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.

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.

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.

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.

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.

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.

Assessing Efficacy of Clinical Disinfectants for Pathogenic Fungi by Single-Cell Raman Microspectroscopy.

Disinfectants are crucial for root canal therapy (RCT), as metabolism of canal-inhabiting microbes can cause refractory infections. To develop effective yet patient- and environment-friendly disinfectant formulations, we quantitatively assessed the metabolism-inhibiting effects of intracanal disinfectants via D2O-probed Single-Cell Raman Spectra (SCRS), using Candida albicans (C. albicans) as a pathogen model. For chlorhexidine gluconate (CHX), sodium hypochlorite (NaClO), and hydrogen peroxide (H2O2), at their MIC of 4, 168, and 60 µg/ml, respectively, despite the complete growth halt, metabolic activity of individual fungal cells was reduced on average by 0.4%, 93.9%, and 94.1% at 8 h, revealing a "nongrowing but metabolically active" (NGMA) state that may underlie potential refractory infections, particularly for CHX. In contrast, at their Metabolic Activity-based Minimum Inhibitory Concentrations (MIC-MA) of 8, 336, and 120 µg/ml, respectively, metabolic activity of all cells was completely halted throughout 8 h exposure. Moreover, combined use of NaClO+H2O2 (combination at 0.5× MIC-MA each) outperforms solo uses of CHX, NaClO, H2O2, or other binary combinations. Furthermore, dynamics of SCRS revealed distinct fungicidal mechanisms of CHX, NaClO, H2O2, and their pairwise combinations. MIC-MA is advantageous in critically assessing antifungal efficacy, and NaClO+H2O2 can potentially serve as a more efficient disinfectant formula for fungal pathogens.

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-R.

Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D2O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.

Periprocedural complications of cardiac implantable electronic device implantation in very elderly patients with cognitive impairment: A prospective study.

ABSTRACT: Very elderly people (over 80 years) with cardiac implantable electronic devices (CIEDs) indications often have a higher prevalence of aging comorbidity, among which cognitive impairment is not uncommon. This study aimed to investigate periprocedural complications of CIED implantation among very elderly patients with and without cognitive impairment. One hundred eighty patients ≥80 years of age indicated for CIED implantation were included in our study. During hospitalization, the cognitive evaluation was performed according to the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). According to the cognitive test results, patients were divided into 2 groups (90 patients with normal cognitive function and 90 patients with cognitive impairment). Meanwhile, their physical parameters and laboratory measurements were completed. The procedural data and periprocedural complications were collected from both groups. The association between cognitive impairment and periprocedural complications was analyzed using univariate and multiple logistic regression analyses. During a one-month follow-up, the most frequent periprocedural complications in very elderly patients were pocket hematoma and thrombosis events. Cognitively impaired patients had a higher incidence of complications than normal cognitive patients. Multivariate regression analysis showed that cognitive impairment was positively correlated with periprocedural complications in very elderly patients. Cognitive impairment is associated with increased periprocedural complications of CIED implantation in very elderly patients.

Schizandrin B attenuates hypoxia/reoxygenation injury in H9c2 cells by activating the AMPK/Nrf2 signaling pathway.

Schizandrin B exhibits prominent antioxidant and anti-inflammatory effects, and plays an important role in ameliorating myocardial ischemia/reperfusion injury. However, the underlying protective mechanisms remain to be elucidated. The aim of the present study was to explore the cardioprotective effects of schizandrin B against hypoxia/reoxygenation (H/R)-induced H9c2 cell injury, focusing on the role of the adenosine monophosphate-activated protein kinase (AMPK)/nuclear factor erythroid 2-related factor 2 (Nrf2) pathway in this process. The results showed that schizandrin B attenuated the H/R-induced decrease in cell viability and the increase in lactate dehydrogenase release, as well as the apoptosis rate in H9c2 cells. Schizandrin B also mitigated H/R-induced oxidative stress, as illustrated by the decrease in intracellular reactive oxygen species generation, malondialdehyde content and NADPH oxidase 2 expression, and the increase in antioxidant enzyme superoxide dismutase and glutathione peroxidase activities. In addition, schizandrin B reversed the H/R-induced upregulation of pro-inflammatory cytokines [interleukin (IL)-1ß (IL-1ß) tumor necrosis factor-α, IL-6 and IL-8] and the downregulation of anti-inflammatory cytokines (transforming growth factor-ß and IL-10) in the culture supernatant. Notably, schizandrin B increased the expression of Nrf2, NAD(P)H: Quinone oxidoreductase (NQO-1) and heme oxygenase-1 (HO-1) in H/R-treated H9c2 cells, activating the Nrf2 signaling pathway. The cardioprotection of schizandrin B against H/R injury was inhibited by Nrf2 knockdown induced byNrf-2-specific small interfering RNA (siRNA; si-Nrf2) transfection. Furthermore, schizandrin B enhanced phosphorylated (p)-AMPK expression, while AMPK knockdown induced by AMPK-specific siRNA(si-AMPK) transfection remarkably eliminated schizandrin B-induced cardioprotection and reduced Nrf2 expression in H/R-treated H9c2 cells. Taken together, these results suggested that schizandrin B exerts cardioprotection on H/R injury in H9c2 cells due to its antioxidant and anti-inflammatory activities via activation of the AMPK/Nrf2 pathway.

