Total Synthesis of Asparenydiol by Two Sonogashira Cross-Coupling Reactions Promoted by Supported Pd and Cu Catalysts

Asparenydiol, which is an important natural compound with potential pharmacological activities, was synthesized through two Sonogashira cross-coupling reactions catalyzed by supported Pd and Cu catalysts and by a Mitsunobu etherification. The optimization of the Sonogashira couplings allowed the use of catalysts supported on different matrices with good results in terms of catalytic efficiency and yields

Endothelial Dysfunction in Patients with Severe Mitral Regurgitation

Mitral valve prolapse (MVP) is the most common cause of severe mitral regurgitation. It has been reported that MVP patients-candidates for mitral valve repair (MVRep)-showed an alteration in the antioxidant defense systems as well as in the L-arginine metabolic pathway. In this study, we investigate if oxidative stress and endothelial dysfunction are an MVP consequence or driving factors. Forty-five patients undergoing MVRep were evaluated before and 6 months post surgery and compared to 29 controls. Oxidized (GSSG) and reduced (GSH) forms of glutathione, and L-arginine metabolic pathway were analyzed using liquid chromatography-tandem mass spectrometry methods while osteoprotegerin (OPG) through the ELISA kit and circulating endothelial microparticles (EMP) by flow cytometry. Six-month post surgery, in MVP patients, the GSSG/GSH ratio decreased while symmetric and asymmetric dimethylarginines levels remained comparable to the baseline. Conversely, OPG levels significantly increased when compared to their baseline. Finally, pre-MVRep EMP levels were significantly higher in patients than in controls and did not change post surgery. Overall, these results highlight that MVRep completely restores the increased oxidative stress levels, as evidenced in MVP patients. Conversely, no amelioration of endothelial dysfunction was evidenced after surgery. Thus, therapies aimed to restore a proper endothelial function before and after surgical repair could benefit MVP patients

Usefulness of multimodality imaging approach in the diagnosis of mechanical prosthetic valve dysfunction

Background Although the long-term outcome of mechanical mitral and aortic prosthetic valve (M-PV, Ao-PV), PV dysfunction (PVD) remains a very serious complication associated with high morbidity and mortality. Thrombosis/pannus and paravalvular leak are the 2 main mechanisms of PVD. The diagnosis of PVD, based on clinical presentation may be challenging, but it is essential for referring the patient to the optimal treatment (clinical follow-up, thrombolysis, surgery). An integrated multimodality imaging approach, comprising several parameters by transthoracic echocardiography (TTE) and fluoroscopy (F), is mandatory to pursue the correct therapeutic pathway. Purpose This study aims to evaluate the incremental diagnostic value of combined TTE+F over each imaging modality alone in symptomatic pts with Ao-PV or M-PV and high suspicion of PVD. Methods 387 consecutive pts (63\ub111y, 213 Ao-PV, 173 M-PV) suspected for PVD, symptomatic for dyspnea, embolic events, fever or haemolysis were enrolled. All patients were imaged by TTE and F within 2 days after the admission to the hospital. TTE was defined positive for PVD in presence of intra/para-prosthetic regurgitation or high transprosthetic gradient (>20mmHg in Ao-PV, >8mmHg in M-PV) together with altered Doppler parameters (for Ao-PV: DVI <0.25, AT>95ms; for M-PV: Peak Mitral Velocity>2m/sec, VTIPrMV/VTILVO>2.5, PHT>130ms). F was defined positive for PVD when leaflet/s restriction occurs. PVD was confirmed by transoesophageal echocardiography (TOE) or positive response of thrombolysis (T), or surgical inspection (S). Results PVD was found in 46% (99/213) of Ao-PV and in 53% (91/173) of M-PV at TOE/T/S. Sensitivity (SE), specificity (SP), negative predictive value (NPV), positive predictive value (PPV) and diagnostic accuracy (ACC) for TTE, F and combined TTE+F are reported in Table. The integration of TTE+F data significantly improved ACC both for Ao-PV and M-PV. At ROC analysis, the combined model of TTE+F showed the highest AUC for the detection of PVD compared with TTE and F alone (Figure). Table 1. Comparison of diagnostic accuracy between TTE, F, and TTE+F TTE-Ao-PV (n=211) F-Ao_PV (n=204) TTE+F-Ao-PV (n=202) TTE-M-PV (n=172) F-M-PV (n=158) TTE+F-M-PV (n=157) SE / SP / NPV / PPV / ACC (%) 86 / 89 / 88 / 88 / 88 59 / 99 / 72 / 98 / 79 94 / 88 / 94 / 88 / 91 74 / 90 / 75 / 89 / 81 49 / 96 / 60 / 93 / 70 81 / 86 / 78 / 88 / 83 Figure 1. ROC curves Conclusions In patients with clinical suspicion of PVD, TTE and F are both valid tools to evaluate the PV performance. However, the combined model of TTE+F had a significant incremental value over TTE or F alone to diagnose the presence of PVD. This multimodality imaging approach allows to overcome several weaknesses of the TTE or F alone and consequently provides a prompt recognition of PVD even though TOE remains the gold standard to diagnose paravalvular Leak and non-obstructive thrombosis

