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Background Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population.Methods The study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period. Multivariable logistic regression and Cox regression models, incorporating bootstrap and multiple imputation techniques, were used to identify predictors and develop models for key clinical outcomes: 3-month poor functional status; 30-day, 1-year and 3-year mortality; survival time.Results A total of 978 patients were included in the analyses. Predictors associated with one or more poor outcomes include: older age (ORs for every 5-year increase: 1.22–1.40), higher premorbid functional modified Rankin Scale (ORs: 1.31–1.75), higher baseline National Institutes of Health Stroke Scale (ORs: 1.05–1.07) score, higher blood glucose (ORs: 1.08–1.19), larger core volume (ORs for every 10 mL increase: 1.10–1.22), pre-EVT thrombolytic therapy (ORs: 0.44–0.56), history of heart failure (outcome: 30-day mortality, OR=1.87), interhospital transfer (ORs: 1.42 to 1.53), non-rural/regional stroke onset (outcome: functional dependency, OR=0.64), longer onset-to-groin puncture time (outcome: 3-year mortality, OR=1.08) and atherosclerosis-caused stroke (outcome: functional dependency, OR=1.68). The models using these predictors demonstrated moderate predictive abilities (area under the receiver operating characteristic curve range: 0.752–0.796).Conclusion Our models using real-world predictors assessed at hospital admission showed satisfactory performance in predicting poor functional outcomes and short-term and long-term mortality for patients with successful reperfusion following EVT. These can be used to inform EVT treatment provision and consent.

Abstract The transcription factor Six2 plays a crucial role in maintaining self-renewing nephron progenitor cap mesenchyme (CM) during metanephric kidney development. In mouse and human, expression at single-cell resolution has detected Six2 in cells as they leave the CM pool and differentiate. The role Six2 may play in these cells as they differentiate remains unknown. Here, we took advantage of the zebrafish pronephric kidney which forms directly from intermediate mesoderm to test six2b function during pronephric tubule development and differentiation. Expression of six2b during early zebrafish development was consistent with a role in pronephros formation. Using morpholino knock-down and CRISPR/Cas9 mutagenesis, we show a functional role for six2b in the development of proximal elements of the pronephros. By 48 h post-fertilization, six2b morphants and mutants showed disrupted pronephric tubule morphogenesis. We observed a lower-than-expected frequency of phenotypes in six2b stable genetic mutants suggesting compensation. Supporting this, we detected increased expression of six2a in six2b stable mutant embryos. To further confirm six2b function, F0 crispant embryos were analyzed and displayed similar phenotypes as morphants and stable mutants. Together our data suggests a conserved role for Six2 during nephrogenesis and a role in the morphogenesis of the proximal tubule.

Oncolytic viruses, modified for tumor-restricted infection, are a promising cancer immunotherapeutic, yet much remains to be understood about factors driving their activity and outcome in the tumor microenvironment. Here, we report that oncolytic herpes simplex virus C134, previously found to exert T cell-dependent efficacy in mouse models of glioblastoma, exerts T cell-independent efficacy in mouse models of medulloblastoma, indicating this oncolytic virus uses different mechanisms in different tumors. We investigated C134’s behavior in mouse medulloblastomas, using single cell RNA sequencing to map C134-induced gene expression changes across cell types, timepoints, and medulloblastoma subgroup models at whole-transcriptome resolution. Our work details substantial oncolytic virus-induced transcriptional remodeling of medulloblastoma-infiltrating immune cells, 10 subpopulations of monocytes and macrophages collectively demonstrating M1-like responses to C134, and suggests C134 be investigated as a potential new therapy for medulloblastoma.

ABSTRACT: Temporal indentations are the most impacting craniofacial complication after coronal flap dissection. It is mainly due to a temporal fat pad or temporalis muscle dissection.Because of the great improvements achieved recently in CAD-CAM-aided surgery and the possibility of performing accurate pre-surgical virtual planning, it is now possible to correct it with a customised virtual approach. Furthermore, advancements in material science have allowed surgeons to rely on biocompatible materials like PEEK (showing a low complication and recurrence rate) for the manufacturing of patient-specific implants.We hereby describe our experience on a case of secondary and corrective surgery after a fronto-orbital remodelling, in which we used PEEK implants designed by CAD and optimized by finite element modelling.

IntroductionThe Arizona Surge Line was an emergent initiative during the COVID-19 pandemic to facilitate COVID-19 patient transfers and load-level hospitals on a statewide level. It was designed and implemented by the Arizona Department of Health Services in preparation for the first hospital surge due to COVID-19, recognizing the disproportionate impact that hospital surge would have on rural and tribal populations.MethodsWe analyzed the Arizona Surge Line transfer data for the state's first two COVID-19 surges (4/16/2020–3/6/2021). Transfer data included transfer request characteristics, patient demographics and participating hospital characteristics. When applicable, we compared this data with Arizona census data, COVID-19 case data, and the CDC/ATSDR Social Vulnerability Index. The primary outcomes studied were the proportion of COVID-19 patient requests being successfully transferred, the median transfer time, and the proportion of vulnerable populations impacted.ResultsDuring the period of study, 160 hospitals in Arizona made 6,732 requests for transfer of COVID-19 patients. The majority of these patients (84%, 95% CI: 83–85%) were placed successfully with a median transfer time of 59 min (inter-quartile range 33–116). Of all transfer requests, 58% originated from rural hospitals, 53% were for patients of American Indian/Alaska Native ethnicity, and 73% of patients originated from highly vulnerable areas. The majority (98%) of receiving facilities were in urban areas. The Arizona Surge Line matched the number of transfers with licensed market shares during the period of study.ConclusionsThe Arizona Surge Line is an equity-enhancing initiative that disproportionately benefited vulnerable populations. This statewide transfer infrastructure could become a standard public health mechanism to manage hospital surges and enhance access to care during a health emergency.