Pitavastatin Combined with Ezetimibe Treatment was an Effective Approach to Non-IRA Lesion of ST-segment Elevation Myocardial Infarction Patients with Primary Percutaneous Coronary Intervention.

OBJECTIVE: ST-Segment Elevation Myocardial Infarction (STEMI) patients with the multivessel disease have distinctive plaque characteristics in non-IRA lesions. Intensive statin therapy was a potential approach to treat STEMI patients with the non-IRA disease. However, there is still poor evidence about the therapeutic effect. In this study, we have evaluated the detailed therapeutic effect of statin plus ezetimibe intensive therapy. METHODS: For STEMI patients with non-IRA disease undergoing primary Percutaneous Coronary Intervention (PCI), 183 control STEMI patients without non-IRA disease undergoing primary PCI, and 200 STEMI patients with non-IRA disease undergoing primary PCI were introduced into this study. 200 STEMI patients with non-IRA disease undergoing primary PCI were divided into Normal group, Intensive group, Normal & Combined group, and Intensive & Combined group. The baseline information for each participant was recorded. Meanwhile, the physiological and biochemical indicators of each member with different treatments were collected after one-year follow-up. RESULTS: For STEMI patients with non-IRA disease undergoing primary PCI, no differences could be detected in multiple indexes such as OCT examination results, age, stroke, etc. However, diabetes mellitus, smoking, and coronary Gensini score were different between different groups (P<0.05). After one year follow-up, cholesterol, low-density lipoprotein, coronary Gensini score, thin-cap fibroatheroma, length of non-infarcted arterial lesions, non-infarct artery lesion range, myocardial infarction again, and revascularization again were significantly different between different groups (P<0.05). CONCLUSION: The results mentioned above suggested that pitavastatin combined with ezetimibe was an effective approach for STEMI patients with non-IRA disease undergoing primary PCI. The results obtained in this study have provided a novel method for the treatment of STEMI patients with non-IRA disease undergoing primary PCI.

CircMACF1 Attenuates Acute Myocardial Infarction Through miR-500b-5p-EMP1 Axis.

It is widely accepted that circular RNA (circRNA) plays an important role in cardiovascular diseases. Therefore, this experiment aimed to investigate the pathogenesis of circMACF1 in acute myocardial infarction (AMI). qRT-PCR and immunoblotting were used to detect the expression levels of circMACF1, miR-500b-5p, and epithelial membrane protein 1 (EMP1). The role of circMACF1, miR-500b-5p, and EMP1 in cardiomyocyte apoptosis was assessed using annexin V-FITC/PI. Echocardiographic assessment, serum creatine kinase MB (CK-MB) and lactate dehydrogenase (LDH), myocardial infarct size, and TUNEL staining were applied in our research. In the MI group, the expression levels of circMACF1 and EMP1 were decreased with the increasing expression level of miR-500b-5p. CircMACF1 upregulated the expression of EMP1 as a sponge of miR-500b-5p, and circMACF1 was a direct target of miR-500b-5p. CircMACF1 impaired the progression of AMI by modulating the miR-500b-5p/EMP1 axis. CircMACF1 may be a potential therapeutic target for treating AMI. Graphical Abstract CircMACF1 upregulated EMP1 expression by sponge miR-500b-5p.

Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo.

The potential of Raman-activated cell sorting (RACS) is inherently limited by conflicting demands for signal quality and sorting throughput. Here, we present positive dielectrophoresis-based Raman-activated droplet sorting (pDEP-RADS), where a periodical pDEP force was exerted to trap fast-moving cells, followed by simultaneous microdroplet encapsulation and sorting. Screening of yeasts for triacylglycerol (TAG) content demonstrated near-theoretical-limit accuracy, ~120 cells min-1 throughput and full-vitality preservation, while sorting fatty acid degree of unsaturation (FA-DU) featured ~82% accuracy at ~40 cells min-1. From a yeast library expressing algal diacylglycerol acyltransferases (DGATs), a pDEP-RADS run revealed all reported TAG-synthetic variants and distinguished FA-DUs of enzyme products. Furthermore, two previously unknown DGATs producing low levels of monounsaturated fatty acid-rich TAG were discovered. This first demonstration of RACS for enzyme discovery represents hundred-fold saving in time consumables and labor versus culture-based approaches. The ability to automatically flow-sort resonance Raman-independent phenotypes greatly expands RACS' application.

Cholesterol efflux capacity in coronary artery disease: a meta-analysis.

