Bio-electrospraying 3-D Organotypic Human Skin Cultures.

Organotypic 3D tissue models have greatly contributed to understand a wide range of molecular and cellular characteristics within a functional or diseased tissue. Human skin reconstructs which act as models are most useful for a wide range of investigations, ranging from tissue engineering and regenerative medicine, drug development, screening, and discovery to name a few. There are many approaches for reconstructing 3D skin tissue models, however, to date there have been very few that are able to generate organotypic 3D constructs with a single technology having minimal processing steps to finally scalability. The many manifestations of 3D bioprinting have contributed to this endeavor, having said that, the technology's limitations have tempered those reconstructed models, as they are known to contain low cell numbers/concentrations to those having damaged/dead molecules/cells within the reconstructed tissue, which are not desirable, for exploring as tissues models. Contrary to 3D bioprinting approaches, bio-electrosprays have been demonstrated to possess the ability to handle large concentrations of cells and molecules to whole fertilized embryos without damaging them from a molecular level upwards. Consequently, this article demonstrates, for the first time, bio-electrospray's capacity to reconstruct skin-like structures in vitro and its potential in reconstructing full-thickness 3D organotypic human skin tissues.

An Overview on the Adhesion Mechanisms of Typical Aquatic Organisms and the Applications of Biomimetic Adhesives in Aquatic Environments.

Water molecules pose a significant obstacle to conventional adhesive materials. Nevertheless, some marine organisms can secrete bioadhesives with remarkable adhesion properties. For instance, mussels resist sea waves using byssal threads, sandcastle worms secrete sandcastle glue to construct shelters, and barnacles adhere to various surfaces using their barnacle cement. This work initially elucidates the process of underwater adhesion and the microstructure of bioadhesives in these three exemplary marine organisms. The formation of bioadhesive microstructures is intimately related to the aquatic environment. Subsequently, the adhesion mechanisms employed by mussel byssal threads, sandcastle glue, and barnacle cement are demonstrated at the molecular level. The comprehension of adhesion mechanisms has promoted various biomimetic adhesive systems: DOPA-based biomimetic adhesives inspired by the chemical composition of mussel byssal proteins; polyelectrolyte hydrogels enlightened by sandcastle glue and phase transitions; and novel biomimetic adhesives derived from the multiple interactions and nanofiber-like structures within barnacle cement. Underwater biomimetic adhesion continues to encounter multifaceted challenges despite notable advancements. Hence, this work examines the current challenges confronting underwater biomimetic adhesion in the last part, which provides novel perspectives and directions for future research.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico.

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Overview of Benchtop Models for Comparison of Surgical Treatments for Benign Prostatic Hyperplasia.

PURPOSE OF REVIEW: Benign prostatic hyperplasia (BPH) is a disease of the lower urinary tract which often requires surgical treatment. Recently, there has been a deluge of new treatment options, rarely validated or compared to current treatments on a benchtop model. The purpose of this review is to examine the literature and report which benchtop models are currently being used, which therapies have been tested on them, and what outcomes are being studied on each model. RECENT FINDINGS: There are various benchtop models to choose from, each with their unique benefits and drawbacks. Perfused porcine kidney models are used to assess bleeding on the benchtop, ex-vivo human prostate helps to see specific interactions of devices with the prostatic tissue, and all other models have evaluated tissue ablation rates and depth of coagulation. There are currently no synthetic or non-animal tissues being used for this purpose, and surgical techniques such as enucleation, water-jet ablation, prostate stents, and water vapor thermal therapy have no representation in these benchtop tests. Benchtop testing serves an important role in the evaluation and comparison of surgical treatments for BPH. This testing allows these therapies to be objectively compared to one another, helping novel medical devices in their path to market and urologists make treatment decisions. Future directions may include further validation of the animal models currently being used and development of synthetic models which mimic the prostate on the benchtop.

From Atherosclerosis to Myocardial Infarction: A Process-Oriented Model Investigating the Role of Risk Factors.

