Benzo-Extended Cyclohepta[def]fluorene Derivatives with Very Low-Lying Triplet States.

Open-shell non-alternant polycyclic hydrocarbons (PHs) are attracting increasing attention due to their promising applications in organic spintronics and quantum computing. Herein we report the synthesis of three cyclohepta[def]fluorene-based diradicaloids (1-3), by fusion of benzo rings on its periphery for the thermodynamic stabilization, as evidenced by multiple characterization techniques. Remarkably, all of them display a very narrow optical energy gap (Eg opt =0.52-0.69 eV) and persistent stability under ambient conditions (t1/2 =11.7-33.3 h). More importantly, this new type of diradicaloids possess a low-lying triplet state with an extremely small singlet-triplet energy gap, as low as 0.002 kcal mol-1 , with a clear dependence on the molecular size. This family of compounds thus offers a new route to create non-alternant open-shell PHs with high-spin ground states, and opens up novel possibilities and insights into understanding the structure-property relationships.

Atrial Flutter Mechanism Detection Using Directed Network Mapping.

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles.

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

A simple model to detect atrial fibrillation via visual imaging.

Automatic detection of atrial fibrillation (AF) is a challenging issue. In this study we proposed and validated a model to identify AF by using facial video recordings. We analyzed photoplethysmographic imaging (PPGi) signals, extracted from video of a subject's face. Sixty-eight patients were included: 30 in sinus rhythm (SR), 25 in AF and 13 presenting with atrial flutter or frequent ectopic beats (ARR). Twenty-six indexes were computed. The dataset was divided in three subsets: the training, validation, and test set, containing, respectively, 58, 29, and 13% of the data. Mean of inter-systolic interval series (M), Local Maxima Similarity (LMS), and pulse harmonic strength (PHS) indexes were significantly different among all groups. Variability and irregularity parameters had the lowest values in SR, the highest in AF, with intermediate values in ARR. The PHS was higher in SR than in ARR, and higher in ARR than in AF. The LMS index was the highest in SR, intermediate in ARR and the lowest in AF. Similarity indexes were higher in SR than in AF and ARR. A model with three features, namely M, Similarity1 and LMS was chosen. With this model, the accuracy for the validation set was 0.947±0.007 for SR, 0.954±0.004 for AF and 0.919±0.006 for ARR; for the test set (never-seen data), accuracy was 0.876±0.021 for SR, 0.870±0.030 for AF and 0.863±0.029 for ARR. A contactless video-based monitoring can be used to detect AF, differentiating it from SR and from frequent ectopies.

Autonomic function in amnestic and non-amnestic mild cognitive impairment: spectral heart rate variability analysis provides evidence for a brain-heart axis.

Mild cognitive impairment (MCI) is a heterogeneous syndrome with two main clinical subtypes, amnestic (aMCI) and non-amnestic (naMCI). The analysis of heart rate variability (HRV) is a tool to assess autonomic function. Cognitive and autonomic processes are linked via the central autonomic network. Autonomic dysfunction entails several adverse outcomes. However, very few studies have investigated autonomic function in MCI and none have considered MCI subtypes or the relationship of HRV indices with different cognitive domains and structural brain damage. We assessed autonomic function during an active orthostatic challenge in 253 oupatients aged ≥ 65, [n = 82 aMCI, n = 93 naMCI, n = 78 cognitively normal (CN), neuropsychologically tested] with power spectral analysis of HRV. We used visual rating scales to grade cerebrovascular burden and hippocampal/insular atrophy (HA/IA) on neuroimaging. Only aMCI showed a blunted response to orthostasis. Postural changes in normalised low frequency (LF) power and in the LF to high frequency ratio correlated with a memory test (positively) and HA/IA (negatively) in aMCI, and with attention/executive function tests (negatively) and cerebrovascular burden (positively) in naMCI. These results substantiate the view that the ANS is differentially impaired in aMCI and naMCI, consistently with the neuroanatomic substrate of Alzheimer's and small-vessel subcortical ischaemic disease.

