Impact of complete revascularization for acute myocardial infarction with multivessel coronary artery disease in diabetes mellitus patients

Background

Clinical benefits of complete revascularization (CR) in acute myocardial infarction (AMI) patients are unclear. Moreover, the benefit of CR is unknown in AMI with Diabetes Mellitus (DM) patient.

Objectives

We sought to compare prognosis of CR and incomplete revascularization (IR) in patients with AMI and multivessel disease, according to the presence of DM.

Methods

A total of 2,150 AMI patients with multivessel coronary artery disease were analyzed. CR was defined based on angiographic image. The primary endpoints of this study was patient oriented composite outcome (POCO) defined as a composite of all cause death, any myocardial infarction, and any revascularization within 3 years.

Results

Overall, 3-year POCO were significantly lower in patients receiving angiographic CR (985 patients, 45.8%) compared with IR (1165 patients, 54.2%). When divided into subgroups according to the presence of DM, CR reduced 3-year clinical outcomes in the non-DM group but not in the DM group (POCO: 11.7% vs. 23.2%, p<0.001, any revascularization: 7.2% vs. 10.8%, p=0.024 in the non-DM group, POCO: 24.3% vs. 27.8%, p=0.295, any revascularization: 13.3% vs. 11.3%, p=0.448 in the DM group, for CR vs. IR). Multivariate analysis showed that CR significantly reduced 3-year POCO (HR 0.52, 95% CI 0.38–0.71) only in the non-DM group.

Conclusion

In AMI patients with multivessel disease, CR may be ineffective in improving clinical outcomes in patients with DM.

Funding Acknowledgement

Type of funding source: None

Predictors of mortality in patients with VA-extracorporeal membrane oxygenation

Background

Venoarterial extracorporeal membrane oxygenation (VA-ECMO) support is a lifesaving tool used in the treatment of cardiogenic shock, acute heart failure, or extracorporeal cardiopulmonary resuscitation (CPR). We report on a single center experience with ECMO and aim to identify the prognostic markers for in-hospital mortality and death at 72 hours after ECMO.

Methods

Between 2011 and 2019 we evaluated 131 patients, who received ECMO. Collected data was analyzed to identify baseline characteristic, outcomes including clinical variables predictive of poor outcome.

Results

The mean age was 62.5 years, 67.2% were male patients, with prior CPR in 61.8%. The annual number of VA-ECMO procedures steadily increased, whereas in-hospital mortality is decreasing. Within the total cohort, the indication for VA-ECMO was cardiac arrest in 19.1%, acute coronary syndrome in 41.2%, acute heart failure in 23.7%, and myocarditis in 10.7%.

Overall in-hospital mortality was 58.8%. Multivariate logistic regression model revealed presence of malignancy, history of revascularization, duration of cardiac arrest, and low BMI as independent predictors for mortality in 72 hours after ECMO (table). On the other hand, predictors of in-hospital mortality were prior congestive heart failure, male, and history of malignancy. The C-statistic for discriminating mortality in 72 hours after ECMO with the duration of cardiac arrest was 0.67 (figure).

Conclusions

Although the use of ECMO as a last line in the treatment of critical patients measures constitutes an important improvement in their care; with 41.2% overall survival; patient selection and timing of ECMO initiation remains challenging. The importance of consideration for ECMO use earlier in course of illness rather than later.

Funding Acknowledgement

Type of funding source: None

Intense cardiac-targeted small extracellular vesicles-mediated delivery of RAGE siRNA attenuates inflammation in rat myocarditis model

Background

Despite advances in the field and new therapeutics being developed, heart disease remains the leading cause of mortality worldwide. Small extracellular vesicles (sEV) are 30–150 nm in diameter and ferry RNA and proteins to cells. sEV are a natural carrier of many signaling molecules and play an important role in heart disease. However, the high probability of off-target effects associated with these carriers is a major barrier to translation into clinical application.

Purpose

Our aim was to evaluate whether sEVs engineered to express potent cardiac targeting peptides (CTPs) could deliver siRNA to the heart and exert a therapeutic effect.

Methods

We use vectors encoding LAMP2B (sEV) or CTP-LAMP2B (PsEV) into HEK 293 cells expressing. sEVs were purified from culture media of HEK 293 cells by serial centrifugation followed by tangential flow filtration (TFF) system. sEV and PsEV were loaded with siRNAs by Exo-Fect™ exosome transfection reagent, and were treated into H9C2 rat cardiomyocyte. TNF-alpha were then added to the cells to induce inflammation. And sEVs were intravenously injected into myocarditis rat. Inflammation factors of in vitro and in vivo inflammation model were identified by western blot. Echocardiographic examination was also performed in rat.

Results

The successful development of PsEVs was analyzed by Western blot and TEM. We observed a 4 fold increase from that of the previously developed sEVs in H9C2 cells and a 200% increase in cardiac-specific delivery without toxicity in rat model. AGE is involved in proinflammatory/pro-apoptotic processes. To block RAGE, we loaded RAGE siRNA (siRAGE) in sEV that had high expression of PsEV. Characteristics of sEVs were maintained despite siRNA load.In inflammatory cell models and rat disease models, PsEV-treated groups significantly reduced molecular levels associated with inflammatory responses such as RAGE, IL-6, TNF-alpha, COX2, HMGB1, and p-p65 / p65.

