Acute myocardial infarction after noncardiac surgery

Em todo o mundo, são realizadas mais de 230 milhões de operações por ano e as complicações cardíacas são as causas mais comuns de morbidade e mortalidade pós-operatórias. Com o aumento da expectativa de vida da população mundial, um número crescente de pacientes com múltiplas comorbidades tem sido submetido a operações não cardíacas. Em consequência, é esperado um aumento de complicações cardiovasculares associadas a tais procedimentos e o infarto agudo do miocárdio (IAM) perioperatório poderá se tornar um problema frequente. No Brasil, o número de operações não cardíacas também está aumentando, sendo realizadas aproximadamente três milhões de cirurgias por ano. Apesar dos avanços nas técnicas cirúrgicas e anestésicas, a mortalidade e o custo relacionados a estes procedimentos também estão aumentando, sendo fundamental o desenvolvimento de estratégias para a redução da mortalidade. A ocorrência de um IAM perioperatório prolonga a necessidade de terapia intensiva, a estadia hospitalar, aumenta o custo da internação e diminui a sobrevida a longo prazo. Esta revisão aborda a fisiopatologia, a incidência, o diagnóstico e o tratamento do IAM perioperatório, baseado nas evidências atuais

Aspirin responsiveness safely lowers perioperative cardiovascular risk

IntroductionVascular surgeries are related to high cardiac morbidity and mortality, and the maintenance of aspirin in the perioperative period has a protective effect. The purpose of this study was to evaluate the association between preoperative platelet aggregability and perioperative cardiovascular (CV) events.MethodsA preoperative platelet aggregation test was performed on an impedance aggregometer in response to collagen and to arachidonic acid (AA) for 191 vascular surgery patients under chronic use of aspirin. We analyzed the following CV events: acute myocardial infarction, unstable angina, isolated troponin elevation, acute ischemic stroke, reoperation, and cardiac death. Hemorrhagic events were also evaluated and classified according to the Thrombolysis In Myocardial Infarction criteria.ResultsThe incidence of CV events was 22% (n = 42). Higher platelet response to AA was associated with CV events, so that patients in the fourth quartile (higher than 11Ω) had almost twice the incidence of CV events when compared with the three lower quartiles: 35% vs 19%; P = .025. The independent predictors of CV events were hemodynamic instability during anesthesia (odds ratio [OR], 4.12; 95% confidence interval [CI], 1.87-9.06; P < .001), dyslipidemia (OR, 3.9; 95% CI, 1.32-11.51; P = .014), preoperative anemia (OR, 2.64; 95% CI, 1.19-5.85; P = .017), and AA platelet aggregability in the upper quartile (OR, 2.48; 95% CI, 1.07-5.76; P = .034). Platelet aggregability was not associated with hemorrhagic events, even when we compared the lowest quartile of AA platelet aggregability (0-1.00 Ω) with the three upper quartiles (>1.00 Ω; OR, 0.77; 95% CI, 0.43-1.37; P = .377).ConclusionsThe degree of aspirin effect on platelet aggregability maybe important in the management of perioperative CV morbidity, without increment in the bleeding toll

Pyk2 activates the NLRP3 inflammasome by directly phosphorylating ASC and contributes to inflammasome-dependent peritonitis

The inflammasome adaptor protein, ASC, contributes to both innate immune responses and inflammatory diseases via self-oligomerization, which leads to the activation of the protease, caspase-1. Here, we report that the cytosolic tyrosine kinases, FAK and Pyk2, are differentially involved in NLRP3 and AIM2 inflammasome activation. The inhibition of FAK and Pyk2 with RNA interference or chemical inhibitors dramatically abolished ASC oligomerization, caspase-1 activation, and IL-1β secretion in response to NLRP3 or AIM2 stimulation. Pyk2 is phosphorylated by the kinase Syk and relocalizes to the ASC specks upon NLRP3 inflammasome activation. Pyk2, but not FAK, could directly phosphorylate ASC at Tyr146, and only the phosphorylated ASC could participate in speck formation and trigger IL-1β secretion. Moreover, the clinical-trial-tested Pyk2/FAK dual inhibitor PF-562271 reduced monosodium urate-mediated peritonitis, a disease model used for studying the consequences of NLRP3 activation. Our results suggest that although Pyk2 and FAK are involved in inflammasome activation, only Pyk2 directly phosphorylates ASC and brings ASC into an oligomerization-competent state by allowing Tyr146 phosphorylation to participate ASC speck formation and subsequent NLRP3 inflammation

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.MethodsTwo hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.ResultsThe NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC.ConclusionAll DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT