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ion for persistent AF.

We assessed the impact of optimal dyslipidemia control on mortality and costs in adults at high risk for cardiovascular disease (HRCVD).

We linked Alberta health databases to identify patients aged ≥ 18 years with HRCVD between April 2012 and March 2017. The first HRCVD event was considered the index event. Patients were categorized into (1) optimal control and (2) suboptimal control of dyslipidemia based on biomarkers and lipid-lowering therapy during the year post-index event. We measured the association between optimal dyslipidemia control and mortality and health care costs using difference-in-difference and propensity score-matching methods.

The study included 459,739 patients with HRCVD (43,776 [9.5%] optimal patients). The optimal patients were older (median age= 62 vs 55 years; P < 0.001), included fewer female patients (37.7% vs 52%; P < 0.001), and featured a higher proportion of secondary prevention patients (15.7% vs 1.7%; P < 0.001). Compared with suboptimal patients, the optimal patients had lower adjusted mortality (0.7% vs 1.9% at 1-year and 2.9% vs 5.1% at 3-year post-index event; both P < 0.001), and higher adjusted health care costs (CA$3758 and CA$6844 at 1-year and 3-year post-index event, respectively; both P < 0.001). Among the secondary prevention group, the optimal patients had lower adjusted mortality (2.4% and 5% absolute reduction at 1-year and 3-year post-index event, respectively; both P < 0.001) at no additional costs. PF-4708671 cell line The results were robust across 5 definitions of optimal dyslipidemia control.

Patients with optimal dyslipidemia control have lower mortality and incur modestly higher costs. However, secondary prevention patients experience lower mortality at no additional costs.

Patients with optimal dyslipidemia control have lower mortality and incur modestly higher costs. However, secondary prevention patients experience lower mortality at no additional costs.

Severely obese patients have decreased cardiorespiratory fitness (CRF) and poor functional capacity. Bariatric surgery-induced weight loss improves CRF, but the determinants of this improvement are not well known. We aimed to assess the determinants of CRF before and after bariatric surgery and the impact of an exercise training program on CRF after bariatric surgery.

Fifty-eight severely obese patients (46.1 ± 6.1 kg/m

, 78% women) were randomly assigned to either an exercise group (n= 39) or usual care (n= 19). Exercise training was conducted from the 3rd to the 6th months after surgery. Anthropometric measurements, abdominal and mid-thigh computed tomographic scans, resting echocardiography, and maximal cardiopulmonary exercise testing was performed before bariatric surgery and 3 and 6 months after surgery.

Weight, fat mass, and fat-free mass were reduced significantly at 3 and 6 months, without any additive impact of exercise training in the exercise group. From 3 to 6 months, peak aerobic power (V̇O

) increased significantly (P < 0.0001) in both groups but more importantly in the exercise group (exercise group from 18.6 ± 4.2 to 23.2 ± 5.7 mL/kg/min; control group from 17.4 ± 2.3 to 19.7 ± 2.4 mL/kg/min; P value, group× time= 0.01). In the exercise group, determinants of absolute V̇O

(L/min) were peak exercise ventilation, oxygen pulse, and heart rate reserve (r

= 0.92; P < 0.0001), whereas determinants of V̇O

indexed to body mass (mL/kg/min) were peak exercise ventilation and early-to-late filling velocity ratio (r

= 0.70; P < 0.0001).

A 12-week supervised training program has an additive benefit on cardiorespiratory fitness for patients who undergo bariatric surgery.

A 12-week supervised training program has an additive benefit on cardiorespiratory fitness for patients who undergo bariatric surgery.Regulatory T cells (Tregs) are vital for the maintenance of immune homeostasis, while their dysfunction constitutes a cardinal feature of autoimmunity. Under steady-state conditions, mitochondrial metabolism is critical for Treg function; however, the metabolic adaptations of Tregs during autoimmunity are ill-defined. Herein, we report that elevated mitochondrial oxidative stress and a robust DNA damage response (DDR) associated with cell death occur in Tregs in individuals with autoimmunity. In an experimental autoimmune encephalitis (EAE) mouse model of autoimmunity, we found a Treg dysfunction recapitulating the features of autoimmune Tregs with a prominent mtROS signature. Scavenging of mtROS in Tregs of EAE mice reversed the DDR and prevented Treg death, while attenuating the Th1 and Th17 autoimmune responses. These findings highlight an unrecognized role of mitochondrial oxidative stress in defining Treg fate during autoimmunity, which may facilitate the design of novel immunotherapies for diseases with disturbed immune tolerance.Serum acetate increases upon systemic infection. Acutely, assimilation of acetate expands the capacity of memory CD8+ T cells to produce IFN-γ. Whether acetate modulates memory CD8+ T cell metabolism and function during pathogen re-encounter remains unexplored. Here we show that at sites of infection, high acetate concentrations are being reached, yet memory CD8+ T cells shut down the acetate assimilating enzymes ACSS1 and ACSS2. Acetate, being thus largely excluded from incorporation into cellular metabolic pathways, now had different effects, namely (1) directly activating glutaminase, thereby augmenting glutaminolysis, cellular respiration, and survival, and (2) suppressing TCR-triggered calcium flux, and consequently cell activation and effector cell function. In vivo, high acetate abundance at sites of infection improved pathogen clearance while reducing immunopathology. This indicates that, during different stages of the immune response, the same metabolite-acetate-induces distinct immunometabolic programs within the same cell type.Background Scant data are available about global patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread and global epidemiology of early confirmed cases of COVID-19 outside mainland China. We describe the global spread of SARS-CoV-2 and characteristics of COVID-19 cases and clusters before the characterisation of COVID-19 as a pandemic.

Cases of COVID-19 reported between Dec 31, 2019, and March 10, 2020 (ie, the prepandemic period), were identified daily from official websites, press releases, press conference transcripts, and social media feeds of national ministries of health or other government agencies. Case characteristics, travel history, and exposures to other cases were abstracted. Countries with at least one case were classified as affected. Early cases were defined as those among the first 100 cases reported from each country. Later cases were defined as those after the first 100 cases. We analysed reported travel to affected countries among the first case reported from each country outside mainland China, demographic and exposure characteristics among cases with age or sex information, and cluster frequencies and sizes by transmission settings.