Evaluation of the Switch From Amiodarone to Dronedarone in Patients With Atrial Fibrillation: Results of the ARTEMIS AF Studies.

BACKGROUND: Switching between antiarrhythmic drugs is timed to minimize arrhythmia recurrence and adverse reactions. Dronedarone and amiodarone have similar electrophysiological profiles; however, little is known about the optimal timing of switching, given the long half-life of amiodarone. METHODS: The ARTEMIS atrial fibrillation (AF) Loading and Long-term studies evaluated switching patients with paroxysmal/persistent AF from amiodarone to dronedarone. Patients were randomized based on the timing of the switch: immediate, after a 2-week, or after a 4-week washout of amiodarone. Patients who did not convert to sinus rhythm after amiodarone loading underwent electrical cardioversion. The primary objectives were, for the Loading study, to evaluate recurrence of AF ≤60 days; and for the Long-term study, to profile the pharmacokinetics of dronedarone and its metabolite according to different timings of dronedarone initiation. RESULTS: In ARTEMIS AF Loading, 176 were randomized (planned 768) after a 28 ± 2 days load of oral amiodarone. Atrial fibrillation recurrence trended less in the immediate switch versus 4-week washout group (hazard ratio [HR] = 0.65 [97.5% CI: 0.34-1.23]; P = .14) and in the 2-week washout versus the 4-week washout group (HR = 0.75 [97.5% CI: 0.41-1.37]; P = .32). In ARTEMIS AF Long-term, 108 patients were randomized (planned 105). Pharmacokinetic analyses (n = 97) showed no significant differences for dronedarone/SR35021 exposures in the 3 groups. CONCLUSION: The trial was terminated early due to poor recruitment and so our findings are limited by low numbers. However, immediate switching from amiodarone to dronedarone appeared to be well tolerated and safe.

Structured telephone support programs in chronic heart failure may be affected by a learning curve.

AIMS: The efficacy of a telephone support program in chronic heart failure has been questioned particularly when considering short-term follow-up. This study is aimed to assess the impact of the HeartNetCare telephone support program in chronic heart failure within 18 months of observation and to verify a possible effect of a learning-to-care curve on outcome. METHODS: We retrospectively compared a cohort of 269 chronic heart failure patients included in HeartNetCare program with 200 patients receiving usual care as a control group. All-cause death and hospitalization for heart failure or implantation of left ventricular assist device were the primary composite outcome. Secondary endpoints were the changes in left ventricular ejection fraction and in New York Heart Association (NYHA) functional class. Outcome data were also analyzed in relation to the time of enrollment from program initiation. RESULTS: At baseline, HeartNetCare group showed lower ejection fraction and higher NYHA class. At the follow-up, 59 HeartNetCare patients (21.9%) and 49 usual care patients (24.5%) reached the primary endpoint (P = 0.51). After 18 months of follow-up, HeartNetCare patients improved ejection fraction and NYHA class compared with the usual care patients (P < 0.001). We also observed that the HeartNetCare patients enrolled after 12 months from the initiation of the program had a more favorable outcome in primary endpoint compared with controls. CONCLUSION: These results indicate that the long-term impact of a structured telephone support program might be able to positively influence symptoms and reduce events in chronic heart failure. These appreciable effects where more evident after an initial period essential for completing the learning-to-care curve.

CT Perfusion Versus Coronary CT Angiography in Patients With Suspected In-Stent Restenosis or CAD Progression.