Machine learning prediction models for mitral valve repairability and mitral regurgitation recurrence in patients undergoing surgical mitral valve repair

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and mod-erate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair

Do rebreathing manoeuvres for non-invasive measurement of cardiac output during maximum exercise test alter the main cardiopulmonary parameters?

Background: Inert gas rebreathing has been recently described as an emergent reliable non-invasive method for cardiac output determination during exercise, allowing a relevant improvement of cardiopulmonary exercise test clinical relevance. For cardiac output measurements by inert gas rebreathing, specific respiratory manoeuvres are needed which might affect pivotal cardiopulmonary exercise test parameters, such as exercise tolerance, oxygen uptake and ventilation vs carbon dioxide output (VE/VCO2) relationship slope. Method: We retrospectively analysed cardiopulmonary exercise testing of 181 heart failure patients who underwent both cardiopulmonary exercise testing and cardiopulmonary exercise test+cardiac output within two months (average 16 \ub1 15 days). All patients were in stable clinical conditions (New York Heart Association I\u2013III) and on optimal medical therapy. Results: The majority of patients were in New York Heart Association Class I and II (78.8%), with a mean left ventricular ejection fraction of 31 \ub1 10%. No difference was found between the two tests in oxygen uptake at peak exercise (1101 (interquartile range 870\u20131418) ml/min at cardiopulmonary exercise test vs 1103 (844\u20131389) at cardiopulmonary exercise test-cardiac output) and at anaerobic threshold. However, anaerobic threshold and peak heart rate, peak workload (75 (58\u2013101) watts and 64 (42\u201390), p < 0.01) and carbon dioxide output were significantly higher at cardiopulmonary exercise testing than at cardiopulmonary exercise test+cardiac output, whereas VE/VCO2 slope was higher at cardiopulmonary exercise test+cardiac output (30 (27\u201335) vs 33 (28\u201337), p < 0.01). Conclusion: The similar anaerobic threshold and peak oxygen uptake in the two tests with a lower peak workload and higher VE/VCO2 slope at cardiopulmonary exercise test+cardiac output suggest a higher respiratory work and consequent demand for respiratory muscle blood flow secondary to the ventilatory manoeuvres. Accordingly, VE/VCO2 slope and peak workload must be evaluated with caution during cardiopulmonary exercise test+cardiac output

Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

Background and aims: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods: Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS&gt;0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results: Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) Conclusions: Deep CNN yielded accurate automated classification of patients with CAD-RADS

Subclinical leaflet thrombosis after transcatheter aortic valve implantation: no association with left ventricular reverse remodeling at 1-year follow-up

Hypo-attenuated leaflet thickening (HALT) of transcatheter aortic valves is detected on multidetector computed tomography (MDCT) and reflects leaflet thrombosis. Whether HALT affects left ventricular (LV) reverse remodeling, a favorable effect of LV afterload reduction after transcatheter aortic valve implantation (TAVI) is unknown. The aim of this study was to examine the association of HALT after TAVI with LV reverse remodeling. In this multicenter case-control study, patients with HALT on MDCT were identified, and patients without HALT were propensity matched for valve type and size, LV ejection fraction (LVEF), sex, age and time of scan. LV dimensions and function were assessed by transthoracic echocardiography before and 12 months after TAVI. Clinical outcomes (stroke or transient ischemic attack, heart failure hospitalization, new-onset atrial fibrillation, all-cause mortality) were recorded. 106 patients (age 81 +/- 7 years, 55% male) with MDCT performed 37 days [IQR 32-52] after TAVI were analyzed (53 patients with HALT and 53 matched controls). Before TAVI, all echocardiographic parameters were similar between the groups. At 12 months follow-up, patients with and without HALT showed a significant reduction in LV end-diastolic volume, LV end-systolic volume and LV mass index (from 125 +/- 37 to 105 +/- 46 g/m(2), p = 0.001 and from 127 +/- 35 to 101 +/- 27 g/m(2), p < 0.001, respectively, p for interaction = 0.48). Moreover, LVEF improved significantly in both groups. In addition, clinical outcomes were not statistically different. Improvement in LVEF and LV reverse remodeling at 12 months after TAVI were not limited by HALT.Cardiolog