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Background: Fewer than one in ten out-of-hospital cardiac arrest (OHCA) patients survive to hospital discharge in the UK. For prehospital teams to improve outcomes in patients who remain in refractory OHCA despite advanced life support (ALS); novel strategies that increase the likelihood of return of spontaneous circulation, whilst preserving cerebral circulation, should be investigated. Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) has been shown to improve coronary and cerebral perfusion during cardiopulmonary resuscitation. Early, prehospital initiation of REBOA may improve outcomes in patients who do not respond to standard ALS. However, there are significant clinical, technical, and logistical challenges with rapidly delivering prehospital REBOA in OHCA; and the feasibility of delivering this intervention in the UK urban–rural setting has not been evaluated. Methods: The Emergency Resuscitative Endovascular Balloon Occlusion of the Aorta in Out-of-Hospital Cardiac Arrest (ERICA-ARREST) study is a prospective, single-arm, interventional feasibility study. The trial will enrol 20 adult patients with non-traumatic OHCA. The primary objective is to assess the feasibility of performing Zone I (supra-coeliac) aortic occlusion in patients who remain in OHCA despite standard ALS in the UK prehospital setting. The trial’s secondary objectives are to describe the hemodynamic and physiological responses to aortic occlusion; to report key time intervals; and to document adverse events when performing REBOA in this context. Discussion: Using compressed geography, and targeted dispatch, alongside a well-established femoral arterial access programme, the ERICA-ARREST study will assess the feasibility of deploying REBOA in OHCA in a mixed UK urban and rural setting.Trial registration.ClinicalTrials.gov (NCT06071910), registration date October 10, 2023, https://classic.clinicaltrials.gov/ct2/show/NCT06071910

Abstract Background Accurate haemodynamic monitoring in the prehospital setting is essential. Non-invasive blood pressure measurement is susceptible to vibration and motion artefact, especially at extremes of hypotension and hypertension: invasive arterial blood pressure (IABP) monitoring is a potential solution. This study describes the largest series to date of cases of IABP monitoring being initiated prehospital. Methods This retrospective observational study was conducted at East Anglian Air Ambulance (EAAA), a UK helicopter emergency medical service (HEMS). It included all patients attended by EAAA who underwent arterial catheterisation and initiation of IABP monitoring between 1st February 2015 and 20th April 2023. The following data were retrieved for all patients: sex; age; aetiology (medical cardiac arrest, other medical emergency, trauma); site of arterial cannulation; operator role (doctor/paramedic); time of insertion and, where applicable, times of pre-hospital emergency anaesthesia, and return of spontaneous circulation following cardiac arrest. Descriptive analyses were performed to characterise the sample. Results 13,556 patients were attended: IABP monitoring was initiated in 1083 (8.0%) cases, with a median age 59 years, of which 70.8% were male. 546 cases were of medical cardiac arrest: in 22.4% of these IABP monitoring was initiated during cardiopulmonary resuscitation. 322 were trauma cases, and the remaining 215 were medical emergencies. The patients were critically unwell: 981 required intubation, of which 789 underwent prehospital emergency anaesthesia; 609 received vasoactive medication. In 424 cases IABP monitoring was instituted en route to hospital. Conclusion This study describes over 1000 cases of prehospital arterial catheterisation and IABP monitoring in a UK HEMS system and has demonstrated feasibility at scale. The high-fidelity of invasive arterial blood pressure monitoring with the additional benefit of arterial blood gas analysis presents an attractive translation of in-hospital critical care to the prehospital setting.

Abstract Background Selective aortic arch perfusion (SAAP) is a novel endovascular technique that combines thoracic aortic occlusion with extracorporeal perfusion of the brain and heart. SAAP may have a role in both haemorrhagic shock and in cardiac arrest due to coronary ischaemia. Despite promising animal studies, no data is available that describes SAAP in humans. The primary aim of this study was to assess the feasibility of selective aortic arch perfusion in humans. The secondary aim of the study was to assess the feasibility of achieving direct coronary artery access via the SAAP catheter as a potential conduit for salvage percutaneous coronary intervention. Methods Using perfused human cadavers, a prototype SAAP catheter was inserted into the descending aorta under fluoroscopic guidance via a standard femoral percutaneous access device. The catheter balloon was inflated and the aortic arch perfused with radio-opaque contrast. The coronary arteries were cannulated through the SAAP catheter. Results The procedure was conducted four times. During the first two trials the SAAP catheter was passed rapidly and without incident to the intended descending aortic landing zone and aortic arch perfusion was successfully delivered via the device. The SAAP catheter balloon failed on the third trial. On the fourth trial the left coronary system was cannulated using a 5Fr coronary guiding catheter through the central SAAP catheter lumen. Conclusions For the first time using a perfused cadaveric model we have demonstrated that a SAAP catheter can be easily and safely inserted and SAAP can be achieved using conventional endovascular techniques. The SAAP catheter allowed successful access to the proximal aorta and permitted retrograde perfusion of the coronary and cerebral circulation.

