Development of drug loaded cardiovascular prosthesis for thrombosis prevention using 3D printing

Cardiovascular disease (CVD) is a general term for conditions which are the leading cause of death in the world. Quick restoration of tissue perfusion is a key factor to combat these diseases and improve the quality and duration of patients' life. Revascularization techniques include angioplasty, placement of a stent, or surgical bypass grafting. For the latter technique, autologous vessels remain the best clinical option; however, many patients lack suitable autogenous due to previous operations and they are often unsuitable. Therefore, synthetic vascular grafts providing antithrombosis, neointimal hyperplasia inhibition and fast endothelialization are still needed. To address these limitations, 3D printed dipyridamole (DIP) loaded biodegradable vascular grafts were developed. Polycaprolactone (PCL) and DIP were successfully mixed without solvents and then vascular grafts were 3D printed. A mixture of high and low molecular weight PCL was used to better ensure the integration of DIP, which would offer the biological functions required above. Moreover, 3D printing technology provides the ability to fabricate structures of precise geometries from a 3D model, enabling to customize the vascular grafts' shape or size. The produced vascular grafts were fully characterized through multiple techniques and the last step was to evaluate their drug release, antiplatelet effect and cytocompatibility. The results suggested that DIP was properly mixed and integrated within the PCL matrix. Moreover, these materials can provide a sustained and linear drug release without any obvious burst release, or any faster initial release rates for 30 days. Compared to PCL alone, a clear reduced platelet deposition in all the DIP-loaded vascular grafts was evidenced. The hemolysis percentage of both materials PCL alone and PCL containing 20% DIP were lower than 4%. Moreover, PCL and 20% DIP loaded grafts were able to provide a supportive environment for cellular attachment, viability, and growth

Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering

Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K-MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K-means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report to analyze recognition results. The proposed methodology will be illustrated by accident data in Chinese rural area in 2017. A grid-based partition was carried on by using intersection as a basic spatial unit. Appended hazard scores were then added to the segments and using K-means clustering, a result of similar hotspots was completed. The accuracy of the results is verified by the analysis of the cause extracted by Fuzzy C-means algorithm (FCM)

Increased plasma homocysteine levels are associated with left ventricular hypertrophy in hypertensive patients with normal renal function.

Introduction: Renal function has an important bearing on plasma homocysteine levels. Plasma homocysteine is related to left ventricular hypertrophy (LVH). However, it remains unclear whether the association between plasma homocysteine levels and LVH is influenced by renal function. This study aimed to investigate relationships among left ventricular mass index (LVMI), plasma homocysteine levels and renal function in a population from southern China. Methods: A cross-sectional study was performed in 2464 patients from June 2016 to July 2021. Patients were divided into three groups based on gender-specific tertiles of homocysteine levels. LVMI ≥ 115 g/m2 for man, or ≥ 95 g/m2 for woman was defined as LVH. Results: LVMI and the percentage of LVH were increased, while estimated glomerular filtration rate (eGFR) was decreased with the increase in homocysteine levels, both significantly. Multivariate stepwise regression analysis showed that eGFR and homocysteine were independently associated with LVMI in patients with hypertension. No correlation was observed between homocysteine and LVMI in patients without hypertension. Stratified by eGFR, further analysis confirmed homocysteine was independently associated with LVMI (β = 0.126, t = 4.333, P < 0.001) only in hypertensive patients with eGFR ≥ 90 mL/(min·1.73m2), not with 60 ≤ eGFR < 90mL/(min·1.73m2). Multivariate logistic regression indicated that in hypertensive patients with eGFR ≥ 90 mL/(min·1.73m2), the patients in high tertile of homocysteine levels had a nearly twofold increased risk of occurring LVH compared with those in low tertile (high tertile: OR = 2.780, 95% CI: 1.945 - 3.975, P < 0.001). Conclusion: Plasma homocysteine levels were independently associated with LVMI in hypertensive patients with normal eGFR

