Thorpepittman5880

Cardiac monitoring after murine myocardial infarction, using serial non-invasive cardiac 18F-FDG positron emissions tomography (PET) represents a suitable and accurate tool for in vivo studies. Cardiac PET imaging enables tracking metabolic alterations, heart function parameters and provides correlations of the infarct size to histology. ECG-gated 18F-FDG PET scans using a dedicated small-animal PET scanner were performed in mice at baseline, 3, 14, and 30 days after myocardial infarct (MI) by permanent ligation of the left anterior descending (LAD) artery. The percentage of the injected dose per gram (%ID/g) in the heart, left ventricular metabolic volume (LVMV), myocardial defect, and left ventricular function parameters end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and the ejection fraction (EF%) were estimated. PET assessment of the defect positively correlates with post-infarct histology at 3 and 30 days. Infarcted murine hearts show an immediate decrease in LVMV and an increase in %ID/g early after infarction, diminishing in the remodeling process. This study of serial cardiac PET scans provides insight for murine myocardial infarction models by novel infarct surrogate parameters. It depicts that serial PET imaging is a valid, accurate, and multimodal non-invasive assessment.Aim Accumulating evidence suggests that MELD-XI score holds the ability to predict the prognosis of congestive heart failure. However, most of the evidence is based on the end-stage heart failure population; thus, we aim to explore the association between the MELD-XI score and the prognosis in heart failure with preserved ejection fraction (HFpEF). Methods A total of 30,096 patients hospitalized for HFpEF in Fujian Provincial Hospital between January 1, 2014 and July 17, 2020 with available measures of creatinine and liver function were enrolled. The primary endpoint was 60-day in-hospital all-cause mortality. Secondary endpoints were 60-day in-hospital cardiovascular mortality and 30-day rehospitalization for heart failure. Results A total of 222 patients died within 60 days after admission, among which 75 deaths were considered cardiogenic. And 73 patients were readmitted for heart failure within 30 days after discharge. Generally, patients with an elevated MELD-XI score tended to have more comorbidities, higher NYHA class, and higher inflammatory biomarkers levels. Meanwhile, the MELD-XI score was positively correlated with NT-pro BNP, left atrial diameter, E/e' and negatively correlated with LVEF. After adjusting for conventional risk factors, the MELD-XI score was independently associated with 60-day in-hospital all-cause mortality [hazard ratio(HR) = 1.052, 95% confidential interval (CI) 1.022-1.083, P = 0.001], 60-day in-hospital cardiovascular mortality (HR = 1.064, 95% CI 1.013-1.118, P = 0.014), and 30-day readmission for heart failure (HR = 1.061, 95% CI 1.015-1.108, P = 0.009). Furthermore, the MELD-XI score added an incremental discriminatory capacity to risk stratification models developed based on this cohort. Conclusion The MELD-XI score was associated with short-term adverse events and provided additional discriminatory capacity to risk stratification models in patients hospitalized for HFpEF.Background Arterial Doppler Ultrasound waveform (DW) analysis allows the detection and evaluation of lower extremity peripheral artery disease. The high heterogeneity of the reported description of DW is reduced by the use of classification. However, the reliability of these classifications is either unknown or low to moderate and practices of vascular caregivers regarding the use of these classifications remain unknown. Aims This study aims to assess the inter-observer reliability of the Saint-Bonnet classification, a 13-category DW classification. buy Ferrostatin-1 The secondary objective was to determine the utilization rate of the most common classifications and the ability of these classifications to describe DW. Methods A national survey was conducted among all vascular physicians of French society of vascular medicine. They were invited by email to describe on a website 20 DW without and with the display of the Saint-Bonnet classification. The reliability of this classification was estimated by Fleiss' Kappa expressed wusion The reliability of Saint-Bonnet classification is weak to moderate by vascular physicians who are not familiar with its use. However, unlike the other classifications, it seems to be sufficiently precise so that the user does not need to complete its description. There is a significant heterogeneity in the use of arterial Doppler classifications in France.Objective Few studies have been concerned with the combined influences of the presence of multiple risk factors on follow-up outcomes in AMI patients. Our study aimed to identify risk factor patterns that may be associated with 1-year survival in male patients with ST-segment elevation myocardial infarction (STEMI). Methods Data were from the China STEMI Care Project Phase 2 (CSCAP-2) collected between 2015 and 2018. A total