Regional patterns of fish diversity, abundance, distribution, and assemblage composition are driven by a combination of biotic and abiotic conditions in the marine environment, but these conditions can be altered through anthropogenic activities, such as those associated with oil and gas extraction. The present study utilises data on fish relative abundance and diversity obtained from 1546 baited remote underwater video deployments conducted between 2004 – 2019 in depths of 9 – 170 m across 2000 km of coastline in north-west Australia on natural habitats and subsea pipelines to understand the influence of oil and gas infrastructure on fish assemblages. A total of 450 fish taxa from 56 families was observed, with populations dominated by generalist and invertebrate carnivore taxa. At the regional scale, subsea pipelines had lower diversity (lower taxonomic richness) than natural environments, but possessed a higher abundance of piscivorous and herbivorous fish taxa. Clear patterns in fish assemblage composition were observed in multivariate analyses, reflecting the proximity of oceanic shoals and banks, depth, and to a lesser extent, oil and gas infrastructure. Shallow-water and close to shoals assemblages were characterised by a diversity of site-attached (e.g., wrasses, tuskfish), reef-associated taxa (e.g., emperors). Mesophotic fish assemblages were characterised by commercially important (e.g., goldband snapper), wide-ranging (e.g., sharks) and sand-affiliated (e.g., toadfish, threadfin bream) taxa. Proximity to pipelines and platforms ranked low as predictors in the multivariate analyses suggesting a negligible regional influence of these structures on fish communities in comparison to depth and shoal habitats. Local-scale influences of subsea infrastructure, however, may be important for some fish species (infrastructure vs. immediate surrounds). Our study highlights the influence of abiotic factors on regional-scale patterns in fish assemblage structure across north-west Australia.

IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.ResultsSixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).ConclusionsConventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524

The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.

The aim of this study was to develop and validate a deep learning-based system to detect peripheral neuropathy (DN) from retinal colour images in people with diabetes. Retinal images from 1561 people with diabetes were used to predictDN diagnosed on vibration perception threshold. A total of 189 had diabetic retinopathy (DR), 276 had DN, and 43 had both DR and DN. 90% of the images were used for training and validation and 10% for testing. Deep neural networks, including Squeezenet, Inception, and Densenet were utilized, and the architectures were tested with and without pre-trained weights. Random transform of images was used during training. The algorithm was trained and tested using three sets of data: all retinal images, images without DR and images with DR. Area under the ROC curve (AUC) was used to evaluate performance. The AUC to predict DN on the whole cohort was 0.8013 (±0.0257) on the validation set and 0.7097 (±0.0031) on the test set. The AUC increased to 0.8673 (±0.0088) in the presence of DR. The retinal images can be used to identify individuals with DN and provides an opportunity to educate patients about their DN status when they attend DR screening.

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).

We developed a novel cantilever-based optical interfacial force microscope (COIFM) to study molecular interaction in liquid environments. The force sensor was created by attaching a chemically etched optical-fiber tip to the force sensor with UV epoxy, and characterized by imaging on a calibration grid. The performance of the COIFM was then demonstrated by measuring the force between two oxidized silicon surfaces in 1 mM KCl as a function of distance. The result was consistent with previously reported electrical double layer forces, suggesting that a COIFM using an optical-fiber tip is capable of measuring force in a liquid environment.

Lipoproteins are some of the most abundant proteins in bacteria. With a lipid anchor to the cell membrane, they function as enzymes, inhibitors, transporters, structural proteins, and as virulence factors. Lipoproteins activate the innate immune system and have biotechnological applications. The first lipoprotein was described by Braun and Rehn in 1969. Up until recently, however, work on lipoproteins has been sluggish, in part due to the challenges of handling proteins that are anchored to membranes by covalently linked lipids or are membrane integral. Activity in the area has quickened of late. In the past 5 years, high-resolution structures of the membrane enzymes of the canonical lipoprotein synthesis pathway have been determined, new lipoprotein types have been discovered and the enzymes responsible for their synthesis have been characterized biochemically. This has led to a flurry of activity aimed at developing novel antibiotics targeting these enzymes. In addition, surface exposed bacterial lipoproteins have been utilized as candidate vaccine antigens, and their potential to act as self-adjuvanting antigens is increasingly recognized. A summary of the latest developments in lipoproteins and their synthesis, as well as how this information is being exploited for therapeutic purposes is presented here.

