Train Trajectory-Following Control Method Using Virtual Sensors.

Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler-Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle's trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient.

The Relationship and Clinical Significance of Hepc-20, LpPLA2, PTX3, and Acute Myocardial Infarction in Patients with Varying Degrees of Coronary Artery Disease.

Objective: This study aimed to investigate the association between hepcidin-20 (Hepc-20), lipoprotein-associated phospholipase A2 (LpPLA2), pentraxin 3 (PTX3), acute myocardial infarction (AMI) occurrence, the severity of coronary artery lesions, and their predictive effectiveness. Methods: A total of 100 patients diagnosed and treated for AMI at our hospital between January 2021 and January 2022 were included in the AMI group. Based on the severity of coronary artery lesions determined by the Gensini score, patients were divided into the mild group and the moderate-to-severe group. Additionally, 100 healthy individuals were selected as control samples and included in the normal group. Serum levels of Hepc-20, LpPLA2, and PTX3 were compared, and receiver operating characteristic curves (ROC curves) were constructed to analyze the predictive efficacy of these biomarkers for AMI occurrence and the degree of coronary artery disease. Results: Compared to the normal group, the AMI group exhibited significantly increased serum levels of Hepc-20, LpPLA2, and PTX3 (P < .05). The sensitivity and specificity of serum Hepc-20, LpPLA2, and PTX3 in predicting AMI occurrence and the severity of coronary artery lesions were >60.00%, and the Area Under Curve (AUC) was >0.70. Moreover, compared to the mild group, the moderate-to-severe group showed significantly higher serum levels of Hepc-20, LpPLA2, and PTX3 (P < .05). Hepc-20, LpPLA2, and PTX3 demonstrated positive correlations with the severity of coronary artery lesions (P < .05). Conclusions: The levels of Hepc-20, LpPLA2, and PTX3 are elevated abnormally in AMI patients and positively associated with the degree of coronary artery disease. Hepc-20, LpPLA2, and PTX3 have the potential to serve as sensitive and accurate predictors of AMI occurrence and the severity of coronary artery disease, thereby warranting their clinical application.

Smartphone-based rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging.

Standard plate count (SPC) has been recognized as the golden standard for the quantification of viable bacteria. However, SPC usually takes one to several days to grow individual cells into a visible colony, which greatly hampers its application in rapid bacteria enumeration. Here we present a microdroplet turbidity imaging based digital standard plate count (dSPC) method to overcome this hurdle. Instead of cultivating on agar plates, bacteria are encapsulated in monodisperse microdroplets for single-cell cultivation. Proliferation of the encapsulated bacterial cell produced a detectable change in microdroplet turbidity, which allowed, after just a few bacterial doubling cycles (i.e., a few hours), enumeration of viable bacteria by visible-light imaging. Furthermore, a dSPC platform integrating a power-free droplet generator with smartphone-based turbidity imaging was established. As proof-of-concept demonstrations, a series of Gram-negative bacteria (Escherichia coli) and Gram-positive bacteria (Bacillus subtilis) samples were quantified via the smartphone dSPC accurately within 6 hours, representing a detection sensitivity of 100 CFU ml-1 and at least 3 times faster. In addition, Enterobacter sakazakii (E. sakazakii) in infant milk powder as a real sample was enumerated within 6 hours, in contrast to the 24 hours needed in traditional SPC. Results with high accuracy and reproducibility were achieved, with no difference in counts found between dSPC and SPC. By enabling label-free, rapid, portable and low-cost enumeration and cultivation of viable bacteria onsite, smartphone dSPC forms the basis for a temporally and geographically trackable network for surveying live microbes globally where every citizen with a cellphone can contribute anytime and anywhere.

Raman-Activated Droplet Sorting (RADS) for Label-Free High-Throughput Screening of Microalgal Single-Cells.

Raman-activated cell sorting (RACS) has attracted increasing interest, yet throughput remains one major factor limiting its broader application. Here we present an integrated Raman-activated droplet sorting (RADS) microfluidic system for functional screening of live cells in a label-free and high-throughput manner, by employing AXT-synthetic industrial microalga Haematococcus pluvialis (H. pluvialis) as a model. Raman microspectroscopy analysis of individual cells is carried out prior to their microdroplet encapsulation, which is then directly coupled to DEP-based droplet sorting. To validate the system, H. pluvialis cells containing different levels of AXT were mixed and underwent RADS. Those AXT-hyperproducing cells were sorted with an accuracy of 98.3%, an enrichment ratio of eight folds, and a throughput of ∼260 cells/min. Of the RADS-sorted cells, 92.7% remained alive and able to proliferate, which is equivalent to the unsorted cells. Thus, the RADS achieves a much higher throughput than existing RACS systems, preserves the vitality of cells, and facilitates seamless coupling with downstream manipulations such as single-cell sequencing and cultivation.

