Surgical Melody Mitral Valve: A Paradigm Shift for Infants With Unrepairable Mitral Valve Disease.

BACKGROUND: The Melody valve (Medtronic, Minneapolis, MN) for mitral valve replacement (MVR) (MelodyMVR) has been an effective strategy to treat unrepairable mitral valve disease in small children. This study analyzed survival, durability, and complications of the MelodyMVR strategy. METHODS: Patients who underwent MelodyMVR between 2014 and 2023 were included. Transplant-free survival was analyzed with Kaplan-Meier analysis. The Fine and Gray subdistribution method was applied to quantify the cumulative incidence. RESULTS: Twenty-five patients underwent MelodyMVR. Median age and weight were 6.3 months (interquartile range, 4.4-15.2 months) and 6.36 kg (interquartile range, 4.41-7.57 kg). Fifteen patients (60%) had congenital mitral valve disease and 13 (52%) had dominant mitral regurgitation. The median diameter of the implanted Melody was 16 mm (interquartile range, 14-18 mm). Mortality at 6 months, 1 year, and 5 years was 8.3% (95% CI, 2.2%-29.4%), 12.5% (95% CI, 4.2%-33.9%), and 17.6% (95% CI, 7.0%-40.7%), respectively. Two hospital survivors (8%) required early Melody replacement. Competing risk analysis showed that ∼50% of patients underwent mechanical MVR by 3.5 years after MelodyMVR. Freedom from bleeding and thrombosis at 4 years was 87.5% (95% CI, 74.2%-100%). Eleven patients underwent subsequent mechanical MVR with no deaths. One (9%) required pacemaker implantation after mechanical MVR. CONCLUSIONS: MelodyMVR provides reasonable early and medium-term survival in small children and a high rate of successful bridge to mechanical MVR. MelodyMVR is associated with minimal pacemaker requirement, bleeding, and thrombosis. Early Melody functional deterioration necessitates early repeat MVR, which can be achieved with minimal morbidity and mortality.

Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network.

Aims: Mathematical models previously developed to predict outcomes in patients with heart failure (HF) generally have limited performance and have yet to integrate complex data derived from cardiopulmonary exercise testing (CPET), including breath-by-breath data. We aimed to develop and validate a time-to-event prediction model using a deep learning framework using the DeepSurv algorithm to predict outcomes of HF. Methods and results: Inception cohort of 2490 adult patients with high-risk cardiac conditions or HF underwent CPET with breath-by-breath measurements. Potential predictive features included known clinical indicators, standard summary statistics from CPETs, and mathematical features extracted from the breath-by-breath time series of 13 measurements. The primary outcome was a composite of death, heart transplant, or mechanical circulatory support treated as a time-to-event outcomes. Predictive features ranked as most important included many of the features engineered from the breath-by-breath data in addition to traditional clinical risk factors. The prediction model showed excellent performance in predicting the composite outcome with an area under the curve of 0.93 in the training and 0.87 in the validation data sets. Both the predicted vs. actual freedom from the composite outcome and the calibration of the prediction model were excellent. Model performance remained stable in multiple subgroups of patients. Conclusion: Using a combined deep learning and survival algorithm, integrating breath-by-breath data from CPETs resulted in improved predictive accuracy for long-term (up to 10 years) outcomes in HF. DeepSurv opens the door for future prediction models that are both highly performing and can more fully use the large and complex quantity of data generated during the care of patients with HF.

Enhancing the Prediction of Cardiac Allograft Vasculopathy Using Intravascular Ultrasound and Machine Learning: A Proof of Concept.