Summary: Background: Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods: Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Findings: Forty patients (32M:8F, age: 22–98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Interpretation: The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established. Funding: UK Research and Innovation/Medical Research Council through the UK Coronavirus Immunology Consortium, Barbour Foundation, General Sir John Monash Foundation, Newcastle University, JGW Patterson Foundation, Wellcome Trust.

Summary: Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows. Motivation: Across the biomedical sciences, there is an urgent need to move beyond qualitative imaging to quantitative, cell-based reporting of tissue microscopy data. Typically, cell segmentation requires fluorescent labeling of nucleus and cytoplasm, which limits the spectral bandwidth available for other reporter molecules. However, recent advances in deep-learning algorithms have transformed automated image classification, and this raises the possibility of proceeding with reduced image information. Here, we show that 2D and 3D cell segmentation of lymphoid tissues can be freely established from the reflected laser excitation light always present during routine confocal microscopy using entirely standard equipment.

Background: The aims of this study were to establish epidemiology, clinical management and outcomes in cases of adult out-of-hospital cardiac arrest complicated by hyperthermia attended by the London Ambulance Service NHS Trust between January 2018 and December 2019. Where evidence is available in relation to this sub-set of cardiac arrest patients it is generally limited to small case series and we therefore we sought to improve knowledge and target therapeutic interventions. Methods and results: Retrospective analysis of 253 cases was undertaken following abstraction from an established cardiac arrest database. Age ranged from 18-99 years with a median of 72 years (IQR 28) and 53.4% (n = 135) of patients were female. Overall thirty-day mortality was 94.5% (n = 239), with 48.2% (n = 122) of patients recognised life extinct in the out-of-hospital phase following termination of resuscitation. No significant differences in clinical characteristics stratified according to temperature group were identified. The presumed aetiology was infective in 62.8% (n = 159) of patients, and due to drug ingestion or heat illness in 7.5% (n = 19) and 2% (n = 5) respectively. In the remaining cases (27.7%, n = 70) it was not possible to determine the likely cause of the arrest. Conclusions: Previous research relating to cardiac arrest complicated by hyperthermia is limited to case reports and small case series, suggesting that the current study represents the most comprehensive analysis of this sub-group of out-of-hospital cardiac arrest patients currently available. Most cases were associated with evidence of infection compared with drug related aetiologies and heat illness. Where indicated, cooling was applied infrequently using inconsistent methods.

Out-of-hospital cardiac arrest (OHCA) affects 80,000 patients per year in the UK; despite improvements in care, survival to discharge remains lower than 10%. NHS England and several societies recommend all resuscitated OHCA patients be directly transferred to a cardiac arrest centre (CAC). However, evidence is limited that all patients benefit from transfer to a CAC, and there are significant organisational, logistic and financial implications associated with such change in policies. Furthermore, there is significant variability in interventional cardiovascular practices for OHCA. Accordingly, the British Cardiovascular Interventional Society established a multidisciplinary group to address variability in practice and provide recommendations for the development of cardiac networks. In this position statement, we recommend: the formal establishment of dedicated CACs; a pathway of conveyance to CACs; and interventional practice to standardise our approach. Further research is needed to understand the role of CACs and which interventions benefit patients with OHCA to support wide-scale changes in networks of care across the UK.

Out-of-hospital cardiac arrest (OHCA) is a major public health issue that poses significant challenges both in immediate management and long-term follow-up. Survivors of OHCA often experience a combination of complex medical, physical and psychological needs that have a significant impact on quality of life. Guidelines suggest a multi-dimensional follow-up to address both physical and non-physical domains for survivors. However, it is likely that there is substantial unwarranted variation in provision of services throughout the UK. Currently, there is no nationally agreed model for the follow-up of OHCA survivors and there is an urgent need for a set of standards and guidelines in order to ensure equal access for all. Accordingly, the British Cardiovascular Interventional Society established a multi-disciplinary working group to develop a position statement that summarises the most up-to-date evidence and provides guidance on essential and desirable services for a dedicated follow-up pathway for survivors of OHCA.