The relationship between neutrophil counts on admission and angiographic no-reflow after primary percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction.

BACKGROUND: The no-reflow phenomenon during primary percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction (STEMI) can lead to poor outcomes. Increased neutrophil counts have been associated with an increased risk of adverse clinical events after acute myocardial infarction (AMI). The aim of this study was to assess the relation between admission neutrophil counts and angiographic no-reflow after PPCI. METHODS: A total of 217 patients with acute STEMI who underwent PPCI, were enrolled. The patients were divided into two groups: no-reflow and normal-reflow. The neutrophil counts and other laboratory parameters were measured on admission before PPCI. RESULTS: There were 41 patients (18.9%) in the no-reflow group and 176 patients in the normal-reflow group. Patients with no-reflow were older (68.0 ± 11.7 years vs 60.7 ± 13.2 years, P = 0.019) and had significantly higher admission neutrophil counts (9.02 ± 3.97 × 109/L vs 7.57 ± 2.82 × 109/L, P = 0.007). Also, high-sensitivity C-reactive protein (hsCRP), white blood counts, monocyte counts were significantly higher while haemoglobin values were significantly lower in the no-reflow group. In multivariate analysis, neutrophil counts remained a strong independent predictor of angiographic no-reflow (odds ratio 1,200, 95% confidence interval 1.073-1.342, P = 0.001) together with age (odds ratio 1.041, 95% confidence interval 1.012-1.071, P = 0.005). CONCLUSIONS: Neutrophil counts on admission and age were independent clinical predictors of no-reflow following primary PCI in patients with STEMI. Our findings suggest that admission neutrophil counts may be available for early risk stratification of no-reflow after primary PCI and might allow the improvement of strategies to prevent this phenomenon.

Condensing Raman spectrum for single-cell phenotype analysis.

BACKGROUND: In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc. RESULTS: In this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.

Raman-activated cell sorting based on dielectrophoretic single-cell trap and release.

Raman-activated cell sorting (RACS) is a promising single-cell technology that holds several significant advantages, as RACS is label-free, information-rich, and potentially in situ. To date, the ability of the technique to identify single cells in a high-speed flow has been limited by inherent weakness of the spontaneous Raman signal. Here we present an alternative pause-and-sort RACS microfluidic system that combines positive dielectrophoresis (pDEP) for single-cell trap and release with a solenoid-valve-suction-based switch for cell separation. This has allowed the integration of trapping, Raman identification, and automatic separation of individual cells in a high-speed flow. By exerting a periodical pDEP field, single cells were trapped, ordered, and positioned individually to the detection point for Raman measurement. As a proof-of-concept demonstration, a mixture of two cell strains containing carotenoid-producing yeast (9%) and non-carotenoid-producing Saccharomyces cerevisiae (91%) was sorted, which enriched the former to 73% on average and showed a fast Raman-activated cell sorting at the subsecond level.

Effectiveness of Biodegradable Magnesium Alloy Stents in Coronary Artery and Femoral Artery.

OBJECTIVES: To access the biocompatibility, effectiveness, and safety of biodegradable magnesium (Mg) alloy stent (BMAS) in the coronary artery and femoral artery. BACKGROUND: Atherosclerosis is a lesion of cardiovascular system, including the diseases in heart and blood vessels. METHODS: The aluminum (Al) and zinc (Zn)-based BMAS was designed by cold drawing methods. Forty healthy immunized mongrel dogs were randomly divided into 8 groups. Five dogs who have not been treated with stent were included in control group. The other dogs were implanted with an absorbable magnesium (Mg) alloy in the coronary and/or femoral artery, and their artery angiography were observed at 7 time points (1, 3, 5, 7, 14, 21, and 28 days; n = 5) follow-up. Dogs from each cohort were sacrificed following angiography for pathology assessment. The histological response including inflammatory response, thrombosis, and intimal hyperplasia were analyzed by hematoxylin-eosin staining. Lumen area (La), intimal hyperplasia area (IHa), and the ratio of IHa were calculated by image analysis software. RESULTS: The thin-walled BMAS were designed and produced by cold-drawing technology. Fifty-one devices were implanted into coronary artery of 35 dogs successfully. During the follow-up days, the angiography of coronary artery and femoral artery had confirmed that the lumen was clear and there were no elastic recoil and thrombosis. The stents were completely disappeared at 7 days after implantation. Moderate intimal hyperplasia was found at 14 days after implantation. CONCLUSION: The BMAS stent proved to be of good biocompatibility, safety, and effectiveness. (J Interven Cardiol 2015;XXXX:XX-XX).