BACKGROUND: Cardiac allograft vasculopathy (CAV) is the leading cause of late graft dysfunction in heart transplantation. Building on previous unsupervised learning models, we sought to identify CAV clusters using serial maximal intimal thickness and baseline clinical risk factors to predict the development of early CAV. METHODS: This is a single-center retrospective study including adult heart transplantation recipients. A latent class mixed-effects model was used to identify patient clusters with similar trajectories of maximal intimal thickness posttransplant and pretransplant covariates associated with each cluster. RESULTS: Among 186 heart transplantation recipients, we identified 4 patient phenotypes: very low, low, moderate, and high risk. The 5-year risk (95% CI) of the International Society for Heart and Lung Transplantation-defined CAV in the high, moderate, low, and very low risk groups was 49.1% (35.2%-68.5%), 23.4% (13.3%-41.2%), 5.0% (1.3%-19.6%), and 0%, respectively. Only patients in the moderate to high risk cluster developed the International Society for Heart and Lung Transplantation CAV 2-3 at 5 years (P=0.02). Of the 4 groups, the low risk group had significantly younger female recipients, shorter ischemic time, and younger female donors compared with the high risk group. CONCLUSIONS: We identified 4 clusters characterized by distinct maximal intimal thickness trajectories. These clusters were shown to discriminate against the development of angiographic CAV. This approach allows for the personalization of surveillance and CAV-directed treatment before the development of angiographically apparent disease.

Reintervention Before Bidirectional Cavopulmonary Shunt and Intermediate Outcomes in Children with Single Ventricle Who Underwent Main Pulmonary Artery Banding.

Unplanned reinterventions following pulmonary artery banding (PAB) in single ventricle patients are common before stage 2 palliation (S2P) but associated risk factors are unknown. We hypothesized that reintervention is more common when PAB is placed at younger age and with a looser band, reflected by lower PAB pressure gradient. Retrospective single center study of single ventricle patients undergoing PAB between Jan 2000 and Dec 2020. The association with reintervention and successful S2P was modeled using exploratory cause-specific hazard regression. A multivariable model was developed adjusting for clinical and statistically relevant predictors. The cumulative proportion of patients undergoing reintervention were summarized using a competing risk model. 77 patients underwent PAB at median (IQR) 47 (24-66) days and 3.73 (3.2-4.5) kg. Within18 months of PAB, 60 (78%) reached S2P, 9 (12%) died, 1 (1%) transplanted and 7 (9%) were alive without S2P. Within 18 months of PAB 10 (13%) patients underwent reintervention related to pulmonary blood flow modification: PAB adjustment (n = 6) and conversion to Damus-Kaye-Stansel/Blalock-Taussig-Thomas shunt (n = 4). 6/10 (60%) reached S2P following reintervention. A trend toward higher intervention in patients with a genetic syndrome (p-0.06) and weight < 3 kg (p-0.057) at time of PAB was noted. Only genetic syndrome was a risk factor associated with poor outcome (p-0.025). PAB has a reasonable outcome in SV patients with unobstructed systemic and pulmonary blood flow, but with a high reintervention rate. Only a quarter of patients with genetic syndromes reach S2P and further study is required to explore the benefits from an alternative palliative strategy.

Experimental and Theoretical Estimations of Atrazine's Adsorption in Mangosteen-Peel-Derived Nanoporous Carbons.

Nanoporous carbons were prepared via chemical and physical activation from mangosteen-peel-derived chars. The removal of atrazine was studied due to the bifunctionality of the N groups. Pseudo-first-order, pseudo-second-order, and intraparticle pore diffusion kinetic models were analyzed. Adsorption isotherms were also analyzed according to the Langmuir and Freundlich models. The obtained results were compared against two commercially activated carbons with comparable surface chemistry and porosimetry. The highest uptake was found for carbons with higher content of basic surface groups. The role of the oxygen-containing groups in the removal of atrazine was estimated experimentally using the surface density. The results were compared with the adsorption energy of atrazine theoretically estimated on pristine and functionalized graphene with different oxygen groups using periodic DFT methods. The energy of adsorption followed the same trend observed experimentally, namely the more basic the pH, the more favored the adsorption of atrazine. Micropores played an important role in the uptake of atrazine at low concentrations, but the presence of mesoporous was also required to inhibit the pore mass diffusion limitations. The present work contributes to the understanding of the interactions between triazine-based pollutants and the surface functional groups on nanoporous carbons in the liquid-solid interface.