Mathematical models are able to reflect biological processes and to capture epidemiologic data. Thus, they may help elucidate roles of risk factors in disease progression. We propose to account for smoking, hypertension, and dyslipidemia in a previously published process-oriented model that describes the development of atherosclerotic lesions resulting in myocardial infarction (MI). The model is sex-specific and incorporates individual heterogeneity. It was applied to population-based individual risk factors and MI rates (Cooperative Health Research in the Region of Augsburg (KORA) study) together with subclinical atherosclerotic lesion data (Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study). Different model variants were evaluated, testing the association of risk factors with different disease processes. Best fits were obtained for smoking affecting a late-stage disease process, suggesting a thrombogenic role. Hypertension was mainly related to complicated, vulnerable lesions. Dyslipidemia was consistent with increasing the number of initial lesions. By accounting for heterogeneity, individual hazard ratios differ from the population average. The mean individual hazard ratio for smoking was twice the population-based hazard ratio for men and even more for women. Atherosclerotic lesion progression and MI incidence data can be related in a mathematical model to illuminate how risk factors affect different phases of this pathological process.

How does an organism extract relevant information from transcription factor concentrations?

How does an organism regulate its genes? The involved regulation typically occurs in terms of a signal processing chain: an externally applied stimulus or a maternally supplied transcription factor leads to the expression of some downstream genes, which, in turn, are transcription factors for further genes. Especially during development, these transcription factors are frequently expressed in amounts where noise is still important; yet, the signals that they provide must not be lost in the noise. Thus, the organism needs to extract exactly relevant information in the signal. New experimental approaches involving single-molecule measurements at high temporal precision as well as increased precision in manipulations directly on the genome are allowing us to tackle this question anew. These new experimental advances mean that also from the theoretical side, theoretical advances should be possible. In this review, I will describe, specifically on the example of fly embryo gene regulation, how theoretical approaches, especially from inference and information theory, can help in understanding gene regulation. To do so, I will first review some more traditional theoretical models for gene regulation, followed by a brief discussion of information-theoretical approaches and when they can be applied. I will then introduce early fly development as an exemplary system where such information-theoretical approaches have traditionally been applied and can be applied; I will specifically focus on how one such method, namely the information bottleneck approach, has recently been used to infer structural features of enhancer architecture.

Engineering the Next Generation of Matched Models for Chemical Translational Biology.

In order to achieve patient personalization and translate compounds through the discovery phase into the clinic, high throughput test models should be designed to be as closely matched to the patient as possible. Engineering high throughput and physiologically relevant biological models is the idealized scenario for testing next generation modulators. I present here a cautionary example of a misaligned model as well as my viewpoint on how overcoming this bottleneck is one of the next frontiers in chemical biology.

SAPs as a new model to probe the pathway of centriole and centrosome assembly.

Centrioles are important cellular organelles involved in the formation of both cilia and centrosomes. It is therefore not surprising that their dysfunction may lead to a variety of human pathologies. Studies have identified a conserved pathway of proteins required for centriole formation, and investigations using the embryo of the fruit fly Drosophila melanogaster have been crucial in elucidating their dynamics. However, a full understanding of how these components interact has been hampered by the total absence of centrioles in null mutant backgrounds for any of these core centriole factors. Here, I review our recent work describing a new model for investigating these interactions in the absence of bona fide centrioles. Sas-6 Ana2 Particles (SAPs) form when two core centriole factors, Sas-6 and Ana2, are co-over-expressed in fruit fly eggs. Crucially, they form even in eggs lacking other core centriole proteins. I review our characterisation of SAPs, and provide one example of how they have been used to investigate the role of a core centriole protein in PCM formation. I then consider some of the strengths and weaknesses of the SAP model, and discuss them in the context of other models for centriole study in Drosophila. Similar aggregates have been seen in other systems upon expression of centriole factors, so SAPs may also be a useful approach to study centriole proteins in other organisms.