BackgroundOvarian cancer (OC) is amongst the most lethal of common cancers in women. Lacking in specific symptoms in the early stages, OC is predominantly diagnosed late when the disease has undergone metastatic spread and chemotherapy is relied on to prolong life. Platinum-based therapies are preferred and although many tumors respond initially, the emergence of platinum-resistance occurs in the majority of cases after which prognosis is very poor. Upregulation of DNA damage pathways is a common feature of platinum resistance in OC with cyclin dependent kinases (CDKs) serving as key regulators of this process and suggesting that CDK inhibitors (CDKis) could be effective tools in the treatment of platinum resistant and refractory OC.AimThe aim of this study was to evaluate the efficacy of CDKis in platinum resistant OC models and serve as a predictor of potential clinical utility.MethodsThe efficacy of CDKi, dinaciclib, was determined in wildtype and platinum resistant cell line pairs representing different OC subtypes. In addition, dinaciclib was evaluated in primary cells isolated from platinum-sensitive and platinum-refractory tumors to increase the clinical relevance of the study.Results and conclusionsDinaciclib proved highly efficacious in OC cell lines and primary cells, which were over a thousand-fold more sensitive to the CDKi than to cisplatin. Furthermore, cisplatin resistance in these cells did not influence sensitivity to dinaciclib and the two drugs combined additively in both platinum-sensitive and platinum-resistant OC cells suggesting a potential role for pan-CDKis (CDKis targeting multiple CDKs), such as dinaciclib, in the treatment of advanced and platinum-resistant OC.

Nagy et al. describe the histomorphogenesis of ovine mammary development in pre- and peri-pubertal sheep as a candidate model for human breast development. They investigate the locale and abundance of several immune cell types at different ages, highlighting this as a valuable model system that can provide new insights into postnatal breast development.

Abstract Background Physical activity, sedentary time and sleep have been shown to be associated with cardio-metabolic health. However, these associations are typically studied in isolation or without accounting for the effect of all movement behaviours and the constrained nature of data that comprise a finite whole such as a 24 h day. The aim of this study was to examine the associations between the composition of daily movement behaviours (including sleep, sedentary time (ST), light intensity physical activity (LIPA) and moderate-to-vigorous activity (MVPA)) and cardio-metabolic health, in a cross-sectional analysis of adults with pre-diabetes. Further, we quantified the predicted differences following reallocation of time between behaviours. Methods Accelerometers were used to quantify daily movement behaviours in 1462 adults from eight countries with a body mass index (BMI) ≥25 kg·m− 2, impaired fasting glucose (IFG; 5.6–6.9 mmol·l− 1) and/or impaired glucose tolerance (IGT; 7.8–11.0 mmol•l− 1 2 h following oral glucose tolerance test, OGTT). Compositional isotemporal substitution was used to estimate the association of reallocating time between behaviours. Results Replacing MVPA with any other behaviour around the mean composition was associated with a poorer cardio-metabolic risk profile. Conversely, when MVPA was increased, the relationships with cardiometabolic risk markers was favourable but with smaller predicted changes than when MVPA was replaced. Further, substituting ST with LIPA predicted improvements in cardio-metabolic risk markers, most notably insulin and HOMA-IR. Conclusions This is the first study to use compositional analysis of the 24 h movement composition in adults with overweight/obesity and pre-diabetes. These findings build on previous literature that suggest replacing ST with LIPA may produce metabolic benefits that contribute to the prevention and management of type 2 diabetes. Furthermore, the asymmetry in the predicted change in risk markers following the reallocation of time to/from MVPA highlights the importance of maintaining existing levels of MVPA. Trial registration ClinicalTrials.gov ( NCT01777893 ).

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Cellular uptake of nanoparticles is highly variable between individual cells in a population. Here, the authors show that this heterogeneity is a result of varying numbers of nanoparticle-containing endosomes while the nanoparticle dose per endosome remains constant.

Maintenance of cell size homeostasis is a property that is conserved throughout eukaryotes. Cell size homeostasis is brought about by the co-ordination of cell division with cell growth and requires restriction of smaller cells from undergoing mitosis and cell division, whilst allowing larger cells to do so. Cyclin-CDK is the fundamental driver of mitosis and therefore ultimately ensures size homeostasis. Here we dissect determinants of CDK activity in vivo to investigate how cell size information is processed by the cell cycle network in fission yeast. We develop a high-throughput single-cell assay system of CDK activity in vivo and show that inhibitory tyrosine phosphorylation of CDK encodes cell size information, with the phosphatase PP2A aiding to set a size threshold for division. CDK inhibitory phosphorylation works synergistically with PP2A to prevent mitosis in smaller cells. Finally, we find that diploid cells of equivalent size to haploid cells exhibit lower CDK activity in response to equal cyclin-CDK enzyme concentrations, suggesting that CDK activity is reduced by increased DNA levels. Therefore, scaling of cyclin-CDK levels with cell size, CDK inhibitory phosphorylation, PP2A, and DNA-dependent inhibition of CDK activity, all inform the cell cycle network of cell size, thus contributing to cell size homeostasis.

