Computed tomography derived bone density measurement in the diabetic foot

Background: The accurate and reliable measurement of foot bone density is challenging and there is currently no gold standard technique. Such measurement is particularly valuable in populations at risk of foot bone pathology such as in those with long term diabetes. With research and development, computed tomography may prove to be a useful tool for this assessment. The aim of this study was to establish the reliability of a novel method of foot bone density measurement in people with diabetes using computed tomography. Methods: Ten feet in people with diabetes were scanned with computed tomography twice with repositioning. Bone density (in Hounsfield units) was assessed in the trabecular and cortical bone in all tarsals and metatarsals. Reliability was assessed with intra-class correlation coefficients (95% confidence intervals), limits of agreement and standard error of measurement. Results: The reliability of the trabecular density of most bones was excellent with intra-class correlation coefficients ranging from 0.68 to 0.91. Additionally, cortical bone density showed fair to good reliability at the talus (0.52), calcaneus (0.59), navicular (0.70), cuboid (0.69), intermediate cuneiform (0.46) and first metatarsal (0.61). Conclusions: The study established the reliability of a practical method of assessing the trabecular and cortical foot bone density using computed tomography scanning. This methodology may be useful in the investigation of foot bone disease occurring in diabetes and its early diagnosis, intervention and assessment of treatment efficacy. Further development of this method is warranted

Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems

As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide future design efforts. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights and overcome the limitations of heritage designs. This paper presents a step toward extracting knowledge from optimization data in multi-split fluid-based thermal management systems using different classification machine learning methods, so that designers can use it to guide decisions in future design efforts. This approach offers several advantages over traditional design heritage methods, including applicability in cases where there is no design heritage and the ability to derive optimal designs. We showcase our framework through four case studies with varying levels of complexity. These studies demonstrate its effectiveness in enhancing the design of complex thermal management systems. Our results show that the knowledge extracted from the configuration design optimization data provides a good basis for more general design of complex thermal management systems. It is shown that the objective value of the estimated optimal configuration closely approximates the true optimal configuration with less than 1 percent error, achieved using basic features based on the system heat loads without involving the corresponding optimal open loop control (OLOC) features. This eliminates the need to solve the OLOC problem, leading to reduced computation costs.Comment: 13 pages, 20 figure

Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.Comment: 13 pages, 17 figure

Peripheral sensory neuropathy is associated with altered postocclusive reactive hyperemia in the diabetic foot

Objective: This study examined whether the presence of peripheral sensory neuropathy or cardiac autonomic deficits is associated with postocclusive reactive hyperemia (reflective of microvascular function) in the diabetic foot. Research design and methods: 99 participants with type 2 diabetes were recruited into this crosssectional study. The presence of peripheral sensory neuropathy was determined with standard clinical tests and cardiac autonomic function was assessed with heart rate variation testing. Postocclusive reactive hyperemia was measured with laser Doppler in the hallux. Multiple hierarchical regression was performed to examine relationships between neuropathy and the peak perfusion following occlusion and the time to reach this peak. Results: Peripheral sensory neuropathy predicted 22% of the variance in time to peak following occlusion (p<0.05), being associated with a slower time to peak but was not associated with the magnitude of the peak. Heart rate variation was not associated with the postocclusive reactive hyperemia response. Conclusions: This study found an association between the presence of peripheral sensory neuropathy in people with diabetes and altered microvascular reactivity in the lower limb

Outcomes in pediatric studies of medium-chain acyl-coA dehydrogenase (MCAD) deficiency and phenylketonuria (PKU): a review.