[Effects of rosuvastatin on arterial stiffness in hyperlipidemia patients].

OBJECTIVE: To evaluate the effects of rosuvastatin on arterial stiffness in hyperlipidemia patient without hypertension. METHODS: A total of 60 patients without hypertension received rosuvastatin (10 mg, n = 60) daily for 12 weeks while another 60 subjects used no statins. Brachial-ankle pulse wave velocity (ba-PWV), radial artery augmentation index of reflected wave (rAI) and metabolic index were measured before and after treatment. RESULTS: There were no significant change before and after non-statin treatment. Total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) levels decreased dramatically after resuvastin treatment [TC: (4.0 ± 1.0) vs (5.8 ± 1.1) mmol/L; LDL-C: (2.1 ± 0.7) vs (3.8 ± 0.7) mmol/L, both P < 0.01]. In rosuvastatin group, ba-PWV and rAI decreased significantly [ba-PWV: (1 340 ± 177) vs (1 477 ± 159) cm/s; rAI: (44 ± 13) % vs (57 ± 15) %, P < 0.01 and P < 0.05). CONCLUSION: Atherosclerosis may be improved by rosuvastatin treatment in hyerlipidemia patient without hypertension.

A double-branch convolutional neural network model for species identification based on multi-modal data.

For species identification analysis, methods based on deep learning are becoming prevalent due to their data-driven and task-oriented nature. The most commonly used convolutional neural network (CNN) model has been well applied in Raman spectra recognition. However, when faced with similar molecules or functional groups, the features of overlapping peaks and weak peaks may not be fully extracted using the CNN model, which can potentially hinder accurate species identification. Based on these practical challenges, the fusion of multi-modal data can effectively meet the comprehensive and accurate analysis of actual samples when compared with single-modal data. In this study, we propose a double-branch CNN model by integrating Raman and image multi-modal data, named SI-DBNet. In addition, we have developed a one-dimensional convolutional neural network combining dilated convolutions and efficient channel attention mechanisms for spectral branching. The effectiveness of the model has been demonstrated using the Grad-CAM method to visualize the key regions concerned by the model. When compared to single-modal and multi-modal classification methods, our SI-DBNet model achieved superior performance with a classification accuracy of 98.8%. The proposed method provided a new reference for species identification based on multi-modal data fusion.

Tracking organic matrix in the seashell by elemental mapping under laser-induced breakdown spectroscopy.

As a biogenic calcium carbonate, the seashell plays a crucial role in marine environmental studies. In these studies, it is essential to investigate the composition of the seashell. In this study, we used laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of cultured scallop-shell (Patinopecten yessoensis), with a specific focus on examining the organic elements (C, N, O, H) to track the shell organic matrix (SOM). Our findings indicate that the seashell organic layer can be accurately identified by referencing the strong emission of nitrogen or the low signal of calcium. To further confirm the presence of this layer, we employed fluorescence spectroscopy, Raman spectroscopy and FTIR spectroscopy. Correlation analysis revealed a strong connection between LIBS emissions (H, O, CC) and seashell organics, as well as demonstrated the presence of organics in metallic emissions (Si, Ba). However, when we conducted elemental mapping on the shell cross-section, the distribution similarity was observed between the elements N, Ba, and Sr. Based on the correlation of organics and the distribution similarity, it is concluded that barium is an element associated with the SOM. These results highlight the potential of LIBS for organic analysis, which can complement traditional seashell analysis.

Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model.

