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Introduction: Heart failure (HF) secondary to acute myocardial infarction (AMI) is still a worldwide problem with a high mortality rate. The current study aimed to explore early and reliable predictive biomarkers of HF following AMI. Methods: The gene expression profile GSE59867 was downloaded from GEO. Array data from peripheral blood mononuclear cells (PBMCs) was used from 46 control patients and 111 patients with AMI at four time points: (i) first day of AMI; (ii) 4-6 days after AMI; (iii) one month after AMI; and (iv) six months after AMI. Among the 111 AMI patients, nine with HF and eight without HF were studied. CIBERSORT was used to analyze the relative proportions of immune cells in PBMCs. The proportions of immune cells in different groups were compared. Differentially expressed genes (DEGs) were analyzed with R language packages. Results: The percentages of monocytes and neutrophils increased significantly on the first day of AMI, and then decreased gradually. The percentage of regulatory T cells increased significantly 4-6 days after AMI, while the percentage of resting memory CD4 cells, CD8 T cells, and resting natural killer cells decreased significantly on the first day of AMI, and then increased gradually. Patients who developed HF had a significantly higher proportion of neutrophils in PBMCs on the first day of AMI, but had a significantly lower proportion of naive CD4 T cells. Two shared genes, interleukin-1 receptor 2 (IL1R2) and leucine-rich repeat neuronal protein 3 (LRRN3), were found to have potentially important roles in predicting the development of HF following AMI. Conclusion: A higher proportion of neutrophils and a lower proportion of naive CD4 T cells in PBMCs on the first day of AMI may be correlated with the development of HF following AMI. IL1R2 and LRRN3 may exert functions in the development of HF following AMI. Resumo: Objetivos: A insuficiência cardíaca (IC) secundária ao enfarte agudo do miocárdio (EAM) constitui um problema mundial devido à elevada taxa de mortalidade. Este estudo tem o objetivo de identificar biomarcadores preditores de IC pós o EAM. Métodos: O perfil da expressão do gene GSE59867 foi descarregado do GEO. Foram utilizados os dados da matriz das células sanguíneas mononucleares periféricas (CSMP), incluindo 46 doentes controlo e 111 doentes com EAM em quatro momentos: i) 1.° dia após EAM; II) 4–6 dias após EAM; iii) um mês após EAM; e iv) seis meses após EAM. Dos 111 doentes com EAM, foram estudados nove doentes com insuficiência cardíaca (IC) e oito sem IC. O CIBERSORT foi utilizado para analisar as proporções relativas das células imunes nas CSMP. As proporções das células imunes foram comparadas nos diferentes grupos. Os genes expressos de forma diferente (GEDs) foram analisados com pacotes de linguagem R. Resultados: A percentagem de monócitos e de neutrófilos aumentou significativamente no primeiro dia após o EAM e foi diminuindo gradualmente. A percentagem de Treg aumentou significativamente 4–6 dias após o EAM. A percentagem de repouso da memória CD4, as células CD8 T e as células de repouso NK diminuíram significativamente no primeiro dia após o EAM e seguidamente aumentaram gradualmente. Os doentes com prognóstico de IC apresentaram uma proporção mais elevada de neutrófilos nas CSMP no primeiro dia após o EAM, tendo, no entanto, apresentado uma proporção significativa menos elevada de células CD4 T. Dois genes partilhados, a interleucina 1 recetor 2 (IL1R2) e a proteína neuronal de repetição rica em leucina 3 (LRRN3), foram consideradas como tendo um papel potencialmente importante no prognóstico de IC após o EAM. Conclusão: Uma proporção mais elevada de neutrófilos e uma proporção mais baixa de células CD4 T naïve nas CSMP no primeiro dia após o EAM podem correlacionar-se com o prognóstico de EAM. A IL1R2 e a LRRN3) podem exercer funções potenciais no desenvolvimento da IC pós-EAM.

ABSTRACT: This paper proposed the use of an adaptive Kalman filter (AFK) to improve Global Positioning System (GPS) positioning accuracy to measure a tractor operational area. First, we used MATLAB to identify the operation trajectory. Then, we used different colors to show the area of operation. Finally, we used an image-processing method to calculate the effective operational area, actual operational area, and repeat and omission rates. We used these rates to evaluate the tractor efficiency. The experiment indicated that the Kalman filter improved the accuracy of GPS single-point positioning. To test the GPS area-measurement precision, field area measurements were taken. We used GPS to measure standard figures and some irregular figures. The results indicate that the area measurement relative error was 2.09%. The measurement accuracy increased with the increasing measurement area. The field test results indicated that the most efficient farming method was alternative tillage and the second most efficient was spindle tillage. The omission rate under back tillage was highest and its operational efficiency was lowest.