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The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care., Graphical Abstract Artificial intelligence-enhanced arrhythmia care.

A continuous processing platform was developed to produce polymeric micelles. A block copolymer of mPEG (5kD)-PCL (2kD) was used as the model drug carrier. The polymeric micelles were produced using an innovative co-axial turbulent jet with co-flow continuous technology to precisely control the physicochemical properties of the micelles. A 3 × 3 × 4 full factorial design of experiment (DoE) study was conducted to optimize the polymeric micelle processing to achieve the desired critical quality attributes such as particle size and polydispersity index (PDI). Curcumin was used as a hydrophobic model drug as polymeric micelles are traditionally used to improve solubility and chemical stability of hydrophobic drug molecules. A second DoE study was conducted to achieve maximal drug loading. The average size of the optimized curcumin-loaded polymeric micelles was 29.1 ± 0.51 d.nm with a PDI value in the range of 0.05 ± 0.02 and a maximum drug loading of 11.1 ± 0.81% (w/w). When compared to polymeric micelles prepared using a manual ethanol injection method the particle size, PDI and drug loading for curcumin-loaded polymeric micelles was 74.8 ± 8.68 d.nm, and 0.46 ± 0.12 and 8.12 ± 1.23%, respectively. These data show that the continuous processing method provided significant improvement in controlling the key quality attributes. The curcumin-loaded polymeric micelles exhibited a sustained release profile during dissolution of about 50% drug released in 12 h compared to the free drug, which was completely released within 10 h. The curcumin-loaded polymeric micelles were characterized for other key quality attributes such as critical micelle concentration (CMC), and morphology by transmission electron microscopy, X-ray powder crystallography, polarized light microscopy, and differential scanning calorimetry. A novel method for determining the CMC of the polymer was developed using dynamic light scattering (DLS). Curcumin-loaded polymeric micelles were further processed by lyophilization to prevent hydrolytic cleavage of the polymers and maintain long term stability. The current study highlighted the potential advantages of transitioning from manual batch processing to continuous processing and serves as an example of improving processing efficiency as well as product quality through utilization of advanced processing technologies.

The United States FDA has received over 800 botanical investigational new drug applications (IND) and pre-IND meeting requests (PIND) in the years preceding 2018. The current data show that indications for submitted INDs cover nearly every review division of the FDA. Despite increasing global interest in the investigation of botanical mixtures as drug products, only two botanical new drug applications (NDA) have been approved in the U.S.: Veregen in 2006 and Fulyzaq (also known as Mytesi) in 2012. Given botanicals' chemical and biological complexity, efforts in characterizing their pharmacology, demonstrating therapeutic efficacy, and ensuring quality consistency remain scientific and regulatory challenges. The FDA published a revised

Personalised treatment of heart disease requires an understanding of the patient-specific characteristics, which can vary over time. A newly developed implantable surface acoustic wave pressure sensor, capable of continuous monitoring of the left ventricle filling pressure, is a novel device for personalised management of patients with heart disease. However, a one-size-fits-all approach to device sizing will affect its positioning within the pulmonary artery and its relationship to the interrogating device on the chest wall on a patient-specific level. In this paper, we analyse the spatial orientation and morphology of the pulmonary artery and its main branches in patients who could benefit from the device and normal controls. The results could optimise the design of the sensor, its stent, and importantly its placement, ensuring long-term monitoring in patient groups.

Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently navigated by 2D fluoroscopy which is insufficiently informative. Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a single fluoroscopy projection in real-time, which could help 3D FEVAR navigation and robotic path planning. This proposed semi-automatic method was based on the Robust Perspective-S-Point (RP5P) method, graft gap interpolation and semiautomatic multiple-class marker center determination. In this paper, an automatic 3D shape instantiation could be achieved by automatic multiple-class marker segmentation and hence automatic multiple-class marker center determination. Firstly, the markers were designed into five different shapes. Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy projections of customized markers into five classes and hence to determine the marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the network architecture, equally-weighted loss function for initial marker segmentation, and then equally-weighted focal loss function for improving the initial marker segmentation. This proposed network outperformed traditional Weighted U-Net on the class-imbalance segmentation in this paper with reducing one hyperparameter - the weight. An overall mean Intersection over Union (mIoU) of 0.6943 was achieved on 78 testing images, where 81.01 % markers were segmented with a center position error < 1.6mm. Comparable accuracy of 3D shape instantiation was also achieved and stated. The data, trained models and TensorFlow codes are available on-line.