Structural basis of GABAB receptor–Gi protein coupling

International audienceG-protein-coupled receptors (GPCRs) have central roles in intercellular communication1,2. Structural studies have revealed how GPCRs can activate G proteins. However, whether this mechanism is conserved among all classes of GPCR remains unknown. Here we report the structure of the class-C heterodimeric GABAB receptor, which is activated by the inhibitory transmitter GABA, in its active form complexed with Gi1 protein. We found that a single G protein interacts with the GB2 subunit of the GABAB receptor at a site that mainly involves intracellular loop 2 on the side of the transmembrane domain. This is in contrast to the G protein binding in a central cavity, as has been observed with other classes of GPCR. This binding mode results from the active form of the transmembrane domain of this GABAB receptor being different from that of other GPCRs, as it shows no outside movement of transmembrane helix 6. Our work also provides details of the inter- and intra-subunit changes that link agonist binding to G-protein activation in this heterodimeric complex

When structured light encounters liquid crystals

Structured light refers to the light field tailored by various degrees of freedom including intensity, phase, and polarization states in both spatial and temporal domains, which may greatly vitalize the technologies in both optics, such as the next-generation optical communication as well as subwavelength imaging and the materials science in both fabrication and characterization. The structured characteristics of the structure light need materials also with structured optical properties that can generate or manipulate structured light in a straightforward way, which can be well satisfied by liquid crystals, a soft mater that can self-assemble into tunable ordered structures through external stimuli. This review summarizes the research progress of the liquid crystal-based devices used in structured light generations and modulations, including the well-established techniques in the market, like the spatial light modulator, q-plate and the liquid crystal integrated optical metasurfaces. Especially, light-matter interactions are discussed from the topological view of both the structured light and the liquid crystal structures. Such a perfect matching in topology makes the liquid crystal a promising star together with structured light in future optic and photonic technologies

Characterizing Drug–Target Residence Time with Metadynamics: How To Achieve Dissociation Rate Efficiently without Losing Accuracy against Time-Consuming Approaches

Drug–target residence time plays a vital role in drug efficacy. However, there is still no effective strategy to predict drug residence time. Here, we propose to use the optimized (or minimized) structures derived from <i>holo</i>-state proteins to calculate drug residence time, which could give a comparable or even better prediction accuracy compared with those calculated utilizing a large number of molecular dynamics (MD) structures based on the Poisson process. Besides, in addition to the Poisson process, one may use fewer samples for predicting residence time due to the reason that, in a large extent, the calculated drug residence time is stable and independent of the number of samples used for the prediction. With remarkably reduced computational load, the proposed strategy may be promising for large-scale drug residence time prediction, such as post-processing in virtual screening (VS) and lead compound optimization

Use of 3D Printing for the Development of Biodegradable Antiplatelet Materials for Cardiovascular Applications

Small-diameter synthetic vascular grafts are required for surgical bypass grafting when there is a lack of suitable autologous vessels due to different reasons, such as previous operations. Thrombosis is the main cause of failure of small-diameter synthetic vascular grafts when used for this revascularization technique. Therefore, the development of biodegradable vascular grafts capable of providing a localized and sustained antithrombotic drug release mark a major step forward in the fight against cardiovascular diseases, which are the leading cause of death globally. The present paper describes the use of an extrusion-based 3D printing technology for the production of biodegradable antiplatelet tubular grafts for cardiovascular applications. For this purpose, acetylsalicylic acid (ASA) was chosen as a model molecule due to its antiplatelet activity. Poly(caprolactone) and ASA were combined for the fabrication and characterization of ASA-loaded tubular grafts. Moreover, rifampicin (RIF) was added to the formulation containing the higher ASA loading, as a model molecule that can be used to prevent vascular prosthesis infections. The produced tubular grafts were fully characterized through multiple techniques and the last step was to evaluate their drug release, antiplatelet and antimicrobial activity and cytocompatibility. The results suggested that these materials were capable of providing a sustained ASA release for periods of up to 2 weeks. Tubular grafts containing 10% (w/w) of ASA showed lower platelet adhesion onto the surface than the blank and grafts containing 5% (w/w) of ASA. Moreover, tubular grafts scaffolds containing 1% (w/w) of RIF were capable of inhibiting the growth of Staphylococcus aureus. Finally, the evaluation of the cytocompatibility of the scaffold samples revealed that the incorporation of ASA or RIF into the composition did not compromise cell viability and proliferation at short incubation periods (24 h)

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website