OBJECTIVE: To quantitatively analyze the association between cholesterol efflux capacity (CEC) and the risk and prognosis of coronary artery disease (CAD). METHODS: A systematic search of electronic databases for studies published until September 2019 was performed. Cohorts, case-control studies, and randomized controlled trials that examined the effect of CEC on the risk and prognosis of CAD were included. RESULTS: Eighteen studies with 12 685 subjects met our inclusion criteria. Among them, 14 studies reported the CEC in non-CAD and CAD groups, and eight studies reported the association between CEC and risk of CAD. Four studies reported the prognosis of stable CAD or acute coronary syndrome (ACS). In the pooled analyses, significantly decreased CEC was found in patients with stable CAD as compared with those without CAD. Decreased CEC was also present in subgroup in patients with ACS. High CEC was significantly associated with decreased risk of CAD [odds ratio (OR) = 0.65, 95% confidence interval (CI): 0.55-0.75, P < 0.001]. High CEC predicted lower all-cause mortality (OR = 0.39, 95% CI: 0.20-0.77, P = 0.007) and cardiovascular mortality (OR = 0.34, 95% CI: 0.13-0.90, P = 0.03) in patients with CAD. However, CEC failed to predict the occurrence of stroke and myocardial infraction in patients with CAD. CONCLUSIONS: Decreased CEC is an independent risk factor for CAD, and it predicts all-cause and cardiovascular mortality in patients with CAD.

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.

Association of Monocyte Count on Admission with the Angiographic Thrombus Burden in Patients with ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention / Associação da Contagem de Monócitos na Internação com a Carga Trombótica Angiográfica em Pacientes com Infarto com Supradesnivelamento do Segmento ST Submetidos à Intervenção Coronária Percutânea Primária

Abstract Background: The intracoronary high-thrombus burden during the primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction (STEMI) can lead to poor outcomes. Monocytes have been described to play an important role in thrombotic disorders. Objectives: This study aimed to investigate the relationship between admission monocyte count and angiographic intracoronary thrombus burden in patients receiving primary percutaneous coronary intervention (PPCI). Methods: A total of 273 patients with acute STEMI who underwent PPCI were enrolled. The patients were divided into two groups according to the thrombolysis in myocardial infarction (TIMI) thrombus grade: low-thrombus burden group with a grade of 0-2 and high-thrombus burden group with a grade of 3-4. The monocyte count and other laboratory parameters were measured on admission before PPCI. P-value < 0.05 was considered significant. Results: There were 95 patients (34.8%) in the high-thrombus burden group, and 178 patients (65.2%) in the low-thrombus burden group. Patients with high-thrombus burden had significantly higher admission monocyte count (0.61 ± 0.29×109/L vs. 0.53 ± 0.24×109/L, p = 0.021). In multivariate analysis, monocyte count was the independent predictor of angiographic high-thrombus burden (odds ratio 3.107, 95% confidence interval [CI] 1.199-7.052, p = 0.020). For the prediction of angiographic high-thrombus burden, admission monocyte count at a cut-off value of 0.48×109/L yielded 0.59 ROC-AUC (71.9% sensitivity, 46.9% specificity). Conclusions: Monocyte count on admission was an independent clinical predictor of high-thrombus burden in patients with STEMI undergoing PPCI. Our findings suggest that admission monocyte count may be available for early risk stratification of high-thrombus burden in acute STEMI patients and might allow the optimization of antithrombotic therapy to improve the outcomes of PPCI.

Resumo Fundamento: A carga trombótica intracoronária durante a intervenção coronária percutânea primária em pacientes com Infarto com Supradesnivelamento do Segmento ST (STEMI) pode levar a resultados negativos.Os monócitos foram descritos para desempenhar um papel importante nos distúrbios trombóticos. Objetivos: Este estudo investigou a relação entre a contagem de monócitos no momento da internação e a carga trombótica angiográfica intracoronária em pacientes submetidos à intervenção coronária percutânea primária (ICPP). Métodos: Um total de 273 pacientes com STEMI agudo submetidos à ICPP participaram. Os pacientes se dividiram em dois grupos de acordo com o grau trombótico na trombólise do infarto do miocárdio (TIMI): grupo baixa carga trombótica, com graus de 0-2, e grupo alta carga trombótica, com graus de 3-4. A contagem de monócitos e outros parâmetros laboratoriais foram medidos na internação antes da ICPP. Consideramos o valor de p < 0,05 significativo. Resultados: Havia 95 pacientes (34,8%) no grupo alta carga trombótica, e 178 pacientes (65,2%) no grupo baixa carga trombótica. Pacientes com alta carga trombótica apresentaram contagem de monócitos no momento da internação mais alta (0,61 ± 0,29×109/L vs. 0,53 ± 0,24×109/L, p = 0,021). Na análise multivariada, a contagem de monócitos foi o indicador independente da alta carga trombótica angiográfica (odds ratio 3,107, intervalo de confiança de 95% [IC] 1,199-7,052, p = 0,020). Para a previsão da alta carga trombótica angiográfica, a contagem de monócitos na internação tinha ponto de corte de 0,48×109/L, chegou a 0.59 ROC-AUC (71,9% sensibilidade, 46,9% especificidade). Conclusões: a contagem de monócitos na internação foi um indicador clínico independente da alta carga trombótica em pacientes com STEMI submetidos à ICPP. Nossos achados sugerem que a contagem de monócitos na internação pode estar disponível para a estratificação de risco precoce da alta carga trombótica em pacientes com STEMI agudo, e podem levar à otimização da terapia antitrombótica para melhorar os resultados da ICPP.