In light of the unique ability of thiazolidinediones to mediate peroxisome proliferator-activated receptor (PPAR)gamma-independent activation of adenosine monophosphate-activated protein kinase (AMPK) and suppression of interleukin (IL)-6 production, we conducted a screening of an in-house, thiazolidinedione-based focused compound library to identify novel agents with these dual pharmacological activities. Cell-based assays pertinent to the activation status of AMPK and mammalian homologue of target of rapamycin (i.e., phosphorylation of AMPK and p70 ribosomal protein S6 kinase, respectively) and IL-6/IL-6 receptor signaling (i.e., IL-6 production and signal transducer and activator of transcription 3 phosphorylation, respectively) in lipopolysaccharide (LPS)-stimulated THP-1 human macrophages were used to screen this compound library, which led to the identification of compound 53 (N-{4-[3-(1-methyl-cyclohexylmethyl)-2,4-dioxo-thiazolidin-5-ylidene-methyl]-phenyl}-4-nitro-3-trifluoro-methyl-benzenesulfonamide) as the lead agent. Evidence indicates that this drug-induced suppression of LPS-stimulated IL-6 production was attributable to AMPK activation. Furthermore, compound 53-mediated AMPK activation was demonstrated in C-26 colon adenocarcinoma cells, indicating that it is not a cell line-specific event.

In cardiac electrophysiology, a long and flexible catheter is delivered to a cardiac chamber for the treatment of arrhythmias. Although several robot-assisted platforms have been commercialized, the disorientation in tele-operation is still not well solved. We propose a validation platform for robot-assisted cardiac EP catheterization, integrating a customized MR Safe robot, a standard clinically used EP catheter, and a human–robot interface. Both model-based and model-free control methods are implemented in the platform for quantitative evaluation and comparison. The model-based and model-free control methods were validated by subject test (ten participants), in which the subjects have to perform a simulated radiofrequency ablation task using both methods. A virtual endoscopic view of the catheter is also provided to enhance hand-to-eye coordination. Assessment indices for targeting accuracy and efficiency were acquired for the evaluation. (1) Accuracy: The average distance measured from catheter tip to the closest lesion target during ablation of model-free method was 19.1% shorter than that of model-based control. (2) Efficiency: The model-free control reduced the total missed targets by 35.8% and the maximum continuously missed targets by 46.2%, both indices corresponded to a low p value ( $$\le 0.05$$ ). The model-free method performed better in terms of both accuracy and efficiency, indicating the model-free control could adapt to soft interaction with environment, as compared with the model-based control that does not consider contacts.

Robot-assisted deployment of fenestrated stent grafts in Fenestrated Endovascular Aortic Repair (FEVAR) requires accurate geometrical alignment. Currently, this process is guided by 2D fluoroscopy, which is uninformative and error prone. In this paper, a real-time framework is proposed to instantiate the 3D shape of a fenestrated stent graft based on only a single low-dose 2D fluoroscopic image. Firstly, the fenestrated stent graft was placed with markers. Secondly, the 3D pose of each stent segment was instantiated by the RPnP (Robust Perspective-n-Point) method. Thirdly, the 3D shape of the whole stent graft was instantiated via graft gap interpolation. Focal-Unet was proposed to segment the markers from 2D fluoroscopic images to achieve semi-automatic marker detection. The proposed framework was validated on five patient-specific 3D printed phantoms of aortic aneurysms and three stent grafts with new marker placements, showing an average distance error of 1-3mm and an average angle error of 4 degree., Comment: 7 pages, 10 figures

Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisition speed and resolution, only 2D images or low-resolution 3D volumes can be used clinically. In this paper, a real-time and registration-free framework for dynamic shape instantiation, generalizable to multiple anatomical applications, is proposed to instantiate high-resolution 3D shapes of an organ from a single 2D image intra-operatively. Firstly, an approximate optimal scan plane was determined by analyzing the pre-operative 3D statistical shape model (SSM) of the anatomy with sparse principal component analysis (SPCA) and considering practical constraints . Secondly, kernel partial least squares regression (KPLSR) was used to learn the relationship between the pre-operative 3D SSM and a synchronized 2D SSM constructed from 2D images obtained at the approximate optimal scan plane. Finally, the derived relationship was applied to the new intra-operative 2D image obtained at the same scan plane to predict the high-resolution 3D shape intra-operatively. A major feature of the proposed framework is that no extra registration between the pre-operative 3D SSM and the synchronized 2D SSM is required. Detailed validation was performed on studies including the liver and right ventricle (RV) of the heart. The derived results (mean accuracy of 2.19mm on patients and computation speed of 1ms) demonstrate its potential clinical value for real-time, high-resolution, dynamic and 3D interventional guidance., Comment: 35 Pages, 11 figures

This letter presents a quantitative approach to the investigation of subject-specific on-body communication channels. To this aim, propagation at 5.8 GHz has been studied considering 50 realistic digital phantoms, statistically generated from a set of 20 magnetic resonance (MR) scans. Both line-of-sight (LoS) and non-line-of-sight (NLoS) communication links have been taken into account. Mathematical expressions are proposed reflecting the correlation between body dimensions (specifically height and waist) and path-loss variation. Results show that linear fitting can be extrapolated between path-loss variations and body shape parameters. In-house parallel finite-difference time-domain (PFDTD) numerical method has been applied to carry out full-wave simulations on the 50 digital phantoms.