The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educational policies and incorporated entrepreneurial skills as a part of their education models to further catalyst the country's economic progress. This research focuses on building a novel hybrid Machine Learning (ML) model by integrating two powerful algorithms namely Random Forest (RF) and Logistic Regression (LR) to assess the intensity of the I&E in education from the data acquired from 25 leading Higher Educational Institution's (HEI) in different provinces. The major contributions to the work are, (1) construction of quality index for each topic of interest using individual RF, (2) ranking the indicators based on the quality index to assess the strength and weaknesses, (3) and finally use the LR algorithm study the quality of each indicator. The efficacy of the proposed hybrid model is validated using the benchmark classification metrics to assess its learning and prediction performance in evaluating the quality of I&E education. The result of the research portrays that the universities have now started to integrate entrepreneurship skills as a part of the curriculum, which is evident from the better ranking of the topic curriculum development which is followed by the enrichment of skills. This comprehensive research will help the institutions to identify the potential areas of growth to boost the economic development and improve the skill set necessary for I&E education among college students.

Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy.

Microplastics (MPs) pose a threat to human and environmental health, and have emerged as a global environmental issue. Because MPs are small and complex, methods of quickly and reliably classifying and identifying them are either lacking or in the early stages of development. In this study, micro-Raman spectroscopy and a convolutional neural network (CNN) were combined to establish identification models for 10 MP references and three environmental samples. In addition, an interaction network was established based on pair-wise correlations of Raman bands to determine the influence of environmental stress on MPs. The CNN model achieved average classification accuracies of 96.43% and 95.6% for the 10 MP references and the three environmental samples, respectively. For MPs exposed to environmental stressors, an interaction network can provide highly sensitive, information-dense, and universally applicable signatures for characterizing the environmental processes affecting MP spectra. The results of this study can help establish efficient and automatic analysis for accurate identification of MPs as well as an intuitive exhibition of spectral changes on environmental exposure.

Mid-level data fusion of Raman spectroscopy and laser-induced breakdown spectroscopy: Improving ores identification accuracy.

The identification of ore samples is of great scientific significance for mineral exploration, and geological evolution research on the planets. Attributed to the changes in the composition and structure of the same ore, the fusion of multiple technologies can effectively meet the comprehensive and accurate analysis of actual samples compared with a single technology. We develop an efficient method of applying the combination of Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) to ores identification. We construct a convolutional neural network (CNN) model and train it with mid-level Raman-LIBS fusion spectra of ores. Also, we develop a hybrid feature selection method AVPSO based on analysis of variance (ANOVA) with the particle swarm optimization (PSO) to improve the classification performance of the model. Compared with the model features visualized by Grad-CAM method, the similarity selected features verify the effectiveness of the AVPSO method. The identification of mid-level fusion strategy provides the best accuracy of 98%, while the accuracies of Raman and LIBS are slightly lower with values of 87.9% and 91.3%, respectively. The proposed method is of great significance for the rapid and accurate identification of ore samples.

Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods.

There has been an increasing demand for the rapid verification of fish authenticity and the detection of adulteration. In this work, we combined LIBS and Raman spectroscopy for the fish species identification for the first time. Two machine learning methods of SVM and CNN are used to establish the classification models based on the LIBS and Raman data obtained from 13 types of fish species. Data fusion strategies including low-level, mid-level and high-level fusions are used for the combination of LIBS and Raman data. It shows that all these data fusion strategies offer a significant improvement in fish classification compared with the individual LIBS or Raman data, and the CNN model works more powerfully than the SVM model. The low-level fusion CNN model provides a best classification accuracy of 98.2%, while the mid-level fusion involved with feature selection improves the computing efficiency and gains the interpretability of CNN.

Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC.

A full-spectrum spontaneous single-cell Raman spectrum (fs-SCRS) captures the metabolic phenome for a given cellular state of the cell in a label-free, landscape-like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP-DLD) force that is exerted to focus and trap fast-moving single cells in a wide channel, which enables efficient fs-SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity-resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell-type classification. Moreover, when coupled with intra-ramanome correlation analysis, it reveals state- and cell-type-specific metabolic heterogeneity and metabolite-conversion networks. The throughput of ≈30-2700 events min-1 for profiling both nonresonance and resonance marker bands in a fs-SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP-DLD-RFC is a valuable new tool for label-free, noninvasive, and high-throughput profiling of single-cell metabolic phenomes.

Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products.

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low-cost strategies for quality assessment. Here, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Single-Cell Identification, Viability and Vitality tests, and Source-tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O-probed single-cell Raman spectrum (SCRS) enables species-level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C-D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source-tracking, single-cell Raman-activated Cell Sorting and Sequencing can proceed, producing indexed, precisely one-cell-based genome assemblies that can reach ~99.40% genome-wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20-fold faster, >10-time cheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

[High maintenance dose of clopidogrel improves long-term clinical outcomes in patients with elective percutaneous coronary intervention].