of 15,675 male STEMI patients were enrolled in this study. Risk factor patterns were characterized using latent class analysis (LCA) according to seven risk factors. Associations between risk factor patterns and follow-up outcomes, including the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) and all-cause death, were investigated by Cox proportional hazard regression analysis. Results We obtained four risk factor patterns as "young and middle-aged with low levels of multimorbidity," "middle-aged with overweight," "middle-aged and elderly with normal weight," and "elderly with high multimorbidity." Four patterns had significant differences in event-free survival (P less then 0.001). As compared with the patients of "young and middle-aged with low levels of multimorbidity" pattern, the risk of incidence of MACCE and all-cause death were increased in patients of "middle-aged with overweight" pattern (All-cause death HR = 1.70, 95% CI1.29~2.23; MACCE HR = 1.49, 95% CI1.29~1.72), "middle-aged and elderly with normal weight" pattern (All-cause death HR = 3.04, 95% CI 2.33~3.98; MACCE HR = 1.82, 95% CI 1.56~2.12), and "elderly with high multimorbidity" pattern (All-cause death HR = 5.78, 95% CI 4.49~7.42; MACCE HR = 2.67, 95% CI 2.31~3.10). Conclusions By adopting a Latent Class Analysis Approach, STEMI patients can be characterized into four risk factor patterns with significantly different prognosis. The data is useful for the improvement of community health management in each specific subgroup of patients, which indicates a particular risk factor pattern.Background Lung injury is a common condition among hospitalized patients with coronavirus disease 2019 (COVID-19). However, whether lung ultrasound (LUS) score predicts all-cause mortality in patients with COVID-19 is unknown. The aim of the present study was to explore the predictive value of lung ultrasound score for mortality in patients with COVID-19. Methods Patients with COVID-19 who underwent lung ultrasound were prospectively enrolled from three hospitals in Wuhan, China between February 2020 and March 2020. Demographic, clinical, and laboratory data were collected from digital patient records. Lung ultrasound scores were analyzed offline by two observers. Primary outcome was in-hospital mortality. Results Of the 402 patients, 318 (79.1%) had abnormal lung ultrasound. Compared with survivors (n = 360), non-survivors (n = 42) presented with more B2 lines, pleural line abnormalities, pulmonary consolidation, and pleural effusion (all p less then 0.05). Moreover, non-survivors had higher global and antratification of patients with COVID-19.The Coronavirus disease 2019 (Covid-19) pandemic has brought the world to a standstill. Healthcare systems are critical to maintain during pandemics, however, providing service to sick patients has posed a hazard to frontline healthcare workers (HCW) and particularly those caring for elderly patients. Various approaches are investigated to improve safety for HCW and patients. One promising avenue is the use of robots. Here, we model infectious spread based on real spatio-temporal precise personal interactions from a geriatric unit and test different scenarios of robotic integration. We find a significant mitigation of contamination rates when robots specifically replace a moderate fraction of high-risk healthcare workers, who have a high number of contacts with patients and other HCW. While the impact of robotic integration is significant across a range of reproductive number R0, the largest effect is seen when R0 is slightly above its critical value. Our analysis suggests that a moderate-sized robotic integration can represent an effective measure to significantly reduce the spread of pathogens with Covid-19 transmission characteristics in a small hospital unit.The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients' lungs for diagnosis and staging. link2 An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force-displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.Soft robotic grippers are increasingly desired in applications that involve grasping of complex and deformable objects. However, their flexible nature and non-linear dynamics makes the modelling and control difficult. Numerical techniques such as Finite Element Analysis (FEA) present an accurate way of modelling complex deformations. link3 However, FEA approaches are computationally expensive and consequently challenging to employ for real-time control tasks. Existing analytical techniques simplify the modelling by approximating the deformed gripper geometry. Although this approach is less computationally demanding, it is limited in design scope and can lead to larger estimation errors. In this paper, we present a learning based framework that is able to predict contact forces as well as stress distribution from soft Fin Ray Effect (FRE) finger images in real-time. These images are used to learn internal representations for deformations using a deep neural encoder, which are further decoded to contact forces and stress maps using separate branches.