Rethinking Donor and Recipient Risk Matching in Europe and North America: Using Heart Transplant Predictors of Donor and Recipient Risk.

BACKGROUND: In Europe, there is greater acceptance of hearts from higher-risk donors for transplantation, whereas in North America, the donor heart discard rate is significantly higher. A Donor Utilization Score (DUS) was used to compare European and North American donor characteristics for recipients included in the International Society for Heart and Lung Transplantation registry from 2000 to 2018. DUS was further evaluated as an independent predictor for 1-year freedom from graft failure, after adjusting for recipient risk. Lastly, we assessed donor-recipient risk matching with the outcome of 1-year graft failure. METHODS: DUS was applied to the International Society for Heart and Lung Transplantation cohort using meta-modeling. Posttransplant freedom from graft failure was summarized by Kaplan-Meier survival. Multivariable Cox proportional hazard regression was applied to quantify the effects of DUS and Index for Mortality Prediction After Cardiac Transplantation score on the 1-year risk of graft failure. We present 4 donor/recipient risk groups using the Kaplan-Meier method. RESULTS: European centers accept significantly higher-risk donor hearts compared to North America. DUS 0.45 versus 0.54, P<0.005). DUS was an independent predictor for graft failure with an inverse linear relationship when adjusted for covariates (P<0.001). The Index for Mortality Prediction After Cardiac Transplantation score, a validated tool to assess recipient risk, was also independently associated with 1-year graft failure (P<0.001). In North America, 1-year graft failure was significantly associated with donor-recipient risk matching (log-rank P<0.001). One-year graft failure was highest with pairing of high-risk recipients and donors (13.1% [95% CI, 10.7%-13.9%]) and lowest among low-risk recipients and donors (7.4% [95% CI, 6.8%-8.0%]). Matching of low-risk recipients with high-risk donors was associated with significantly less graft failure (9.0% [95% CI, 8.3%-9.7%]) than high-risk recipients with low-risk donors (11.4% [95% CI, 10.7%-12.2%]) Conclusions: European heart transplantation centers are more likely to accept higher-risk donor hearts than North American centers. Acceptance of borderline-quality donor hearts for lower-risk recipients could improve donor heart utilization without compromising recipient survival.

Comparison between Del Nido and conventional blood cardioplegia in pediatric open-heart surgery.

BACKGROUND: Del Nido cardioplegia (DNc) was designed for superior myocardial protection during cardiopulmonary bypass (CPB). We conducted a retrospective review to explore if DNc was associated with increase in systemic ventricle dysfunction (sVD) following pediatric CPB. METHODS AND RESULTS: This single-center, retrospective study included 1534 patients undergoing CPB between 2013 and 2016, 997 prior to center-wide conversion to DNc and 537 following. The primary outcome was new postoperative ≥moderate sVD by echocardiogram. Secondary outcomes included sVD of any severity and right ventricular dysfunction. Data was evaluated by interrupted time-series analysis. Groups had similar cardiac diagnoses and surgical complexity. Del Nido cardioplegia was associated with longer median (IQR) CPB [117 (84-158) vs 108 (81-154), p = 0.04], and aortic cross-clamp [83 (55-119) vs 76 (53-106), p = 0.03], and fewer cardioplegia doses [2 (1-2) vs 3 (2-4), p < 0.0001]. Mortality was similar in both groups. Frequency of sVD was unchanged following DNc, including predetermine subgroups (neonates, infants, and prolonged cross-clamp). Logistic regression showed a significant rise in right ventricular dysfunction (OR 5.886 [95% CI: 0.588, 11.185], p = 0.03) but similar slope. CONCLUSIONS: Use of DNc was not associated with increased in reported sVD, and provided similar myocardical protection to the systemic ventricle compared to conventional cardioplegia but may possibly impact right ventricular function. Studies evaluating quantitative systolic and diastolic function are needed.

Melody Mitral Valve Is a Promising Alternative to Mechanical Valve Replacement for Young Children.

