Graphical abstract: Study design and main results: Two test-retest data sets were collected at 2 different centers (data set I and data set II). For both data sets, 2 different echocardiographers acquired separate recordings in immediate succession in each patient to create a test-retest pair of images (upper panel). For each patient, these test-retest recordings were analyzed by 4 readers who measured global longitudinal strain (GLS) using a commercially available semiautomatic method. This resulted in 12 interreader and 4 intrareader scenarios. In addition, GLS was measured using an artificial intelligence (AI) method based on deep learning. The test-retest measurement bias and minimal detectable change for all inter- and intrareader scenarios and the AI scenario are presented for both data sets (lower panels). Artificial intelligence–based measurements eliminated test-retest bias between readers and resulted in reduced test-retest variability.
Eosinophils play a homeostatic role in the body’s immune responses. These cells are involved in combating some parasitic, bacterial, and viral infections and certain cancers and have pathologic roles in diseases including asthma, chronic rhinosinusitis with nasal polyps, eosinophilic gastrointestinal disorders, and hypereosinophilic syndromes. Treatment of eosinophilic diseases has traditionally been through nonspecific eosinophil attenuation by use of glucocorticoids. However, several novel biologic therapies targeting eosinophil maturation factors, such as interleukin (IL)-5 and the IL-5 receptor or IL-4/IL-13, have recently been approved for clinical use. Despite the success of biologic therapies, some patients with eosinophilic inflammatory disease may not achieve adequate symptom control, underlining the need to further investigate the contribution of patient characteristics, such as comorbidities and other processes, in driving ongoing disease activity. New research has shown that eosinophils are also involved in several homeostatic processes, including metabolism, tissue remodeling and development, neuronal regulation, epithelial and microbiome regulation, and immunoregulation, indicating that these cells may play a crucial role in metabolic regulation and organ function in healthy humans. Consequently, further investigation is needed into the homeostatic roles of eosinophils and eosinophil-mediated processes across different tissues and their varied microenvironments. Such work may provide important insights into the role of eosinophils not only under disease conditions but also in health. This narrative review synthesizes relevant publications retrieved from PubMed informed by author expertise to provide new insights into the diverse roles of eosinophils in health and disease, with particular emphasis on the implications for current and future development of eosinophil-targeted therapies. 相似文献
ObjectiveTo examine the effect of high-intensity interval training (HIIT) on body fat mass and distribution in patients with myocardial infarction (MI) who underwent cardiac rehabilitation (CR).Patients and MethodsWe retrospectively screened 391 consecutive patients with MI enrolled in CR between September 1, 2015, and February 28, 2018. We included 120 patients who completed 36 CR sessions and underwent pretest-posttest dual-energy x-ray absorptiometry; 90 engaged in HIIT, and 30 engaged in moderate-intensity continuous training (MICT). High-intensity interval training included 4 to 8 alternating intervals of high- (30-60 seconds at a rating of perceived exertion [RPE] of 15-17 [Borg scale range, 6-20]) and low-intensity (1-5 minutes at RPE <14), and MICT performed for 20 to 45 minutes of exercise at an RPE of 12 to 14. Body weight, fat mass, and lean mass were measured via dual-energy x-ray absorptiometry with lipid profile measured via clinical procedures.ResultsThe HIIT and MICT groups were similar in age (67 vs 67 years), sex (26.7% [24 of 90 patients in the HIIT group] vs 26.7% [8 of 30 in the MICT group), and body mass index (30.3 vs 29.5 kg/m2) at baseline. The HIIT group had greater reductions in body fat percentage (P<.001), fat mass (P<.001), abdominal fat percentage (P<.001), waist circumference (P=.01), total cholesterol (P=.002), low-density lipoprotein cholesterol (P<.001), and triglycerides (P=.006). Improvements in total body mass and body mass index were not different across groups. After matching exercise duration, exercise intensity, and energy expenditure, HIIT-induced improvements in total fat mass (P=.02), body fat percentage (P=.01), and abdominal fat percentage (P=.02) persisted.ConclusionOur data suggest that supervised HIIT results in significant reductions in total fat mass (P<.001) and abdominal fat percentage (P<.001) and improved lipid profile in patients with MI who undergo CR. 相似文献
ObjectiveTo assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%).MethodsPatients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality.ResultsA total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%.ConclusionA novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery. 相似文献
ObjectiveTo evaluate the association of baseline and postinfusion patient characteristics with acute kidney injury (AKI) in the month after chimeric antigen receptor T-cell (CAR-T) therapy.MethodsWe retrospectively reviewed records of 83 patients with non-Hodgkin lymphoma undergoing CAR-T therapy (axicabtagene ciloleucel) between June 2016 and November 2020. Patients were followed up to 1 month after treatment. Post–CAR-T AKI was defined as a more than 1.5-fold increase in serum creatinine concentration from baseline (on the day of CAR-T infusion) at any time up to 1 month after CAR-T therapy.ResultsOf 83 patients, 14 (17%) developed AKI during follow-up. At 1 month after CAR-T infusion, 10 of 14 (71%) AKI events had resolved. Lower baseline estimated glomerular filtration rate, use of intravenous contrast material, tumor lysis prophylaxis, higher peak uric acid and creatine kinase levels during follow-up, and change in lactate dehydrogenase from baseline to peak level within 1 month after initiation of CAR-T therapy were significantly associated with AKI incidence during follow-up. Incidence of AKI was also higher in patients who received higher doses of corticosteroids and tocilizumab.ConclusionAcute kidney injury occurred in approximately 1 in 6 patients who received axicabtagene ciloleucel for non-Hodgkin lymphoma. Patients with high tumor burden receiving higher total doses of corticosteroids or tocilizumab should be closely monitored for development of AKI. Lower baseline kidney function at CAR-T initiation, exposure to contrast material, and progressive increase in levels of tumor lysis markers (uric acid, lactate dehydrogenase, creatine kinase) after CAR-T infusion may predict risk of AKI during the 1 month after infusion. 相似文献