OBJECTIVE: To evaluate the efficacy of a 300 mg loading dose of clopidogrel followed by 150 mg as maintenance dose in patients with percutaneous coronary intervention (PCI). METHODS: A total of 108 consecutive patients undergoing elective PCI were recruited from our hospital from July 2007 to July 2008. A 300 mg loading dose was administered prior to PCI. Then they were randomized to receive clopidogrel 75 mg (n = 55) or 150 mg (n = 46) daily for 30 days. From Day 30 to Month 6 post-operation, all of them received 75 mg/d clopidogrel and were followed up for a mean period of 6 months. RESULTS: Thirty days after PCI, the platelet inhibition of the 150 mg group was significantly higher than the 75 mg group (64.2% ± 13.3% vs 52.6% ± 14.3%, P = 0.00). The ratios of fatal or non-fatal myocardial infarction (MI) (1(1.8%) vs 3 (6.5%), P = 0.405) and target vessel revascularization (TVR) (4(7.2%) vs 6 (13.0%), P = 0.714) were significantly lower in the 150 mg group than those in the 75 mg group. So the overall incidence of MACE including death, MI and TVR was obviously lower in the 150 mg group than that in the 75 mg group (13.0% vs 20.2%, absolute risk reduction 7.3%). CONCLUSION: A high clopidogrel maintenance dose of 150 mg daily for the first month after PCI reduces the risk of long-term adverse events in patients with elective percutaneous coronary intervention.

[High-dose tirofiban in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention].

OBJECTIVE: To evaluate the clinical outcomes of high-dose tirofiban in patients with ST-elevation myocardial infarction (ASTEMI) undergoing primary percutaneous coronary intervention (PCI). METHODS: A total of 104 consecutive ASTEMI patients undergoing primary PCI were enrolled from January 2010 to February 2011. They were randomized into the high-dose tirofiban group (n = 52) and the normal-dose tirofiban group (n = 52). We measured the sumST-segment resolution of ECG post-PCI respectively and left ventricular ejective fraction (LVEF) at Day 90 post-PCI. RESULTS: After PCI, the sumST-segment resolution of ECG of the high-dose tirofiban group significantly improved than that of the normal-dose tirofiban group (38% ± 12% vs 34% ± 13%, P < 0.05). Before PCI, LVEF of two groups is 50.2% ± 1.4% vs 49.6% ± 1.1% (P > 0.05), but at day 90 post-PCI, LVEF had significant difference between two groups (60.1% ± 1.1% vs 56.0% ± 1.2%, P < 0.05). The rates of major and moderate hemorrhage did not differ significantly between two groups. CONCLUSION: High-dose tirofiban improves myocardial reperfusion and clinical outcome. It re-emphasizes the importance of further platelet aggregation inhibition in ASTEMI patients undergoing primary PCI.

[Prognostic significance of admission N-terminal pro-brain natriuretic peptide in predicting angiographic no-reflow phenomenon during percutaneous coronary intervention in patients with acute myocardial infarction].

OBJECTIVE: To determine the relationship between N-terminal pro-brain-type natriuretic peptide (NT-proBNP) and angiographic no-reflow phenomenon in patients with acute myocardial infarction (AMI) after primary percutaneous coronary intervention (PCI). METHODS: The data of 106 consecutive AMI patients undergoing primary PCl were collected and analyzed retrospectively. NT-proBNP was obtained pre-PCI at admission. According to the NT-proBNP level, they were divided into normal and elevated NT-proBNP groups. The no-reflow phenomenon was defined as an angiographic outcome of Thrombolysis In Myocardial Infarction (TIMI) grade < 3 without accompanying mechanical factors. RESULTS: The patients with elevated NT-proBNP on admission had a higher incidence of no-reflow phenomenon than those with NT-proBNP level. Compared to normal reflow counterparts, no-reflow patients had a higher NT-proBNP level [1883 ng/L (484 ∼ 5500 ng/L) vs 220 ng/L (87 ∼ 926 ng/L) P = 0.046]. Multivariate analysis showed that a high NT-proBNP level (NT-proBNP > 1765 ng/L) on admission was an independent predictor of no-reflow. This cut-off value yielded a sensitivity of 60.0% and a specificity of 87.5% respectively. CONCLUSION: The NT-proBNP level on admission may be a prognostic biomarker in the prediction of the development of angiographic "no-reflow" phenomenon after primary PCI for AMI patients.