PURPOSE: The purpose of this study was to compare outcomes of Melody mitral valve to mechanical mitral valve replacement (MVR) for young children. DESCRIPTION: Children who underwent Melody MVR from 2014 to 2020 were case-matched to mechanical MVR patients. Transplant-free survival and cumulative incidence of reintervention were compared. A subanalysis was performed for infants aged < 1 year (9 Melody MVRs and their matches). EVALUATION: Twelve children underwent Melody MVR. Two children (17%) salvaged from mechanical support died. Five of 10 survivors (50%) had subsequent MVR. At 1 and 3 years, transplant-free survival (Melody: 83%, 83%; mechanical: 83%, 67%; P = .180) and reintervention (Melody: 9%, 39%; mechanical: 0%, 18%; P = .18) were equivalent between groups. For children < 1 year of age, Melody MVR had a modest survival benefit (Melody: 89%, 89%; mechanical: 80%, 60%; P = .046), while rate of reintervention remained equivalent (Melody: 13%, 32%; mechanical: 0%, 22%; P = .32). CONCLUSIONS: For patients < 1 year old, Melody MVR offers a promising alternative and is a reasonable bridge to mechanical MVR, which can be performed safely at an older age. Further studies are necessary to corroborate these findings.

Clonal haematopoiesis is associated with higher mortality in patients with cardiogenic shock.

AIMS: Cardiogenic shock (CS) with variable systemic inflammation may be responsible for patient heterogeneity and the exceedingly high mortality rate. Cardiovascular events have been associated with clonal haematopoiesis (CH) where specific gene mutations in haematopoietic stem cells lead to clonal expansion and the development of inflammation. This study aims to assess the prevalence of CH and its association with survival in a population of CS patients in a quaternary centre. METHODS AND RESULTS: We compared the frequency of CH mutations among 341 CS patients and 345 ambulatory heart failure (HF) patients matched for age, sex, ejection fraction, and HF aetiology. The association of CH with survival and levels of circulating inflammatory cytokines was analysed. We detected 266 CH mutations in 149 of 686 (22%) patients. CS patients had a higher prevalence of CH-related mutations than HF patients (odds ratio 1.5; 95% confidence interval [CI] 1.0-2.1, p = 0.02) and was associated with decreased survival (30 days: hazard ratio [HR] 2.7; 95% CI 1.3-5.7, p = 0.006; 90 days: HR 2.2; 95% CI 1.3-3.9, p = 0.003; and 3 years: HR 1.7; 95% CI 1.1-2.8, p = 0.01). TET2 or ASXL1 mutations were associated with lower survival in CS patients at all time-points (p ≤ 0.03). CS patients with TET2 mutations had higher circulating levels of SCD40L, interferon-γ, interleukin-4, and tumour necrosis factor-α (p ≤ 0.04), while those with ASXL1 mutations had decreased levels of CCL7 (p = 0.03). CONCLUSIONS: Cardiogenic shock patients have high frequency of CH, notably mutations in TET2 and ASXL1. This was associated with reduced survival and dysregulation of circulating inflammatory cytokines in those CS patients with CH.

Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival.

