Platelet-like particles (PLPs), derived from megakaryocytic cell lines MEG-01 and K-562, are widely used as a surrogate to study platelet formation and function. We demonstrate by RNA-Seq that PLPs are transcriptionally distinct from platelets. Expression of key genes in signaling pathways promoting platelet activation/aggregation, such as the PI3K/AKT, protein kinase A, phospholipase C, and α-adrenergic and GP6 receptor pathways, was missing or under-expressed in PLPs. Functionally, PLPs do not aggregate following epinephrine, collagen, or ADP stimulation. While PLPs aggregated in response to thrombin, they did not display enhanced expression of surface markers P-selectin and activated α2bβ3, in contrast to platelets. We have previously demonstrated that platelets physically couple to MDA-PCa-2b and RC77T/E prostate cancer (PCa) cells via specific ligand-receptor interactions, leading to platelet-stimulated cell invasiveness and apoptotic resistance, and reciprocal cell-induced platelet aggregation. In contrast, PLP interactions with PCa cells inhibited both cell invasion and apoptotic resistance while failing to promote PLP aggregation. Moreover, PLPs reduced platelet-PCa cell interactions and antagonized platelet-stimulated oncogenic effects in PCa cells. RNA-Seq analysis identified candidate ligand-transmembrane protein combinations involved in anti-tumorigenic signaling of PLPs to PCa cells. Antibody neutralization of the TIMP3-MMP15 and VEGFB-FGFR1 signaling axes reversed PLP-mediated anti-invasion and apoptotic sensitization, respectively. In summary, PLPs lack many transcriptomic, molecular and functional features of platelets and possess novel anti-tumorigenic properties. These findings indicate that PLPs may have a potential therapeutic role in targeting and disrupting the oncogenic signaling between platelets and cancer cells, offering a new avenue for anti-cancer strategies.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as

AIMS:We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA).METHODS:CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up.RESULTS:Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively.CONCLUSIONS:In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Platelets play a crucial role in cancer and thrombosis. However, the receptor-ligand repertoire mediating prostate cancer (PCa) cell-platelet interactions and ensuing consequences have not been fully elucidated. Microvilli emanating from the plasma membrane of PCa cell lines (RC77 T/E, MDA PCa 2b) directly contacted individual platelets and platelet aggregates. PCa cell-platelet interactions were associated with calcium mobilization in platelets, and translocation of P-selectin and integrin αIIbβ3 onto the platelet surface. PCa cell-platelet interactions reciprocally promoted PCa cell invasion and apoptotic resistance, and these events were insensitive to androgen receptor blockade by bicalutamide. PCa cells were exceedingly sensitive to activation by platelets in vitro, occurring at a PCa cell:platelet coculture ratio as low as 1:10 (whereas PCa patient blood contains 1:2,000,000 per ml). Conditioned medium from cocultures stimulated PCa cell invasion but not apoptotic resistance nor platelet aggregation. Candidate transmembrane signaling proteins responsible for PCa cell-platelet oncogenic events were identified by RNA-Seq and broadly divided into 4 major categories: (1) integrin-ligand, (2) EPH receptor-ephrin, (3) immune checkpoint receptor-ligand, and (4) miscellaneous receptor-ligand interactions. Based on antibody neutralization and small molecule inhibitor assays, PCa cell-stimulated calcium mobilization in platelets was found to be mediated by a fibronectin1 (FN1)-αIIbβ3 signaling axis. Platelet-stimulated PCa cell invasion was facilitated by a CD55-adhesion G protein coupled receptor E5 (ADGRE5) axis, with contribution from platelet cytokines CCL3L1 and IL32. Platelet-stimulated PCa cell apoptotic resistance relied on ephrin-EPH receptor and lysophosphatidic acid (LPA)-LPA receptor (LPAR) signaling. Of participating signaling partners, FN1 and LPAR3 overexpression was observed in PCa specimens compared to normal prostate, while high expression of CCR1 (CCL3L1 receptor), EPHA1 and LPAR5 in PCa was associated with poor patient survival. These findings emphasize that non-overlapping receptor-ligand pairs participate in oncogenesis and thrombosis, highlighting the complexity of any contemplated clinical intervention strategy.

Introduction: Applying atherosclerosis imaging quantitative coronary computed tomography angiography (AI-QCT) guided by artificial intelligence, we hypothesize that use of AI-QCT may enable identification of plaque thresholds that prognosticate major adverse cardiovascular events (MACE).

