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BackgroundNational guidelines recommend that waist circumference (WC) be measured in patients with a body mass index (BMI) 27–35 kg/m2. Unfortunately, perhaps due to logistical reasons, WC is seldom measured in clinical settings. Herein, we describe the performance of a self-operated waist measurement device (SOWMD) as a potential means to overcome barriers to measuring WC.Materials and methodsTen volunteers underwent WC measures by professionals and SOWMD on 5 separate days to assess the reproducibility and accuracy. We then compared SOWMD measures with CT-derived fat content by recruiting 81 patients scheduled for a diagnostic abdominal CT scan.ResultsThere was no difference between professionally measured and SOWMD-measured WC; the intraindividual coefficient of variation over the 5 days was between 0.4% and 2.2%. The WC measured manually, by SOWMD and CT scan were highly correlated (r=0.90–0.92, all p<0.001). The minimal WC measured by SOWMD was a better predictor (r=0.81 for all patients, r=0.87 for men, both p<0.001) of CT-measured visceral adipose tissue volume than other approaches. The minimal WC measured by SOWMD was correlated with fasting plasma glucose (r=0.40, p<0.05), triglyceride (r=0.41, p<0.01) and high-density cholesterol (r=−0.49, p<0.001) concentrations.ConclusionSOWMD is a reproducible, accurate and convenient way to measure WC that can provide added value for health care providers when combined with BMI information.

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.

Quasi-periodic eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks 1 – 5 . The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs) undergoing instabilities 6 – 8 or interacting with a stellar object in a close orbit 9 – 11 . It has been suggested that this disk could be created when the SMBH disrupts a passing star 8 , 11 , implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs 4 , 12 and two observed TDEs have exhibited X-ray flares consistent with individual eruptions 13 , 14 . TDEs and QPEs also occur preferentially in similar galaxies 15 . However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 h from AT2019qiz, a nearby and extensively studied optically selected TDE 16 . We detect and model the X-ray, ultraviolet (UV) and optical emission from the accretion disk and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs. The detection and modelling of nine X-ray quasi-periodic eruptions from a nearby tidal disruption event shows that these eruptions arise in accretion disks around massive black holes, left behind by tidally disrupted stars, and that an orbiting body colliding with this disk is a plausible explanation for the X-ray variability.

The SARS-CoV-2 pandemic has made it clear that we have a desperate need for antivirals. We present work that the mammalian SKI complex is a broad-spectrum, host-directed, antiviral drug target. Yeast suppressor screening was utilized to find a functional genetic interaction between proteins from influenza A virus (IAV) and Middle East respiratory syndrome coronavirus (MERS-CoV) with eukaryotic proteins that may be potential host factors involved in replication. This screening identified the SKI complex as a potential host factor for both viruses. In mammalian systems siRNA-mediated knockdown of SKI genes inhibited replication of IAV and MERS-CoV. In silico modeling and database screening identified a binding pocket on the SKI complex and compounds predicted to bind. Experimental assays of those compounds identified three chemical structures that were antiviral against IAV and MERS-CoV along with the filoviruses Ebola and Marburg and two further coronaviruses, SARS-CoV and SARS-CoV-2. The mechanism of antiviral activity is through inhibition of viral RNA production. This work defines the mammalian SKI complex as a broad-spectrum antiviral drug target and identifies lead compounds for further development.

Background Understanding the earliest manifestations of Alzheimer’s disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer’s disease (FAD) cohort to characterise preclinical cognitive change. Methods Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline. Results Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum. Conclusions Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer’s disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.

Salmon is the most commonly consumed finfish in the United States of America (USA), and the mislabeling of salmon is a widespread problem. Washington State is a global supplier of wild-caught Pacific salmon and local salmon mislabeling results in substantial economic, ecological, and cultural impacts. Previous studies in Washington State identified high levels of mislabeled salmon in both markets and restaurants, resulting in local legislation being passed that requires proper labeling of salmon products, including identifying it as wild-caught or farm-raised. To investigate whether recent legislative efforts reduced salmon fraud rates, we acquired and genetically barcoded salmon samples from 67 grocery stores and 52 sushi restaurants in Seattle, Washington. DNA from each salmon sample was isolated and the cytochrome c oxidase gene was sequenced to identify the fish species. Our study, conducted from 2022–2023, revealed 18% of salmon samples from both grocery stores and sushi restaurants were mislabeled. While most samples were acquired during the fall months when wild salmon is in season, we still observed a high salmon mislabeling rate. Unlike grocery stores, Seattle sushi restaurants often sold farmed salmon mislabeled as wild salmon. Specifically, substitutions of vendor-claimed wild salmon with farmed salmon occurred in 32.3% of sushi restaurant samples compared to 0% of grocery store samples. Additionally, occurrences of wild salmon being substituted with another salmon species (wild or farmed) occurred in 38.7% of sushi restaurant samples compared to 11.1% of grocery store samples. All salmon substitutions in sushi restaurants harmed the customer financially as they were given a cheaper market-priced fish. In grocery stores, however, we did not detect significant economic loss to customers due to salmon mislabeling. Taken together, it is important to continue to develop and enforce legislation in Washington State that prevents salmon fraud and promotes ecologically sustainable fishing practices.