BACKGROUND: Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches. METHODS: Plasma gas chromatography-liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women's Hospital, Boston, USA). Seven ML approaches were trained to differentiate survivors from non-survivors. Model performance predicting 28 day mortality was assessed through internal cross-validation, and innate top-feature (metabolite) selection and rankings were compared across the 7 ML approaches and with conventional statistical methods (logistic regression). Metabolites were consensus ranked by a summary, ensemble ML ranking procedure weighing their contribution to mortality risk prediction across multiple ML models. RESULTS: Median (IQR) patient age was 58 (47, 62) years, 45% were women, and median (IQR) SOFA score was 9 (6, 12). Mortality at 28 days was 42%. The models' specificity ranged from 0.619 to 0.821. Partial least squares regression-discriminant analysis and nearest shrunken centroids prioritized the greatest number of metabolites identified by at least one other method. Penalized logistic regression demonstrated top-feature results that were consistent with many ML methods. Across the plasma metabolome, the 13 metabolites with the strongest linkage to mortality defined through an ensemble ML importance score included lactate, bilirubin, kynurenine, glycochenodeoxycholate, phenylalanine, and others. Four of these top 13 metabolites (3-hydroxyisobutyrate, indoleacetate, fucose, and glycolithocholate sulfate) have not been previously associated with sepsis survival. Many of the prioritized metabolites are constituents of the tryptophan, pyruvate, phenylalanine, pentose phosphate, and bile acid pathways. CONCLUSIONS: We identified metabolites linked with sepsis survival, some confirming prior observations, and others representing new associations. The application of ensemble ML feature-ranking tools to metabolomic data may represent a promising statistical platform to support biologic target discovery.

Predicted heart mass for size matching in obese heart transplant donors and recipients.

INTRODUCTION: Predicted heart mass (PHM) was neither derived nor evaluated in an obese population. Our objective was to evaluate size mismatch using actual body weight or ideal body weight (IBW)-adjusted PHM on mortality and risk assessment. METHODS: We conducted a retrospective cohort study of adult recipients with BMI ≥30 kg/m2 or recipients of donors with BMI≥30 kg/m2 from the ISHLT registry. We used multivariable Cox proportional hazard models to evaluate 30-day and 1-year mortality. The two models were compared using net reclassification index. RESULTS: 10,817 HT recipients, age 55 (IQR 46-62) years, 23% female, BMI 31 kg/m2 (IQR 28-33) were included. Donors were age 34 (IQR 24-44) years, 31% female, and BMI 31 kg/m2 (IQR 26-34). There was a significant nonlinear association between mortality and actual PHM but not IBW-adjusted PHM. Undersizing using actual PHM was associated with higher 30-day and 1-year mortality (p < .01), not seen with IBW-adjusted PHM. Actual PHM better risk classified .6% (95% CI .3-.8) patients compared to IBW-adjusted PHM. CONCLUSION: Actual PHM can be used for size matching when assessing mortality risk in obese recipients or recipients of obese donors. There is no advantage to re-calculating PHM using IBW to define candidate risk at the time of organ allocation.

Performance of a C-containing Cu-based photocatalyst for the degradation of tartrazine: Comparison of performance in a slurry and CPC photoreactor under artificial and natural solar light.

A carbon-containing Cu-based material (Cu@C) was used as photocatalyst for the degradation of a commonly food-industry azo-dye (tartrazine, also called Y5), under solar light at laboratory and pilot scale photoreactors. Important performance parameters such as dark adsorption capacity, catalyst́s loading and initial concentration of the dye were first optimized in a slurry photoreactor at laboratory scale under artificial solar light following the kinetics of degradation of the dye. Afterwards, the photocatalytic activity was investigated at pilot scale in a compound parabolic collector (CPC) photoreactor operating for 10 h of irradiation. The degradation of tartrazine is among the highest values reported for alternative metal oxide semiconductors, in both photoreactor configurations. Catalytic data revealed a 3 times faster degradation kinetics of tartrazine in the CPC photoreactor under natural solar light than in the slurry reactor under artificial solar light. This behavior indicates that a moderate photon flux in the CPC is more adequate to operate with the prepared photocatalyst, as it minimizes the recombination of charge carriers in the catalyst. This is important, since most of the photocatalytic tests designed to evaluate the activity of novel materials are frequently carried out under simulated solar light and disregard the impact of photon flux in outdoor conditions.

Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies.

BACKGROUND: Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe coronavirus disease 2019 (COVID-19), indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted. METHODS: Prospectively evaluating the prevalence of circulating inflammatory, cardiac, and EC activation markers as well as developing a microRNA atlas in 241 unvaccinated patients with suspected SARS-CoV-2 infection allowed for prognostic value assessment using a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred. FINDINGS: Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction, which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N. INTERPRETATION: Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis. FUNDING: This work was directly supported by grant funding from the Ted Rogers Center for Heart Research, Toronto, Ontario, Canada and the Peter Munk Cardiac Center, Toronto, Ontario, Canada.