Control and Anticontrol of chaos in Fractional-order models of Diabetes, HIV, Dengue, Migraine, Parkinson's and Ebola Virus diseases.

This work proposes new fractional-order (FO) models of six chaotic diseases whose fractional dynamics have not been studied so far in literature. Secondly, design and analysis of suitable controllers to control chaos where present, and that of anticontrollers to generate chaos where absent, for these newly proposed FO models of diseases, are put forward. The proposed controllers and anticontrollers address the problem of the health hazards arising from the dysfunctionalities due to the impact of chaos in these biological models. Controllers to supress chaos in four diseases, namely, FO Diabetes Mellitus, FO Human Immunodeficiency Virus (HIV), FO Ebola Virus and FO Dengue models are designed by Back-stepping, Adaptive Feedback and Sliding Mode Control strategies, whereas anticontrollers to introduce chaos in diseases, namely, FO Parkinson's illness and FO Migraine models, are carried out by Linear State Feedback, Single State Sinusoidal Feedback and Sliding Mode Anticontrol strategies. The equilibrium points, eigenvalues and Lyapunov Exponents of the FO disease models are evaluated and indicate the significance of chaos in them and necessitate upon the requirement of controllers and anticontrollers accordingly. The simulation results in terms of bifurcation diagrams, time series plots and phase portraits confirm the successful accomplishment of the control objectives.

Studying the surfaces of bacteria using neutron scattering: finding new openings for antibiotics.

The use of neutrons as a scattering probe to investigate biological membranes has steadily grown in the past three decades, shedding light on the structure and behaviour of this ubiquitous and fundamental biological barrier. Meanwhile, the rise of antibiotic resistance has catalysed a renewed interest in understanding the mechanisms underlying the dynamics of antibiotics interaction with the bacterial cell envelope. It is widely recognised that the key reason behind the remarkable success of Gram-negative pathogens in developing antibiotic resistance lies in the effectiveness of their outer membrane (OM) in defending the cell from antibacterial compounds. Critical to its function, the highly asymmetric lipid distribution between the inner and outer bilayer leaflets of the OM, adds an extra level of complexity to the study of this crucial defence barrier. Here we review the opportunities offered by neutron scattering techniques, in particular reflectometry, to provide structural information on the interactions of antimicrobials with in vitro models of the OM. The differential sensitivity of neutrons towards hydrogen and deuterium makes them a unique probe to study the structure and behaviour of asymmetric membranes. Molecular-level understanding of the interactions between antimicrobials and the Gram-negative OM provides valuable insights that can aid drug development and broaden our knowledge of this critically important biological barrier.

Soil carbon dioxide venting through rice roots.

The growth of rice in submerged soils depends on its ability to form continuous gas channels-aerenchyma-through which oxygen (O2 ) diffuses from the shoots to aerate the roots. Less well understood is the extent to which aerenchyma permits venting of respiratory carbon dioxide (CO2 ) in the opposite direction. Large, potentially toxic concentrations of dissolved CO2 develop in submerged rice soils. We show using X-ray computed tomography and image-based mathematical modelling that CO2 venting through rice roots is far greater than thought hitherto. We found rates of venting equivalent to a third of the daily CO2 fixation in photosynthesis. Without this venting through the roots, the concentrations of CO2 and associated bicarbonate (HCO3- ) in root cells would have been well above levels known to be toxic to roots. Removal of CO2 and hence carbonic acid (H2 CO3 ) from the soil was sufficient to increase the pH in the rhizosphere close to the roots by 0.7 units, which is sufficient to solubilize or immobilize various nutrients and toxicants. A sensitivity analysis of the model showed that such changes are expected for a wide range of plant and soil conditions.

Statistical selection of biological models for genome-wide association analyses.

