger particles might confer advantages for longer-term imaging

Novel deep learning approach to model and predict the spread of COVID-19

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM)

The Grounds and Extent of Legal Responsibility

To question that is the title of this symposium, What Do Compensatory Damages Compensate?, requires consideration of the basic grounds and purposes of legal responsibility. The question is usefully brought into sharper focus by the specific questions and puzzles posed to the contributors to stimulate thought and discussion