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Objective: To estimate rates and time to reach emergence of consciousness from vegetative state/unresponsive wakefulness syndrome (VS/UWS), and explore factors associated with improved recovery in children and adolescents with disorders of consciousness (DoC) following severe traumatic and non-traumatic brain injury. Methods: Analytical, retrospective, cohort study. Clinical records of consecutively referred patients admitted in VS/UWS to a neurological rehabilitation institute in Argentina, between 2005 and 2021 were reviewed. Seventy children and adolescents were included in the analysis. A specialized 12-week rehabilitation program was administered, and emergence was defined by scores ≥44 points on the Western Neuro Sensory Stimulation Profile (WNSSP), sustained for at least 3 weeks on consecutive weekly evaluations. Results: Emergence from VS/UWS to consciousness occurred within 5.4 (SD 2.6) weeks in almost one-third of patients. Multivariate Cox regression analysis showed emergence was significantly lower in patients with hypoxic ischemic encephalopathy compared to patients with other non-traumatic etiologies [HRadj 0.23 (95% CI 0.06–0.89); p = 0.03)]. Conclusions: Our findings reinforce growing evidence on the impact of etiology on DoC recovery in pediatric populations, ultimately influencing treatment and family-related decisions in child neurorehabilitation. [ABSTRACT FROM AUTHOR]

Pulmonary vein isolation (PVI) is the most successful treatment for atrial fibrillation (AF) nowadays. However, not all AF patients benefit from PVI. In this study, we evaluate the use of ECGI to identify reentries and relate rotor density in the pulmonary vein (PV) area as an indicator of PVI outcome. Rotor maps were computed in a set of 29 AF patients using a new rotor detection algorithm. The relationship between the distribution of reentrant activity and the clinical outcome after PVI was studied. The number of rotors and proportion of PSs in different atrial regions were computed and compared retrospectively in two groups of patients: patients that remained in sinus rhythm 6 months after PVI and patients with arrhythmia recurrence. The total number of rotors obtained was higher in patients returning to arrhythmia after the ablation (4.31 ± 2.77 vs. 3.58 ± 2.67%, p = 0.018). However, a significantly higher concentration of PSs in the pulmonary veins was found in patients that remained in sinus rhythm (10.20 ± 12.40% vs. 5.19 ± 9.13%, p = 0.011) 6 months after PVI. The results obtained show a direct relationship between the expected AF mechanism and the electrophysiological parameters provided by ECGI, suggesting that this technology offers relevant information to predict the clinical outcome after PVI in AF patients.

Anthracyclines are widely used in the treatment of many solid cancers, but their efficacy is limited by cardiotoxicity. As the number of pediatric cancer survivors continues to rise, there has been a concomitant increase in people living with anthracycline-induced cardiotoxicity. Accordingly, there is an ongoing need for new models to better understand the pathophysiological mechanisms of anthracycline-induced cardiac damage. Here we generated induced pluripotent stem cells (iPSCs) from two pediatric oncology patients with acute cardiotoxicity induced by anthracyclines and differentiated them to ventricular cardiomyocytes (hiPSC-CMs). Comparative analysis of these cells (CTX hiPSC-CMs) and control hiPSC-CMs revealed that the former were significantly more sensitive to cell injury and death from the anthracycline doxorubicin (DOX), as measured by viability analysis, cleaved caspase 3 expression, oxidative stress, genomic and mitochondrial damage and sarcomeric disorganization. The expression of several mRNAs involved in structural integrity and inflammatory response were also differentially affected by DOX. Functionally, optical mapping analysis revealed higher arrythmia complexity after DOX treatment in CTX iPSC-CMs. Finally, using a panel of previously identified microRNAs associated with cardioprotection, we identified lower levels of miR-22-3p, miR-30b-5p, miR-90b-3p and miR-4732-3p in CTX iPSC-CMs under basal conditions. Our study provides valuable phenotype information for cellular models of cardiotoxicity and highlights the significance of using patient-derived cardiomyocytes for studying the associated pathogenic mechanisms.