BACKGROUND: Inherited metabolic diseases (IMDs) are a group of individually rare single-gene diseases. For many IMDs, there is a paucity of high-quality evidence that evaluates the effectiveness of clinical interventions. Clinical effectiveness trials of IMD interventions could be supported through the development of core outcome sets (COSs), a recommended minimum set of standardized, high-quality outcomes and associated outcome measurement instruments to be incorporated by all trials in an area of study. We began the process of establishing pediatric COSs for two IMDs, medium-chain acyl-CoA dehydrogenase (MCAD) deficiency and phenylketonuria (PKU), by reviewing published literature to describe outcomes reported by authors, identify heterogeneity in outcomes across studies, and assemble a candidate list of outcomes. METHODS: We used a comprehensive search strategy to identify primary studies and guidelines relevant to children with MCAD deficiency and PKU, extracting study characteristics and outcome information from eligible studies including outcome measurement instruments for select outcomes. Informed by an established framework and a previously published pediatric COS, outcomes were grouped into five, mutually-exclusive, a priori core areas: growth and development, life impact, pathophysiological manifestations, resource use, and death. RESULTS: For MCAD deficiency, we identified 83 outcomes from 52 articles. The most frequently represented core area was pathophysiological manifestations, with 33 outcomes reported in 29/52 articles (56%). Death was the most frequently reported outcome. One-third of outcomes were reported by a single study. The most diversely measured outcome was cognition and intelligence/IQ for which eight unique measurement instruments were reported among 14 articles. For PKU, we identified 97 outcomes from 343 articles. The most frequently represented core area was pathophysiological manifestations with 31 outcomes reported in 281/343 articles (82%). Phenylalanine concentration was the most frequently reported outcome. Sixteen percent of outcomes were reported by a single study. Similar to MCAD deficiency, the most diversely measured PKU outcome was cognition and intelligence/IQ with 39 different instruments reported among 82 articles. CONCLUSIONS: Heterogeneity of reported outcomes and outcome measurement instruments across published studies for both MCAD deficiency and PKU highlights the need for COSs for these diseases, to promote the use of meaningful outcomes and facilitate comparisons across studies

Uptake and impact of vaccinating primary school-age children against influenza: experiences of a live attenuated influenza vaccine programme, England, 2015/16.

The 2015/16 influenza season was the third season of the introduction of an intra-nasally administered live attenuated influenza vaccine (LAIV) for children in England. All children aged 2‒6 years were offered LAIV, and in addition, a series of geographically discrete areas piloted vaccinating school-age children 7‒11 years old. Influenza A(H1N1)pdm09 was the dominant circulating strain during 2015/16 followed by influenza B. We measured influenza vaccine uptake and the overall and indirect effect of vaccinating children of primary school -age, by comparing cumulative disease incidence in targeted and non-targeted age groups in vaccine pilot and non-pilot areas in England. Uptake of 57.9% (range: 43.6-72.0) was achieved in the five pilot areas for children aged 5‒11 years. In pilot areas, cumulative emergency department respiratory attendances, influenza-confirmed hospitalisations and intensive care unit admissions were consistently lower, albeit mostly non-significantly, in targeted and non-targeted age groups compared with non-pilot areas. Effect sizes were less for adults and more severe endpoints. Vaccination of healthy primary school-age children with LAIV at moderately high levels continues to be associated with population-level reductions in influenza-related respiratory illness. Further work to evaluate the population-level impact of the programme is required

Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation

The brain contains a complex network of axons rapidly communicating information between billions of synaptically connected neurons. The morphology of individual axons, therefore, defines the course of information flow within the brain. More than a century ago, Ramón y Cajal proposed that conservation laws to save material (wire) length and limit conduction delay regulate the design of individual axon arbors in cerebral cortex. Yet the spatial and temporal communication costs of single neocortical axons remain undefined. Here, using reconstructions of in vivo labelled excitatory spiny cell and inhibitory basket cell intracortical axons combined with a variety of graph optimization algorithms, we empirically investigated Cajal's conservation laws in cerebral cortex for whole three-dimensional (3D) axon arbors, to our knowledge the first study of its kind. We found intracortical axons were significantly longer than optimal. The temporal cost of cortical axons was also suboptimal though far superior to wire-minimized arbors. We discovered that cortical axon branching appears to promote a low temporal dispersion of axonal latencies and a tight relationship between cortical distance and axonal latency. In addition, inhibitory basket cell axonal latencies may occur within a much narrower temporal window than excitatory spiny cell axons, which may help boost signal detection. Thus, to optimize neuronal network communication we find that a modest excess of axonal wire is traded-off to enhance arbor temporal economy and precision. Our results offer insight into the principles of brain organization and communication in and development of grey matter, where temporal precision is a crucial prerequisite for coincidence detection, synchronization and rapid network oscillations

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men