Severe asthma has been shown to occur in the combined presence of high pollen and thunderstorm conditions, also known as ‘thunderstorm asthma.’ First studied as severe epidemic events, recent longitudinal work studied less dramatic but more frequent occurrences. We explore thunderstorm asthma-related emergency department visits in the Minneapolis-St. Paul metropolitan area and evaluated risk differences by sex and age. We define a thunderstorm asthma exposure event as the daily occurrence of 2 or more lightning strikes during high pollen periods, and use daily counts of asthma-related emergency department visits to estimate relative and absolute risk of severe asthma during thunderstorm asthma events for the full population and for sex and age subgroups. The overall population had a 1.06 (95 % CI: 1.02, 1.09) times higher risk of asthma-related ED visits during thunderstorm asthma events compared to days without thunderstorm asthma events. Children under 18 show no higher risk (RR 1.02; 95 % CI: 0.97 1.08), but adults 18–44 years (RR 1.08; 95 % CI: 1.02, 1.13) and 45 and up (RR 1.08; 95 % CI 1.02, 1.15) show higher relative risk. Absolute risk measures show similar patterns to the age and sex results, but age-sex subgroups show more variation in absolute vs relative risk. Our results support an association between ED visits and thunderstorm asthma and provide evidence of varying risks by sex across the life course. These differences in risk have implications for clinical treatment of this allergic type of asthma and for future research into this poorly recognized environmental exposure.Plain Language Summary: Recent research has highlighted the existence of Thunderstorm asthma events, a phenomenon in which pollen grains rupture in the conditions that occur with a thunderstorm, releasing sub-pollen particles that are capable of triggering severe asthma in susceptible populations. Where severe asthma is a disease that usually impacts children, we find in this study that asthma ED visits associated with thunderstorm asthma events more frequently impact adults, particularly males 18–44 and females 45 and up.

Background: : Climate change impacts humans and society both directly and indirectly. Alaska, for example, is warming twice as fast as the global mean, and researchers are starting to grapple with the varied and interconnected ways in which climate change affects the people there. With the number of wildfires increasing in Alaska as a result of climate change, the number of asthma cases has increased, driven by exposure to small particulate matter. However, it is not clear how far away smoke from wildfires can affect health. In this study, we hope to establish a relationship between proximity to wildfires and asthma in locations where direct PM2.5 measurement is not easily accomplished. Methods: : In this study, we examined whether proximity to wildfire exposure is associated with regional counts of adults with asthma, calculated using Behavioral Risk Factor Surveillance System (BRFSS) survey data and US Census data. We assigned ''hotspots'' around population centers with a range of various distances to wildfires in Alaska. Results: : We found that wildfires are associated with asthma prevalence, and the association is strongest within 25 miles of fires. Conclusions: : This study highlights the fact that proximity to wildfires has potential as a simple proxy for actual measured wildfire smoke, which has important implications for wildfire management agencies and for policy makers who must address health issues associated with wildfires, especially in rural areas.

Increasing agricultural production is essential for reducing food insecurity, which continues to be a major challenge faced by many developing countries. Here, we examine agricultural production—both crop and livestock—in association with demographic and climate changes from 1990 through 2014 in Kyrgyzstan, an understudied, disadvantaged, mountainous Central Asian country that experienced dramatic changes in political and market structures, rapid demographic changes, and climatic changes during the study period. Results indicate that crop and livestock productions, which are correlated with food security, experienced dramatic changes in the study period. The total fertility rate was found to have a negative association with livestock production. Precipitation and temperature measures had statistically significant and strong correlations with both crop and livestock productions, but their significance diminished dramatically when considered together with socioeconomic factors, regional variation, and temporal change. There were strong correlations among demographic, climatic, and socioeconomic factors, making it challenging to separate their individual attributions to the changes of agricultural production. Overall, a wetter and colder climate was associated with agricultural production increases. However, the growing season from 1991 through 2014 in Kyrgyzstan has become drier and hotter, challenging future agricultural production. This study provides insights into agricultural production and food security in relation to demographic and climate changes in Central Asia.

Highly Active Anti-Retroviral Therapy (HAART) has significantly increased life expectancy of the HIV-positive population. Nevertheless, the average lifespan of HIV patients remains shorter compared to uninfected individuals. Immunosenescence, a current explanation for this difference invokes heavily on viral stimulus despite HAART efficiency in viral suppression. We propose here that the premature and accelerated ageing of HIV patients can also be caused by adverse effects of antiretroviral drugs, specifically those that affect the mitochondria. The Nucleoside Reverse Transcriptase Inhibitor (NRTI) antiretroviral drug class for instance, is known to cause depletion of mitochondrial DNA via inhibition of the mitochondrial specific DNA polymerase-ƴ. Besides NRTIs, other antiretroviral drug classes such as Protease Inhibitors also cause severe mitochondrial damage by increasing oxidative stress and diminishing mitochondrial function. We also discuss important areas for future research and argue in favour of the use of C. elegans as a novel model system for studying these effects.