A doença cardiovascular aterosclerótica (DCVA) é uma doença sistêmica iniciada por um influxo endotelial de partículas lipídicas, incluindo lipoproteínas de baixa densidade (LDL), com subsequente ativação endotelial via recrutamento local de células inflamatórias. Esse processo local– desencadeado pela exposição determinada pela idade a fatores de risco da DCVA genéticos, ambientais e de estilo de vida– é o primeiro passo de um processo que levará a um estado inflamatório sistêmico crônico e de baixo grau. A exposição prolongada do endotélio aos [...]

Introduction: Intermediate-term outcomes of cardiac computed tomography angiography (CCTA) in low to intermediate risk acute coronary syndromes (ACS) is uncertain. There is scarce data on associated plaque findings of atherosclerosis imaging-quantitative computed tomography (AI-QCT) guided by artificial intelligence in these patients

Introduction: Atherosclerosis imaging applied to quantitative computed tomography (AI-QCT) now allows for rapid, accurate assessment of coronary vessel morphology, stenosis and atherosclerosis evaluation through artificial intelligence. AI-QCT may improve identification of patients that would benefit from preventive therapies beyond current guidelines.

Short Editorial related to the article: Epicardial Fat Volume Is Associated with Endothelial Dysfunction, but not with Coronary Calcification: From the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 +/- 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (>= 50%) stenosis in 54% of patients, including severe (>= 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p<.001) than MPI for predicting >= 50% stenosis by QCA (0.88 vs 0.66), >= 70% stenosis by QCA (0.92 vs 0.81), and FFR<0.80 (0.90 vs 0.71). An AI-QCT result of >= 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting >= 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AI-QCT in all patients and those showing >= 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with >= 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs.

The African American (AA) population displays a 1.6 to 3-fold higher incidence of thrombosis and stroke mortality compared with European Americans (EAs). Current antiplatelet therapies target the ADP-mediated signaling pathway, which displays significant pharmacogenetic variation for platelet reactivity. The focus of this study was to define underlying population differences in platelet function in an effort to identify novel molecular targets for future antiplatelet therapy. We performed deep coverage RNA-Seq to compare gene expression levels in platelets derived from a cohort of healthy volunteers defined by ancestry determination. We identified > 13,000 expressed platelet genes of which 480 were significantly differentially expressed genes (DEGs) between AAs and EAs. DEGs encoding proteins known or predicted to modulate platelet aggregation, morphology, or platelet count were upregulated in AA platelets. Numerous G-protein coupled receptors, ion channels, and pro-inflammatory cytokines not previously associated with platelet function were likewise differentially expressed. Many of the signaling proteins represent potential pharmacologic targets of intervention. Notably, we confirmed the differential expression of cytokines IL32 and PROK2 in an independent cohort by quantitative real-time polymerase chain reaction, and provide functional validation of the opposing actions of these two cytokines on collagen-induced AA platelet aggregation. Using Genotype-Tissue Expression whole blood data, we identified 516 expression quantitative trait locuses with Fst values > 0.25, suggesting that population-differentiated alleles may contribute to differences in gene expression. This study identifies gene expression differences at the population level that may affect platelet function and serve as potential biomarkers to identify cardiovascular disease risk. Additionally, our analysis uncovers candidate novel druggable targets for future antiplatelet therapies.

The Clinical Pharmacogenetics Implementation Consortium (CPIC®) establishes evidence-based guidelines for utilizing pharmacogenetic information for certain priority drugs. Warfarin, clopidogrel and simvastatin are cardiovascular drugs that carry strong prescribing guidance by CPIC. The respective pharmacogenes for each of these drugs exhibit considerable variability amongst different ethnic/ancestral/racial populations. Race and ethnicity are commonly employed as surrogate biomarkers in clinical practice and can be found in many prescribing guidelines. This is controversial due to the large variability that exists amongst different racial/ethnic groups, lack of detailed ethnic information and the broad geographic categorization of racial groups. Using a retrospective analysis of electronic health records (EHR), we sought to determine the degree to which self-reported race/ethnicity contributed to the probability of adverse drug reactions for these drugs. All models used individuals self-reporting as White as the comparison group. The majority of apparent associations between different racial groups and drug toxicity observed in the "race only" model failed to remain significant when we corrected for covariates. We did observe self-identified Asian race as a significant predictor (p = 0.016) for warfarin hemorrhagic events in all models. In addition, patients identifying as either Black/African-American (p = 0.001) or Other/Multiple race (p = 0.019) had a lower probability of reporting an adverse reaction than White individuals while on simvastatin even after correcting for other covariates. In both instances where race/ethnicity was predictive of drug toxicity (i.e., warfarin, simvastatin), the findings are consistent with the known global variability in the pharmacogenes described in the CPIC guidelines for these medications. These results confirm that the reliability of using self-identified race/ethnic information extracted from EHRs as a predictor of adverse drug reactions is likely limited to situations where the genes influencing drug toxicity display large, distinct ethnogeographic variability.