Understanding Complex Interactions in Pediatric Diastolic Function Assessment.

BACKGROUND: Diagnosing left ventricular diastolic dysfunction (DD) noninvasively in children is difficult as no validated pediatric diagnostic algorithm is available. The aim of this study is to explore the use of machine learning to develop a model that uses echocardiographic measurements to explain patterns in invasively measured markers of DD in children. METHODS: Children at risk for developing DD were enrolled, including patients with Kawasaki disease, heart transplantation, aortic stenosis, and coarctation of the aorta when undergoing clinical left heart catheterization. Simultaneous invasive pressure measurements were made using a high-fidelity catheter (time constant of isovolumic relaxation [Tau, τ], left ventricular end-diastolic pressure, and maximum negative rate of pressure change) and echocardiographic DD measurements. Spearman correlations were performed for each echocardiographic feature with invasive markers to understand pairwise relationships. Separate random forest (RF) models were implemented to assess all echocardiographic features, key demographic data, and clinical diagnosis in predicting invasive markers. A backward stepwise regression model was simultaneously implemented as a comparative conventional reference model. The relative importance of all parameters was ranked in terms of accuracy reduction. Model approximation was then performed using a regression tree with the top-ranked features of each RF model to improve model interpretability. Regression coefficients of the linear models were presented. RESULTS: Fifty-nine children were included. Spearman correlations were generally low. The RF models' performance measures were noninferior to those of the linear model. However, the linear model's regression coefficients were unintuitive. The highest ranked important features for the RF models were propagation velocity for Tau, E/propagation velocity ratio for left ventricular end-diastolic pressure, and systolic global longitudinal strain rate for maximum negative rate of pressure change. CONCLUSIONS: Estimating individual components of DD can potentially improve the noninvasive assessment of pediatric DD. Although pairwise correlations measured were weak and linear regression coefficients unintuitive, approximated machine learning models aided in understanding how echocardiographic and invasive parameters of DD are related. This machine learning approach could help in further development of pediatric-specific diagnostic algorithms.

Cardiovascular Signatures of COVID-19 Predict Mortality and Identify Barrier Stabilizing Therapies

STRUCTURED ABSTRACTO_ST_ABSBackgroundC_ST_ABSEndothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe COVID-19, indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted. MethodsEvaluating the prevalence of circulating inflammatory, cardiac and EC activation markers, and the development of a microRNA atlas in 241 patients with suspected SARS-CoV-2 infection, allowed their prognostic value to be assessed by a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred. FindingsMultiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N. InterpretationIntegration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWhile diagnostic testing has allowed for the rapid identification of COVID-19 cases, the lack of post-diagnosis risk assessment metrics, especially among the highest-risk subgroups, thereby undermined the cascade and allocation of care. To date, the integration of clinical data with broad omics technologies has opened up new avenues for efficiently delineating complex patient phenotypes and their associations with clinical outcomes, with circulating profiles of plasma microRNAs (miRNA), in particular, having been shown to be tightly associated with disease, and capable of providing not only detailed prognostic information but also mechanistic insight. Added value of this studyMarkers of endothelial dysfunction at presentation, while indicative of poor outcomes in COVID-19-positive patients, likely reflect systemic vascular dysfunction in critically ill patients and are not specific to SARS-CoV-2 infection. More so, the generation of a plasma microRNA atlas uncovers COVID-19-specific prognostic markers and multiple disease-specific pathways of interest, including endothelial barrier dysfunction. Furthermore, synthesis of electronic health record data with clinically relevant multi-omic datasets using a machine learning approach provides substantially better metrics by which mortality can be estimated in patients with severe COVID-19. Finally, targeted stabilization of the endothelial barrier with Q-Peptide and Slit2-N are novel therapeutic avenues that should be explored in COVID-19 patients. Implications of all the available evidenceTogether, our work provides biological insight into the role of the endothelium in SARS-CoV-2 infection, the importance of miRNA as disease- and pathway-specific biomarkers, and the exciting possibility that endothelial barrier stabilizing treatments might hold promise in COVID-19.