Abstract The concept that cell‐based repair of myocardial injury might be possible was introduced almost two decades ago; however, the field of cardiovascular reparative medicine has been criticized as translation to clinically effective approaches has been slow. The recent retraction of a series of papers has further impacted perception of this area of research. As researchers, clinicians, and teachers in this field, we felt it incumbent to critically appraise the current state of cardiac cell repair, determine what can be learned from past mistakes, and formulate best practices for future work. This special communication summarizes an introspective assessment of what has fallen short, how to prevent similar issues, and how the field might best move forward in the service of science and patients.

Introduction: Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and recording ECG electrodes distribution to compute ECGI during atrial fibrillation (AF).Methods: Torso meshes from 100 to 2000 nodes heterogeneously and homogeneously distributed were compared. Signals from nine AF realistic mathematical simulations were used for computing the ECGI. Results for each torso mesh were compared with the ECGI computed with a 4,000 nodes reference torso. In addition, real AF recordings from 25 AF patients were used to compute ECGI in torso meshes from 100 to 1,000 nodes. Results were compared with a reference torso of 2000 nodes. Torsos were remeshed either by reducing the number of nodes while maximizing the overall shape preservation and then assigning the location of the electrodes as the closest node in the new mesh or by forcing the remesher to place a node at each electrode location. Correlation coefficients, relative difference measurements and relative difference of dominant frequencies were computed to evaluate the impact on signal morphology of each torso mesh.Results: For remeshed torsos where electrodes match with a geometrical node in the mesh, all mesh densities presented similar results. On the other hand, in torsos with electrodes assigned to closest nodes in remeshed geometries performance metrics were dependent on mesh densities, with correlation coefficients ranging from 0.53 ± 0.06 to 0.92 ± 0.04 in simulations or from 0.42 ± 0.38 to 0.89 ± 0.2 in patients. Dominant frequency relative errors showed the same trend with values from 1.14 ± 0.26 to 0.55 ± 0.21 Hz in simulations and from 0.91 ± 0.56 to 0.45 ± 0.41 Hz in patients.Conclusion: The effect of mesh density in ECGI is minimal when the location of the electrode is preserved as a node in the mesh. Torso meshes constructed without imposing electrodes to constitute nodes in the torso geometry should contain at least 400 nodes homogeneously distributed so that a distance between nodes is below 4 cm.

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. Despite the frequency of the disease, the treatment strategies for AF are inefficient. We developed a cost-effectiveness model to evaluate potential improvements in the application of cardiac ablations to treat AF. These are surgical procedures to terminate the arrhythmia and restore Sinus Rhythm. A Markov Model with a time horizon of five years was built to represent the management of patients in AF. A Montecarlo simulation was developed as a sensitivity analysis when the effectiveness increases the estimate of the potential impact of an improvement on the efficacy of cardiac ablation. The result of the analysis showed 44% of patients were untreated in any way. The base case ends up with 45% of patients having sinus rhythm restored after five years. The Montecarlo simulation estimates that in 58% of cases, the alternative of increasing ablation effectiveness by 25% would be cost-effective. If the number of performed ablations is doubled, the robustness increases to 86%. In conclusion, the model of management of AF highlights the importance of not only increasing effectiveness, but also treating more patients. Our study shows that investing in new screening technology to increase the effectiveness of ablations would be cost-effective.

Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images. Several deep learning-based methods have been proposed to detect AF, but most of the AF-based studies do not include the estimation of ablation targets. In this study, we propose to model the location of AF drivers from BSP as a supervised classification problem using convolutional neural networks (CNN). Accuracy in the test set ranged between 0.75 (SNR = 5 dB) and 0.93 (SNR = 20 dB upward) when assuming time independence, but it worsened to 0.52 or lower when dividing AF models into blocks. Therefore, CNN could be a robust method that could help to non-invasively identify target regions for ablation in AF by using body surface potential mapping, avoiding the use of ECGI.