Haemodynamic mechanism of formation and distribution of coronary atherosclerosis: A lesion-specific model

Accurate adventitia reconstruction; significant or not in atherosclerotic plaque growth simulationsƒ A comparative study

A reform in the diagnosis and treatment process is urgently required as carotid artery disease remains a leading cause of death in the world. To this purpose, all computational techniques are now being applied to enhancing the most cutting-edge diagnosis techniques. Computational modeling of plaque generation and evolution is being refined over the past years to forecast the atherosclerotic progression and the corresponding risk in patient-specific carotid arteries. A prerequisite to their implementation is the reconstruction of the precise three-dimensional models of patient-specific main carotid arteries. Even with the most sophisticated algorithms, accurate reconstruction of the arterial vessel is frequently difficult. Furthermore, there are several works of plaque growth modeling that ignore the reconstruction of the artery's outer layer in favor of a virtual one. In this paper, we investigate the importance of an accurate adventitia layer in plaque growth modeling. This is done as a comparative study by implementing a novel plaque growth model in two reconstructed carotid arterial segments using either their realistic or virtual adventitia layer as input. The results indicate that accurate adventitia reconstruction is of minor importance regarding species distributions and plaque growth in carotid segments, which initially did not contain any plaque regions.Clinical Relevance- The findings of this comparative study emphasize the importance of precise adventitia geometry in plaque growth modeling. As a result, this work sets a higher standard for publishing new plaque growth models.

Pharmacological Mechanisms of Tinglizi against Chronic Heart Failure Determined by Network Pharmacology and Molecular Docking

Objective. Tinglizi has been extensively used to treat chronic heart failure (CHF) in modern times, but the material basis and pharmacological mechanisms are still unclear. To explore the material basis and corresponding potential targets and to elucidate the mechanism of Tinglizi, network pharmacology and molecular docking methods were utilized. Methods. The main chemical compounds and potential targets of Tinglizi were collected from the pharmacological database analysis platform (TCMSP). The corresponding genes of related action targets were queried through gene cards and UniProt database. The corresponding genes of CHF-related targets were searched through Disgenet database, and the intersection targets were obtained by drawing Venn map with the target genes related to pharmacodynamic components. Then, drug targets and disease targets were intersected and put into STRING database to establish a protein interaction network. The “active ingredient-CHF target” network was constructed with Cytoscape 3.8.2. Finally, Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of intersection targets were analyzed using metascape. With the aid of SYBYL software, the key active ingredients and core targets were docked at molecular level, and the results were visualized by PyMOL software. Molecular docking was carried out to investigate interactions between active compounds and potential targets. Results. A total of 12 active components in Tinglizi were chosen from the TCMSP database, and 193 corresponding targets were predicted. Twenty-nine potential targets of Tinglizi on CHF were obtained, of which nine were the core targets of this study. Twenty GO items were obtained by GO function enrichment analysis ( $P<0.05$ ), and 10 signal pathways were screened by KEGG pathway enrichment analysis ( $P<0.05$ ), which is closely related to the treatment of CHF by Tinglizi. The constructed drug compound composition action target disease network shows that quercetin, kaempferol, and other active compounds play a key role in the whole network. The results of molecular docking showed that all the key active ingredients, such as quercetin and isorhamnetin, were able to successfully dock with ADRB2 and HMOX1 with a total score above 5.0, suggesting that these key components have a strong binding force with the targets. Conclusion. Through network pharmacology and molecular docking technology, we found that the main components of Tinglizi in the treatment of CHF are quercetin, kaempferol, β-sitosterol, isorhamnetin, and so on. The action targets are beta 2-adrenergic receptor (ADRB2), heme oxygenase 1 (HMOX1), and so on. The main pathways are advanced glycation end products/receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications, hypoxia-inducible factor (HIF-1) signaling pathway, estrogen signaling pathway, and so on. They play an integrated role in the treatment of CHF.

Incidence and Predictors of Stent Thrombosis in Patients Treated with Stents for Coronary Bifurcation Narrowing (From the BIFURCAT Registry)

Percutaneous coronary interventions performed at coronary bifurcations yield high rates of stent thrombosis (ST). The aim of the present study was to investigate the predictors of ST in contemporary coronary bifurcation percutaneous coronary interventions. We retrospectively investigated the BIFURCAT (comBined Insights From the Unified RAIN and COBIS bifurcAtion regisTries) registry on coronary bifurcations to assess the incidence and predictors of definite ST, which were the study primary endpoints. Predictors of ST among patients on dual antiplatelet therapy (DAPT) were also examined. A total of 5330 patients were included. After a mean 2-years follow-up, 64 (1.2%) patients experienced ST. 42 (65.6%) ST patients were on DAPT. At multivariable analysis, age (HR 1.02, CI 1.01 to 1.05, p = 0,027), smoking status (HR 2.57, CI 1.49 to 4.44, p = 0.001), chronic kidney disease (HR 2.26, CI 1.24 to 4.12, p = 0.007) and a 2-stent strategy (HR 2.38, CI 1.37 to 4.14, p = 0.002) were independent predictors of ST, whereas intracoronary imaging (HR 0.42, CI 0.23 to 0.78, p = 0.006) and final kissing balloon (FKB) (HR 0.48, CI 0.29 to 0.82, p = 0.007) were protective against ST. Among patients on DAPT, smoking status and a 2-stent strategy significantly increased the risk of ST, while intracoronary imaging and FKB reduced the risk. In conclusion, age, smoking status, chronic kidney disease and a 2-stent strategy were significant predictors of ST, whereas intracoronary imaging use and FKB had a protective effect. Only smoking status and a 2-stent strategy significantly predicted ST in DAPT subgroup, while intracoronary imaging and FKB had a protective role.

The composition of vulnerable plaque and its effect on arterial waveforms

Carotid plaque composition is a key factor of plaque stability and it carries significant prognostic information. The carotid unstable plaques are characterized by a thin fibrous cap (FC) ≤65μm with large lipid core (LC), while stable plaques have a thicker FC and less LC. Identifying the percentage of plaque compositions could help surgeons to make a precise decision for their patients' treatment protocol. This study aims to distinguish between stable and unstable plaque by defining the relationship between plaque composition and arterial waveform non-invasively. An in-vitro arterial system, composed of a Harvard pulsatile flow pump and artificial circulation system, was used to investigate the effect of the plaque compositions on the pulsatile arterial waveforms. Five types of arterial plaques, composed of the LC, FC, Collagen (Col) and Calcium (Ca), were implemented into the artificial carotid artery to represent the diseased arterial system with 30% of blockage. The pulsatile pressure, velocity and arterial wall movement were measured simultaneously at the site proximal to the plaque. Non-invasive wave intensity analysis (Non-WIA) was used to separate the waves into forward and backward components. The correlation between the plaque compositions and the reflected waveforms was quantitatively analysed. The experimental results indicate that the reflected waveforms are strongly correlated with the plaque compositions, where the percentages of the Col are linearly correlated with the amplitude of the backward diameter (correlation coefficient, r = 0.74) and the lipid content has a strong negative correlation with the backward diameter (r = 0.82). A slight weak correlation exists between the reflected waveform and the percentage of Ca. The strong correlation between the compositions of the plaques with the backward waveforms observed in this study demonstrates that the components of the arterial plaques could be distinguished by the arterial waveforms. This finding might lead to a potential novel non-invasive clinical tool to determine the composition of the plaques and distinguish between stable and vulnerable arterial plaques at the early stage.