Associations between the spatiotemporal distribution of Kawasaki disease and environmental factors: evidence supporting a multifactorial etiologic model.

The etiology of Kawasaki Disease (KD), the most common cause of acquired heart disease in children in developed countries, remains elusive, but could be multifactorial in nature as suggested by the numerous environmental and infectious exposures that have previously been linked to its epidemiology. There is still a lack of a comprehensive model describing these complex associations. We present a Bayesian disease model that provides insight in the spatiotemporal distribution of KD in Canada from 2004 to 2017. The disease model including environmental factors had improved Watanabe-Akaike information criterion (WAIC) compared to the base model which included only spatiotemporal and demographic effects and had excellent performance in recapitulating the spatiotemporal distribution of KD in Canada (98% and 86% spatial and temporal correlations, respectively). The model suggests an association between the distribution of KD and population composition, weather-related factors, aeroallergen exposure, pollution, atmospheric concentration of spores and algae, and the incidence of healthcare encounters for bacterial pneumonia or viral intestinal infections. This model could be the basis of a hypothetical data-driven framework for the spatiotemporal distribution of KD. It also generates novel hypotheses about the etiology of KD, and provides a basis for the future development of a predictive and surveillance model.

Upgrading of pine tannin biochars as electrochemical capacitor electrodes.

Biochar derived from the pyrolysis of pine tannin is a green and available by-product of oil manufacturing that presents interesting features after having been activated by KOH at 650 °C. Different weight ratios of KOH to biochar were used and the resulting activated carbons (ACs) presented highly developed specific surface areas of up to 2190 m2 g-1, well-connected porosity and high oxygen content, leading to enhanced electrochemical performance when used as electrochemical capacitor electrodes in a 1 M H2SO4 aqueous electrolyte. Galvanostatic charge/discharge experiments evidenced that the best material achieved a maximum electrode capacitance of up to 232 F g-1 (at 0.5 A g-1) with a capacitance retention of 70% at 10 A g-1 using commercial mass loadings (i.e., approx. 10 mg cm-2). In addition, long cycling stability with a residual capacitance of 92 to 94% after 10,000 cycles at 5 A g-1 was achieved. These results prove that ACs derived from pine tannin biochars have great potential for their commercial use as electrochemical energy storage devices.

The Cramer's rule for the parametrization of phenol and its hydroxylated byproducts: UV spectroscopy vs. high performance liquid chromatography.

A linear algebra theorem like Cramer's rule was used for the analysis of a system of equations obtained from UV spectroscopy, and results were compared against those obtained from HPLC analysis. This parametrization allowed to quantify the concentration of the main intermediate products detected along the photodegradation of phenol under UV-Vis irradiation of TiO2. UV spectroscopy data for phenol, hydroquinone, and benzoquinone were analyzed using the Cramer's rule. The overlapping interference of the intermediate products in the UV spectra was corrected. It can be concluded that the Cramer's rule can be used for the parametrization of the UV absorbance data of phenol and its main intermediate products. This methodology permitted to obtain the concentration of phenol and their intermediate products by UV-visible with a high precision in comparison of HPLC. The parametrization showed a correlation coefficient of ca. 0.9775 between the phenol concentration obtained by UV spectroscopy and values obtained from HPLC analysis. In this sense, results can be considered with good precision, and accordingly, it can be concluded that the methodology is reliable, and UV-visible spectroscopy can be selected instead of HPLC in much of the experiments concerning with aqueous-phase reactions.

The Effect of Age on Outcomes After Destination-Therapy Left Ventricular Assist Device Implantation: An Analysis of the IMACS Registry.

