RESUMO
Takotsubo syndrome (TTS), commonly referred to as "broken heart syndrome," is a distinctive form of acute and reversible heart failure that primarily affects young to middle-aged individuals, particularly women. While emotional or physical stressors often trigger TTS, rare cases have been linked to interventional procedures for congenital heart disease (CHD). Despite its recognition, the exact causes of TTS remain elusive. Research indicates that dysregulation in autonomic nerve function, involving sympathetic and parasympathetic activities, plays a pivotal role. Genetic factors, hormonal influences like estrogen, and inflammatory processes also contribute, unveiling potential gender-specific differences in its occurrence. Understanding these multifaceted aspects of TTS is crucial for refining clinical approaches and therapies. Continued research efforts will not only deepen our understanding of this syndrome but also pave the way for more targeted and effective diagnostic and treatment strategies. In this report, we conduct an in-depth analysis of a case involving a TTS patient, examining the illness progression and treatment procedures. The aim of this analysis is to enhance the understanding of TTS among primary care physicians. By delving into this case, we aspire to prevent misdiagnosis of typical TTS cases that patients may present, thereby ensuring a more accurate diagnosis and appropriate treatment.
Assuntos
Permeabilidade do Canal Arterial , Insuficiência Cardíaca , Cardiomiopatia de Takotsubo , Pessoa de Meia-Idade , Humanos , Feminino , Cardiomiopatia de Takotsubo/diagnóstico por imagem , Cardiomiopatia de Takotsubo/etiologia , Permeabilidade do Canal Arterial/complicações , Insuficiência Cardíaca/complicações , Emoções , SíndromeRESUMO
BACKGROUND: Cardiovascular diseases have become a prominent threat to public health and quality of life. In recent years, some studies have reported that ivabradine can improve the cardiac function and prognosis of patients with acute myocardial infarction (AMI). METHODS: We searched China National Knowledge Infrastructure (CNKI), Wanfang database, Chinese Biomedical Literature (CBM), Chongqing Weipu Chinese Sci-tech Journal Database (VIP), PubMed, Cochrane Library, and EMBASE for randomized controlled trials (RCTs) of ivabradine in the treatment of AMI from January 1980 until December 2020. Each RCT was systematically reviewed. RESULTS: A total of 7 RCTs with 658 patients were included. Compared with the control group, the heart rate [mean deviation (MD) =-9.20, 95% confidence interval (CI): -13.03 to -5.38, P<0.00001] and brain natriuretic peptide (BNP) (MD =-112.73, 95% CI: -186.12 to -39.35, P=0.003) of patients who received ivabradine combined with conventional standard treatment were significantly lower and left ventricular ejection fraction (LVEF) (MD =3.17, 95% CI: 2.12 to 4.23, P<0.00001) was significantly better. The difference in adverse events was not statistically significant [odds ratio (OR) =2.45, 95% CI: 0.92 to 6.55, P=0.07]. DISCUSSION: Ivabradine combined with ß-blockers can reduce the resting heart rate and improve heart function in patients with AMI while not increasing adverse events. However, due to limitations in the number and quality of studies included, our conclusions need to be further confirmed by analyzing more studies.
Assuntos
Infarto do Miocárdio , China , Humanos , Ivabradina/uso terapêutico , Infarto do Miocárdio/tratamento farmacológico , Volume SistólicoRESUMO
The outbreak of the COVID-19 epidemic brings huge consequences on the global economy and health. Diverse restrictive policies have been enforced to prevent the novel Coronavirus from spreading. During the COVID-19 pandemic, the express logistics companies transport essential goods among different regions in China, supporting people's normal lives. This study explores the dynamic impacts of COVID-19 epidemic on the intra-provincial and inter-provincial express parcel flows based on the statistical and econometric analysis considering the individual-specific effects of each province. The key results obtained from the unique long-run panel data analysis are: (1) the temporal fluctuation of inter-provincial express logistics flows affected by the epidemic is stronger than that of intra-provincial flows, both of which also show significant spatial heterogeneity; (2) the process of China's fighting against the COVID-19 pandemic is divided into four stages according to the severity of pandemic and implications of restrictive policies, which have moderating effects for the impacts on the express logistics; (3) the dynamic effects of the pandemic on the express logistics are obviously heterogeneous in different stages, confirming the effectiveness of restrictive and support policies; (4) the delayed effects of COVID-19 epidemic on the regional express logistics vary with different setting of time lags. In the short term, the typical restrictive policies and Chinese Spring Festival holidays have significant negative effects on the express logistics. In the long term, the resumption of work and production stimulate the demands of express logistics, presenting significant positive effects in post-epidemic era. This study can provide the policy implications for the logistics planning and management under the public health intervention.
RESUMO
The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.