Non-alcoholic fatty liver disease ranges from steatosis to non-alcoholic steatohepatitis (NASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC). Here, we show that platelet number, platelet activation and platelet aggregation are increased in NASH but not in steatosis or insulin resistance. Antiplatelet therapy (APT; aspirin/clopidogrel, ticagrelor) but not nonsteroidal anti-inflammatory drug (NSAID) treatment with sulindac prevented NASH and subsequent HCC development. Intravital microscopy showed that liver colonization by platelets depended primarily on Kupffer cells at early and late stages of NASH, involving hyaluronan-CD44 binding. APT reduced intrahepatic platelet accumulation and the frequency of platelet–immune cell interaction, thereby limiting hepatic immune cell trafficking. Consequently, intrahepatic cytokine and chemokine release, macrovesicular steatosis and liver damage were attenuated. Platelet cargo, platelet adhesion and platelet activation but not platelet aggregation were identified as pivotal for NASH and subsequent hepatocarcinogenesis. In particular, platelet-derived GPIbα proved critical for development of NASH and subsequent HCC, independent of its reported cognate ligands vWF, P-selectin or Mac-1, offering a potential target against NASH. Blockade of intrahepatic accumulation and function of platelets represents a potential approach to treat non-alcoholic steatohepatitis (NASH) and prevent subsequent progression to hepatocellular carcinoma

Category: Ankle; Ankle Arthritis Introduction/Purpose: As the incidence of TAA increases, identifying ways to stratify a patient’s risk for adverse outcomes becomes imperative. Two potential tools, the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI), have been widely supported and utilized across various fields of medicine and orthopaedic surgery. Despite encouraging results in TAA literature, the validity of the CCI and ECI as predictive tools for postoperative complications and functional outcomes following TAA remains ultimately unexplored. This study aims to describe and compare the predictive capacity of the CCI and ECM on the outcomes of TAA to further understand the reliability of such comorbidity indices in orthopaedic research. Methods: The Nationwide Readmissions Database (NRD) was queried from 2015 to 2020 to identify 29,705 patients undergoing primary TAA. Patients’ comorbidity was measured via CCI and ECI score, with patients who experienced adverse postoperative outcomes (any complication, readmission, mortality, extended length of stay (LOS), and adverse discharge) identified. Each index’s predictive ability was measured using the c statistic, a measure of area under the receiver operating characteristic curve (AUC). The value for AUC ranges from 0.50 to 1.0, indicating the discriminative ability of each index in assigning probability of the examined outcome, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect ability to discriminate. AUC was categorized into poor (AUC<.70), acceptable (0.70< AUC< 0.80), excellent (0.80< AUC< 0.90), and outstanding (0.90< AUC< 1.0) regarding the predictive capability of each model. The indices were also compared to a base model, which considered age, sex, and primary payer. Results: The overall cohort was majority male (54.2%) with a mean age of 64.15 (range 17-90) years, and the majority of patients had Medicare as their primary expected payer (58.9%). Elixhauser comorbidity index provided a superior predictive ability for any complication (ECI AUC=0.61, CCI AUC=0.59; p<.001), extended LOS (ECI AUC=0.69, CCI AUC=0.65; p<.001), and adverse discharge (ECI AUC=0.70, CCI AUC=0.68; p<.001) as compared to the Charlson comorbidity index. Examination of each model’s predictive ability found mortality was the only variable that both ECI (AUC=0.88, 95%Confidence Interval [CI]= 0.77–1.00) and CCI (AUC=0.88; 95% CI=0.79–0.96) were able to predict with excellent capability. Additionally, the ECI predicted adverse discharge with acceptable capability (AUC=0.70; 95%CI=0.69-0.71). Conclusion: In this study, the ECI outperformed the CCI in predicting any complication, extended length of stay ≥4 days, and adverse discharge to a facility in patients undergoing total ankle arthroplasty. However, the only variable that was excellently predicted was 180-day mortality, in which there was no difference in the predictive capability of each index. These findings indicate that while ECI and CCI are capable of predicting postoperative mortality following TAA, there is a need for alternative models that offer better predictive capability when examining other postoperative adverse outcomes.

Submission Type: Ankle Arthritis Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: As the incidence of TAA increases, identifying ways to stratify a patient’s risk for adverse outcomes becomes imperative. Two potential tools, the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI), have been widely supported and utilized across various fields of medicine and orthopaedic surgery. Despite encouraging results in TAA literature, the validity of the CCI and ECI as predictive tools for postoperative complications and functional outcomes following TAA remains ultimately unexplored. This study aims to describe and compare the predictive capacity of the CCI and ECM on the outcomes of TAA to further understand the reliability of such comorbidity indices in orthopaedic research. Methods: The Nationwide Readmissions Database (NRD) was queried from 2015 to 2020 to identify 29,705 patients undergoing primary TAA. Patients’ comorbidity was measured via CCI and ECI score, with patients who experienced adverse postoperative outcomes (any complication, readmission, mortality, extended length of stay (LOS), and adverse discharge) identified. Each index’s predictive ability was measured using the c statistic, a measure of area under the receiver operating characteristic curve (AUC). The value for AUC ranges from 0.50 to 1.0, indicating the discriminative ability of each index in assigning probability of the examined outcome, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect ability to discriminate. AUC was categorized into poor (AUC <.70), acceptable (0.70< AUC < 0.80), excellent (0.80< AUC < 0.90), and outstanding (0.90< AUC < 1.0) regarding the predictive capability of each model. The indices were also compared to a base model, which considered age, sex, and primary payer. Results: The overall cohort was majority male (54.2%) with a mean age of 64.15 (range 17-90) years, and the majority of patients had Medicare as their primary expected payer (58.9%). Elixhauser comorbidity index provided a superior predictive ability for any complication (ECI AUC=0.61, CCI AUC=0.59; p<.001), extended LOS (ECI AUC=0.69, CCI AUC=0.65; p<.001), and adverse discharge (ECI AUC=0.70, CCI AUC=0.68; p<.001) as compared to the Charlson comorbidity index. Examination of each model’s predictive ability found mortality was the only variable that both ECI (AUC=0.88, 95%Confidence Interval [CI]= 0.77–1.00) and CCI (AUC=0.88; 95% CI=0.79–0.96) were able to predict with excellent capability. Additionally, the ECI predicted adverse discharge with acceptable capability (AUC=0.70; 95%CI=0.69-0.71). Conclusion: In this study, the ECI outperformed the CCI in predicting any complication, extended length of stay ≥4 days, and adverse discharge to a facility in patients undergoing total ankle arthroplasty. However, the only variable that was excellently predicted was 180-day mortality, in which there was no difference in the predictive capability of each index. These findings indicate that while ECI and CCI are capable of predicting postoperative mortality following TAA, there is a need for alternative models that offer better predictive capability when examining other postoperative adverse outcomes.

Category: Ankle; Ankle Arthritis Introduction/Purpose: The overuse of opioid medications in patients undergoing orthopaedic surgery has become widely labeled as a national epidemic. Recent literature, particularly in the fields of orthopaedic spine surgery and joint arthroplasty, has illustrated an association between chronic opioid use and increased costs and postoperative complications. The purpose of this study is to evaluate the influence of chronic opioid dependence on postoperative outcomes following total ankle arthroplasty (TAA). We hypothesized that preoperative opioid dependence would be associated with higher rates of complications, revisions, readmissions, and mortality following TAA, as well as increased cost and longer hospital stays. Methods: The Nationwide Readmissions Database (NRD) was queried from 2015-2020 to identify 29,751 patients undergoing primary elective TAA. Patients were divided into two cohorts based on the presence of preoperative opioid dependence, with 861 (2.9%) being chronic opioid users or having opioid use disorders. Preoperative demographics, comorbidities, postoperative outcomes, cost of admission, and total length of stay (LOS) were analyzed between cohorts. Multivariate regression analyses were conducted to identify independent predictors of postoperative outcomes other than preoperative opioid dependence. Results: The overall cohort was majority male (54.2%) with mean age of 64.15 (range 17-90) years, and Charlson-Deyo Comorbidity Index (CCI) score of 0.65 (range 0-12). When stratifying by preoperative opioid dependence, it was found that opioid dependent patients were statistically significantly older (Opioid=62.74 years; Nonopioid=65.21 years; p<.001), from lower income quartile (p=.042), more likely to have Medicaid insurance (p<.001), and had a higher CCI score (Opioid=0.97; Nonopioid=0.64; p<.001). Multivariate regression analysis of 180-day postoperative outcomes found that preoperative opioid dependence was significantly predictive of increased risk of wound dehiscence (p<.001), adverse discharge (p<.001), and extended stay greater than 4 days (p<.001). Further, preoperative opioid dependence is predictive of more than a $1,000 increase in total cost of procedure (β= 1,052.27; 95% CI= 117.62-1,986.91; p=.027). (Table1) Conclusion: Chronic preoperative opioid use was significantly predictive of higher rates of postoperative complications, LOS, and a substantially higher cost of procedure following TAA. Physicians should consider this modifiable risk factor when identifying patients and tailoring medication regimens for patients receiving ankle arthroplasty.