Conclusions

Our results suggest that PsEV can potentially serve as a treatment delivery vehicle for heart disease.

Scheme

Funding Acknowledgement

Type of funding source: None

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

BACKGROUND

Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin (H&E) sections in diverse tumors including colorectal cancers (CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time- and cost-effective complementary method for personalized treatment.

AIM

To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images (WSIs) with deep learning-based classifiers.

METHODS

A total of 629 CRC patients from The Cancer Genome Atlas (TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital (SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic (ROC) curves and area under the curves (AUCs) for all the classifiers were presented.

RESULTS

The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs. The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved.

CONCLUSION

APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.

Melatonin regulates mitochondrial function and biogenesis during rat dental papilla cell differentiation

OBJECTIVE

The aim of this study was to investigate the effect of melatonin on mitochondria of dental papilla cells (DPCs) during the odontogenic differentiation process.

MATERIALS AND METHODS

Primary DPCs were obtained from the first molar dental papilla of neonatal rats and cultured in osteogenic (OS) or basal medium supplemented with melatonin at different concentrations (0, 1 pM, 0.1 nM, 10 nM, and 1 μM) for differentiation in vitro. Effects of melatonin on differentiation, mitochondrial respiratory function, and mitochondrial biogenesis of DPCs were analyzed.

RESULTS

Upon odontogenic induction, Alkaline phosphatase (ALP) activity, dentin sialophosphoprotein (DSPP), and dentin matrix protein (DMP1) expression were significantly enhanced, with a peaked expression at 10 nM of melatonin treatment. During DPCs differentiation, 10 nM melatonin could significantly induce the increase of intracellular Adenosine triphosphate (ATP), the decrease of the oxidized form of nicotinamide adenine dinucleotide (NAD+)/NADH ratio and reactive oxygen species (ROS). The mRNA and protein levels of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), nuclear respiratory factor 1 (NRF-1), and mitochondrial transcription factor A (TFAM) were significantly increased, and the peak level of expression was found in cells treated with 10 nM of melatonin. Furthermore, the mitochondria DNA (mtDNA) copy number was significantly decreased during DPCs differentiation.

CONCLUSIONS

These findings suggest that melatonin can promote the differentiation of rat DPCs and regulate mitochondrial energy metabolism, ROS scavenging, and mitochondrial biogenesis.

Association between immunotherapy biomarkers and glucose metabolism from F-18 FDG PET

OBJECTIVE

To assess associations between parameters derived from F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and mRNA expression levels of immune checkpoint biomarkers such as programmed death receptor 1 (PD-1), programmed death-ligand 1 (PD-L1), cytotoxic T-lymphocyte antigen 4 (CTLA-4) as well as tumor mutation burden (TMB) in non-small cell lung cancer (NSCLC) patients.

PATIENTS AND METHODS

Integrated data were downloaded from Genomic Data Common Data Portal. Clinical, mRNA-seq, and whole exome-seq data of lung adenocarcinoma and squamous cell carcinoma from The Cancer Genome Atlas (TCGA) database were analyzed. TMB was defined as the total number of somatic missense mutations per megabase of the genome examined. Expression levels of PD-1, PD-L1, CTLA4 mRNA and TMB were collected. Correlations between imaging parameters of glucose metabolism and the expression levels of genomic biomarkers from cancers were evaluated. Bonferroni correction (adjusted p<0.0027) was applied to reduce type 1 error.

RESULTS

Of 31 NSCLC cases, 11 cases were adenocarcinoma (LUAD) and 20 were squamous cell carcinoma (LUSC). In linear regression analysis, texture parameters such as low gray-level run emphasis (LGRE, R2=0.48, p<0.0001), short run low gray-level emphasis (SRLGE, R2=0.45, p<0.0001) and long run low gray-level emphasis (LRLGE, R2=0.41, p=0.0001) derived from gray-level run length matrix (GLRLM) showed remarkable correlation with PD-L1 mRNA expression. Expression of PD-1, CTLA-4, and TMB failed to show any significant correlation with parameters of the F-18 FDG PET/CT.

CONCLUSIONS

Texture parameters derived from PET, known to indicate glucose uptake distribution, were correlated with expression of PD-L1 mRNA but not with expression of PD-1, CTLA-4 and TMB. Thus, tumoral heterogeneity could be a surrogate marker for the identification of PD-L1 level in NSCLC.

The Lq- NORM LEARNING FOR ULTRAHIGH-DIMENSIONAL SURVIVAL DATA: AN INTEGRATIVE FRAMEWORK

In the era of precision medicine, survival outcome data with high-throughput predictors are routinely collected. Models with an exceedingly large number of covariates are either infeasible to fit or likely to incur low predictability because of overfitting. Variable screening is key in identifying and removing irrelevant attributes. Recent years have seen a surge in screening methods, but most of them rely on some particular modeling assumptions. Motivated by a study on detecting gene signatures for multiple myeloma patients' survival, we propose a model-free L q -norm learning procedure, which includes the well-known Cramér-von Mises and Kolmogorov criteria as two special cases. The work provides an integrative framework for detecting predictors with various levels of impact, such as short- or long-term impact, on censored outcome data. The framework naturally leads to a scheme which combines results from different q to reduce false negatives, an aspect often overlooked by the current literature. We show that our method possesses sure screening properties. The utility of the proposal is confirmed with simulation studies and an analysis of the multiple myeloma study.