Genome-wide association studies have discovered many biologically important associations of genes with phenotypes. Typically, genome-wide association analyses formally test the association of each genetic feature (SNP, CNV, etc) with the phenotype of interest and summarize the results with multiplicity-adjusted p-values. However, very small p-values only provide evidence against the null hypothesis of no association without indicating which biological model best explains the observed data. Correctly identifying a specific biological model may improve the scientific interpretation and can be used to more effectively select and design a follow-up validation study. Thus, statistical methodology to identify the correct biological model for a particular genotype-phenotype association can be very useful to investigators. Here, we propose a general statistical method to summarize how accurately each of five biological models (null, additive, dominant, recessive, co-dominant) represents the data observed for each variant in a GWAS study. We show that the new method stringently controls the false discovery rate and asymptotically selects the correct biological model. Simulations of two-stage discovery-validation studies show that the new method has these properties and that its validation power is similar to or exceeds that of simple methods that use the same statistical model for all SNPs. Example analyses of three data sets also highlight these advantages of the new method. An R package is freely available at www.stjuderesearch.org/site/depts/biostats/maew.

The discovery of hydrogen bonds in DNA and a re-evaluation of the 1948 Creeth two-chain model for its structure.

We recall the experimental approaches involved in the discovery of hydrogen bonds in deoxyribonucleic acid (DNA) made 70 years ago by a team of scientists at University College Nottingham led by J.M. Gulland, and in relation to previous studies. This discovery proved an important step in the elucidation of the correct structure for DNA made by J.D. Watson and F.H.C. Crick, as acknowledged in 'The Double Helix'. At that time of the discovery, however, it was impossible to delineate between inter- and intra-chain hydrogen bonds. We also consider in the light of more recent hydrodynamic theory a tentative model for DNA proposed by Gulland's and D.O. Jordan's PhD student J.M. Creeth in his PhD thesis of 1948, with the correct prediction of two chains with a sugar-phosphate backbone on the exterior and hydrogen-bonded bases between the nucleotide bases of opposite chains in the interior. Our analysis shows that his incorporation of alternating breaks in the two-chain structure was not necessary to explain the viscosity data on scission of hydrogen bonds after titrating to high or low pH. Although Creeth's model is a depiction of DNA structure alone, he could not know whether the hydrogen bonding was intermolecular, although this was subsequently proved correct by others. The mechanisms by which replicative processes occurred were of course unknown at that time, and so, he could not have realised how closely his tentative model resembled steps in some viral replicative mechanisms involving the molecule of life that he was working on.

Modelling phosphorus uptake in microalgae.

Phosphorus (P) is an essential non-renewable nutrient that frequently limits plant growth. It is the foundation of modern agriculture and, to a large extent, demand for P is met from phosphate rock deposits which are limited and becoming increasingly scarce. Adding an extra stroke to this already desolate picture is the fact that a high percentage of P, through agricultural runoff and waste, makes its way into rivers and oceans leading to eutrophication and collapse of ecosystems. Therefore, there is a critical need to practise P recovery from waste and establish a circular economy applicable to P resources. The potential of microalgae to uptake large quantities of P and use of this P enriched algal biomass as biofertiliser has been regarded as a promising way to redirect P from wastewater to the field. This also makes the study of molecular mechanisms underlying P uptake and storage in microalgae of great interest. In the present paper, we review phosphate models, which express the growth rate as a function of intra- and extracellular phosphorus content for better understanding of phosphate uptake and dynamics of phosphate pools.

Total flucloxacillin plasma concentrations poorly reflect unbound concentrations in hospitalized patients with Staphylococcus aureus bacteraemia.