BACKGROUND: As patients with advanced heart failure are living longer, defining the impact of left ventricular assist devices (LVADs) on outcomes in an aging population is of great importance. We describe overall survival, rates of adverse events (AEs), and post-AE survival in patients age ≥ 70 years vs age 50-69 years after destination-therapy (DT) LVAD implantation. METHODS: A retrospective analysis was conducted with the use of the International Society for Heart and Lung Transplantation Mechanically Assisted Circulatory Support (IMACS) registry. All adults age ≥ 50 years with a continuous-flow DT LVAD from 2013 to 2017 were included. The primary outcome was all-cause mortality. The secondary outcomes were the incidence of and survival after gastrointestinal (GI) bleeding, infection, stroke, pump thrombosis, pump exchange, and right-side heart failure. Mortality and AEs were assessed with the use of competing risk models. RESULTS: At total of 5,572 patients were included: 3,700 aged 50-69 and 1,872 aged ≥ 70. All-cause mortality by 42 months was 55.8% in patients aged ≥ 70 and 44.8% in patients aged 50-69 (P = 0.001). Patients aged ≥ 70 had a 37.8% higher risk of death after DT LVAD implantation (hazard ratio 1.378, 95% CI 1.251-1.517). Patients aged ≥ 70 had higher risk of GI bleeding but lower risk of right-side heart failure. There was no difference between age groups for risk of infection or stroke. Experiencing any AE was associated with an increased risk of death that did not vary with age. CONCLUSIONS: Patients aged ≥ 70 years have reduced survival after DT LVAD, in part because of increased GI bleeding, while the incidence of other AEs is similar to that of patients aged 50-69 years. Careful patient selection beyond age alone may allow for optimal outcomes after DT LVAD implantation.

Risk factors for early development of cardiac allograft vasculopathy by intravascular ultrasound.

BACKGROUND: Cardiac allograft vasculopathy (CAV) is the leading cause of late graft loss. While there are numerous post-transplant factors which may increase the risk of the development of CAV, there is a paucity of data on the impact of donor-derived atherosclerosis (DA), early discontinuation of prednisone, and early initiation of proliferation signal inhibitors (PSI) as assessed by intravascular ultrasound (IVUS). METHODS: Retrospective single-center study of all adult transplant patients (2008-2017) with serial IVUS at baseline and annually for 5 years. DA was defined as a baseline maximal intimal thickness (MIT) ≥0.5 mm, and CAV development was defined as MIT ≥1 mm or an increase in MIT ≥0.5 mm at year 1 compared with baseline or an increase in 0.3 mm annually thereafter. Clinical risk factors for CAV were identified using multivariable hazard regression. Separate multistate models were applied to assess the association of prednisone discontinuation and PSI initiation and CAV. RESULTS: Of 282 patients screened, 186 patients had a 1-year angiogram. The mean age of those included in the cohort was 51 ± 11 years, 70% were male, 58% were Caucasian, and 27% were supported by a left ventricular assist device. Donor atherosclerosis was present in 40%. The cumulative incidence of CAV at 5 years is 41% in DA- vs. 59% in DA + (p = .012). Donor age was a strong predictor of DA (p = .016). Significant risk factors for CAV included male sex (HR = 4.141, p = .001), non-Caucasian race (HR = 1.98, p = .011), BMI < 18 kg/m2 (HR = 4.596, p = .042), longer ischemic time (HR = 1.374, p = .028), older donor age (HR = 1.158, p = .009), and rejection with hemodynamic compromise within the first year (HR = 2.858, p = .043). Prednisone discontinuation within 1 year was associated with a lower risk of CAV (HR 0.58 p = .047). Initiation of proliferation signal inhibitors (PSI) within 2 years resulted in fewer cases of CAV (HR 0.397 p < .001). CONCLUSION: In patients with an angiogram at 1 year, those with DA were significantly more likely to develop CAV. Lower incidence of CAV by IVUS was seen in patients who discontinued prednisone in the first year or had initiation of a PSI within two years of transplantation. Knowledge of early IVUS may allow a more tailored approach to patient management.