Nanoparticle drug delivery vehicles introduce multiple pharmacokinetic processes, with the delivery, accumulation, and stability of the therapeutic molecule influenced by nanoscale processes. Therefore, considering the complexity of the multiple interactions, the use of data-driven models has critical importance in understanding the interplay between controlling processes. We demonstrate data simulation techniques to reproduce the time-dependent dose of trimethyl chitosan nanoparticles in an ND7/23 neuronal cell line, used as an in vitro model of native peripheral sensory neurons. Derived analytical expressions of the mean dose per cell accurately capture the pharmacokinetics by including a declining delivery rate and an intracellular particle degradation process. Comparison with experiment indicates a supply time constant, τ = 2 h. and a degradation rate constant, b = 0.71 h−1. Modeling the dose heterogeneity uses simulated data distributions, with time dependence incorporated by transforming data-bin values. The simulations mimic the dynamic nature of cell-to-cell dose variation and explain the observed trend of increasing numbers of high-dose cells at early time points, followed by a shift in distribution peak to lower dose between 4 to 8 h and a static dose profile beyond 8 h.

The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al.demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.

Analysis of the thickness dependence of the potential profile of the metal-ZnO-metal (MZM) structure has been conducted based on Poisson’s equation and Schottky theory. Quantum scattering theory is then used to calculate the transmission probability of an electron passing through the MZM structure. Results show that the quantum resonance (QR) effect becomes pronounced when the thickness of the ZnO film reaches to around 6 nm. Strain induced piezopotentials are considered as biases to the MZM, which significantly changes the QR according to the analysis. This effect can be potentially employed as nanoscale strain sensors.

Herein, we present a novel imaging platform to study the biological effects of non-invasive radiofrequency (RF) electric field cancer hyperthermia. This system allows for real-time in vivo intravital microscopy (IVM) imaging of radiofrequency-induced biological alterations such as changes in vessel structure and drug perfusion. Our results indicate that the IVM system is able to handle exposure to high-power electric-fields without inducing significant hardware damage or imaging artifacts. Furthermore, short durations of low-power (< 200 W) radiofrequency exposure increased transport and perfusion of fluorescent tracers into the tumors at temperatures below 41°C. Vessel deformations and blood coagulation were seen for tumor temperatures around 44°C. These results highlight the use of our integrated IVM-RF imaging platform as a powerful new tool to visualize the dynamics and interplay between radiofrequency energy and biological tissues, organs, and tumors.

An automated technique for the identification, tracking and analysis of biological cells is presented. It is based on the use of nanoparticles, enclosed within intra-cellular vesicles, to produce clusters of discrete, point-like fluorescent, light sources within the cells. Computational analysis of these light ensembles in successive time frames of a movie sequence, using k-means clustering and particle tracking algorithms, provides robust and automated discrimination of live cells and their motion and a quantitative measure of their proliferation. This approach is a cytometric version of the moving light display technique which is widely used for analyzing the biological motion of humans and animals. We use the endocytosis of CdTe/ZnS, core-shell quantum dots to produce the light displays within an A549, epithelial, lung cancer cell line, using time-lapse imaging with frame acquisition every 5 minutes over a 40 hour time period. The nanoparticle moving light displays provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships allowing construction of multi-parameter lineage trees.

We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. Here we link the "real" data to initialise a computer simulation of the cell cycle that mimics the evolution of individual cells within a larger population and simulates the associated changes in fluorescence intensity of functional reporters. The model is based on stochastic formulations of cell cycle progression and cell division and uses evolutionary algorithms, allied to further experimental data sets, to optimise the system variables. At the population level, the in-silico cells provide the same statistical distributions of fluorescence as their real counterparts; in addition the model maintains information at the single cell level. The cell model is demonstrated in the analysis of cell cycle perturbation in human osteosarcoma tumour cells, using the topoisomerase II inhibitor, ICRF-193. The simulation gives a continuous temporal description of the pharmacodynamics between discrete experimental analysis points with a 24 hour interval; providing quantitative assessment of inter-mitotic time variation, drug interaction time constants and sub-population fractions within normal and polyploid cell cycles. Repeated simulations indicate a model accuracy of +/-5%. The development of a simulated cell model, initialized and calibrated by reference to experimental data, provides an analysis tool in which biological knowledge can be obtained directly via interrogation of the in-silico cell population. It is envisaged that this approach to the study of cell biology by simulating a virtual cell population pertinent to the data available can be applied to "generic" cell-based outputs including experimental data from imaging platforms.