AIMS: Flucloxacillin dosing may be guided by measurement of its total plasma concentrations. Flucloxacillin is highly protein bound with fraction unbound in plasma (fu ) of around 0.04 in healthy individuals. The utility of measuring unbound flucloxacillin concentrations for patients outside the intensive care unit (ICU) is not established. We aimed to compare flucloxacillin fu in non-ICU hospitalised patients against healthy volunteers, and to examine the performance of a published model for predicting unbound concentrations, using total flucloxacillin and plasma albumin concentrations. METHODS: Data from 12 healthy volunteers (248 samples) and 47 hospitalized patients (61 samples) were examined. Plasma flucloxacillin concentrations were measured using a validated liquid chromatography-tandem mass spectrometry method. Flucloxacillin fu for the two groups was compared using a generalized estimating equation model to account for clustered observations. The performance of the single protein binding site prediction model in hospitalized patients was compared with measured unbound concentrations using Bland-Altman plots. RESULTS: The median (range) flucloxacillin fu for healthy (median albumin 45 g l-1 ) and hospitalized individuals (median albumin 30 g l-1 ) were 0.04 (0.02-0.07) and 0.10 (0.05-0.37), respectively (P < 0.0001). The prediction model underpredicted unbound flucloxacillin concentrations with a mean bias (95% limits of agreement) of -54% (-137%, +30%). CONCLUSIONS: The flucloxacillin fu values observed in our cohort of hospitalized patients had a wide range and were greater than those of healthy individuals. Unbound flucloxacillin plasma concentrations were predicted poorly by the model. Instead, unbound concentrations should be measured to guide dosing.

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging.

OBJECTIVE: This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol. MATERIALS AND METHODS: Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter. RESULTS: Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain500,1000, kidney300,1000, and liver200,1000. CONCLUSION: IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability.

[Magnetic resonance imaging : Recent studies on biological effects of static magnetic and high­frequency electromagnetic fields]. / Magnetresonanztomographie : Neuere Studien zur biologischen Wirkung statischer Magnetfelder und hochfrequenter elektromagnetischer Felder.

PROBLEM: During the last few years, new studies on biological effects of strong static magnetic fields and on thermal effects of high-frequency electromagnetic fields used in magnetic resonance imaging (MRI) were published. Many of these studies have not yet been included in the current safety recommendations. METHOD: Scientific publications since 2010 on biological effects of static and electromagnetic fields in MRI were researched and evaluated. RESULTS: New studies confirm older publications that have already described effects of static magnetic fields on sensory organs and the central nervous system, accompanied by sensory perceptions. A new result is the direct effect of Lorentz forces on ionic currents in the semicircular canals of the vestibular system. Recent studies of thermal effects of high-frequency electromagnetic fields were focused on the development of anatomically realistic body models and a more precise simulation of exposure scenarios. RECOMMENDATION FOR PRACTICE: Strong static magnetic fields can cause unpleasant sensations, in particular, vertigo. In addition, they can influence the performance of the medical staff and thus potentially endanger the patient's safety. As a precaution, medical personnel should move slowly within the field gradient. High-frequency electromagnetic fields lead to an increase in the temperature of patients' tissues and organs. This should be considered especially in patients with restricted thermoregulation and in pregnant women and neonates; in these cases exposure should be kept as low as possible.

Ultrasonography-based Fetal Weight Estimation: Finding an Appropriate Model for an Indian Population.

BACKGROUND: Very limited information is available regarding the accuracy and applicability of various ultrasonography parameters [abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and head circumference (HC)]-based fetal weight estimation models for Indian population. The objective of this study was to systematically evaluate commonly used fetal weight estimation models to determine their appropriateness for an Indian population. METHODS: Retrospective data of 300 pregnant women was collected from a tertiary care center in Bengaluru, India. The inclusion criteria were a live singleton pregnancy, gestational age ≥ 34 weeks, and last ultrasound scan to delivery duration ≤ 7 days. Cases with suspected fetal growth restriction or malformation were excluded. For each case, fetal weight was estimated using 34 different models. The models specifically designed for low birth weight, small for gestation age, or macrosomic babies were excluded. The models were ranked based on their mean percentage error (MPE) and its standard deviation (random error). A model with the least MPE and random error ranking was considered as the best model. RESULTS: In total, 149 cases were found suitable for the study. Out of 34, only 12 models had MPE within ± 10% and only seven models had random error < 10%. Most of the Western population-based models had a tendency to overestimate the fetal weight. Based on MPE and random error ranking, the Woo's (AC-BPD) model was found to be the best, followed by Jordaan (AC), Combs (AC-HC-FL), Hadlock (AC-HC), and Hadlock-3 (AC-HC-FL) models. It was observed that the models based on just AC and AC-BPD combinations had statistically significant lesser MPE than the models based on all other combinations (p < 0.05). CONCLUSION: It was observed that the existing models have higher errors on Indian population than on their native populations. This points toward limitations in direct application of these models on Indian population without due consideration. Therefore, it is recommended that clinicians should exert caution in interpretation of fetal weight estimations based on these models. Moreover, this study highlights a need of models based on native Indian population.

A Systematic Evaluation of Ultrasound-based Fetal Weight Estimation Models on Indian Population.

BACKGROUND: The purpose of this study was to systematically evaluate ultrasound-based fetal weight estimation models on Indian population to find out their performance across different weight bands and ability to correctly categorize low birth weight (LBW) and high birth weight (HBW) fetuses. METHODS: We used retrospectively collected data of 154 cases for the study. Inclusion criteria were a live singleton pregnancy, gestational age ≥34 weeks and ultrasound scan to delivery duration ≤7 days. Cases with fetal growth restriction or malformation were excluded. The cases were divided into standard weight bands of 500 g each based on newborns' actual birth weights (ABW). For each weight band, performance of 12 different models based on abdominal circumference (AC), biparietal diameter (BPD), head circumference (HC) and femur length (FL) was evaluated by mean percentage error (MPE) and its standard deviation (random error). Sensitivity and positive predict value (PPV) of models to categorize LBW (ABW ≤ 2500 g) and HBW (ABW >3500 g) neonates were also evaluated. RESULTS: We observed a significant variation in MPE of the 12 models with no single model being consistently superior across all the weight bands. For the cases with birth weight ≤3000 g, the Woo (AC-BPD) model was found to be more appropriate, whereas for the cases with birth weight >3000 g the Woo (AC-BPD-FL) model was found more appropriate. In general, models had a tendency to overestimate fetal weight in LBW neonates and underestimate it in HBW neonates. Overall, the models showed poor sensitivity and PPV to categorize LBW and HBW neonates. The highest sensitivity (57.1%) for LBW identification was observed with the Woo (AC-BPD) model; the highest PPV (50%) for HBW neonate identification was observed with the Hadlock (AC-HC), Warsof (AC-BPD) and Combs (AC-HC-FL) model. CONCLUSION: We found that the existing fetal weight estimation models have high systematic and random errors on Indian population, with a general tendency of overestimation of fetal weight in the LBW category and underestimation in the HBW category. We also observed that these models have a limited ability to predict babies at a risk of either low or high birth weight. It is recommended that the clinicians should consider all these factors, while interpreting estimated weight given by the existing models.

Opportunities and challenges of current electrophysiology research: a plea to establish 'translational electrophysiology' curricula.

Cardiac electrophysiology has evolved into an important subspecialty in cardiovascular medicine. This is in part due to the significant advances made in our understanding and treatment of heart rhythm disorders following more than a century of scientific discoveries and research. More recently, the rapid development of technology in cellular electrophysiology, molecular biology, genetics, computer modelling, and imaging have led to the exponential growth of knowledge in basic cardiac electrophysiology. The paradigm of evidence-based medicine has led to a more comprehensive decision-making process and most likely to improved outcomes in many patients. However, implementing relevant basic research knowledge in a system of evidence-based medicine appears to be challenging. Furthermore, the current economic climate and the restricted nature of research funding call for improved efficiency of translation from basic discoveries to healthcare delivery. Here, we aim to (i) appraise the broad challenges of translational research in cardiac electrophysiology, (ii) highlight the need for improved strategies in the training of translational electrophysiologists, and (iii) discuss steps towards building a favourable translational research environment and culture.