OBJECTIVES: The goal of this study was to assess the diagnostic performance of coronary computed tomography angiography (CTA) alone, adenosine-stress myocardial perfusion assessed by computed tomography (CTP) alone, and coronary CTA + CTP by using a 16-cm Z-axis coverage scanner versus invasive coronary angiography (ICA) and fractional flow reserve (FFR) as the clinical standard. BACKGROUND: Diagnostic performance of coronary CTA for in-stent restenosis detection is still challenging. Recently, CTP showed additional diagnostic power over coronary CTA in patients with suspected coronary artery disease. However, few data are available on CTP performance in patients with previous stent implantation. METHODS: Consecutive stable patients with previous coronary stenting referred for ICA were enrolled. All patients underwent stress myocardial CTP and rest CTP + coronary CTA. Invasive FFR was performed during ICA when clinically indicated. The diagnostic rate and diagnostic accuracy of coronary CTA, CTP, and coronary CTA + CTP were evaluated in stent-, territory-, and patient-based analyses. RESULTS: In the 150 enrolled patients (132 men; mean age 65.1 ± 9.1 years), the CTP diagnostic rate was significantly higher than that of coronary CTA in all analyses (territory based [96.7% vs. 91.1%; p < 0.0001] and patient based [96% vs. 68%; p < 0.0001]). When ICA was used as gold standard, CTP diagnostic accuracy was significantly higher than that of coronary CTA in all analyses (territory based [92.1% vs. 85.5%, p < 0.03] and patient based [86.7% vs. 76.7%, p < 0.03]). The concordant coronary CTA + CTP assessment exhibited the highest diagnostic accuracy values versus ICA (95.8% in the territory-based analysis). The diagnostic accuracy of CTP was significantly higher than that of coronary CTA (75% vs. 30.5%; p < 0.001). The radiation exposure of coronary CTA + CTP was 4.15 ± 1.5 mSv. CONCLUSIONS: In patients with coronary stents, CTP significantly improved the diagnostic rate and accuracy of coronary CTA alone compared with both ICA and invasive FFR as gold standard.

Cardiopulmonary exercise testing in a combined screening approach to individuate pulmonary arterial hypertension in systemic sclerosis.

OBJECTIVES: The DETECT algorithm has been developed to identify SSc patients at risk for pulmonary arterial hypertension (PAH) yielding high sensitivity but low specificity, and positive predictive value. We tested whether cardiopulmonary exercise testing (CPET) could improve the performance of the DETECT screening strategy. METHODS: Consecutive SSc patients over a 30-month period were screened with the DETECT algorithm and positive subjects were referred for CPET before the execution of right-heart catheterization. The predictive performance of CPET on top of DETECT was evaluated and internally validated via bootstrap replicates. RESULTS: Out of 314 patients, 96 satisfied the DETECT application criteria and 54 were positive. PAH was ascertained in 17 (31.5%) and pre-capillary pulmonary hypertension in 23 (42.6%) patients. Within CPET variables, the slope of the minute ventilation to carbon dioxide production relationship (VE/VCO2 slope) had the best performance to predict PAH at right-heart catheterization [median (interquartile range) of specificity 0.778 (0.714-0.846), positive predictive value 0.636 (0.556-0.750)]; exploratory analysis on pre-capillary yielded a specificity of 0.714 (0.636-0.8) and positive predictive value of 0.714 (0.636-0.8). CONCLUSION: In association with the DETECT algorithm, CPET may be considered as a useful tool in the workup of SSc-related pulmonary hypertension. The sequential determination of the VE/VCO2 slope in DETECT-positive subjects may reduce the number of unnecessary invasive procedures without any loss in the capability to capture PAH. This strategy had also a remarkable performance in highlighting the presence of pre-capillary pulmonary hypertension.

Quantum units from the topological engineering of molecular graphenoids.

Robustly coherent spin centers that can be integrated into devices are a key ingredient of quantum technologies. Vacancies in semiconductors are excellent candidates, and theory predicts that defects in conjugated carbon materials should also display long coherence times. However, the quantum performance of carbon nanostructures has remained stunted by an inability to alter the sp2-carbon lattice with atomic precision. Here, we demonstrate that topological tailoring leads to superior quantum performance in molecular graphene nanostructures. We unravel the decoherence mechanisms, quantify nuclear and environmental effects, and observe spin-coherence times that outclass most nanomaterials. These results validate long-standing assumptions on the coherent behavior of topological defects in graphene and open up the possibility of introducing controlled quantum-coherent centers in the upcoming generation of carbon-based optoelectronic, electronic, and bioactive systems.

Use of PRECISE-DAPT Score and Admission Platelet Count to Predict Mortality Risk in Patients With Acute Coronary Syndrome.

The PREdicting bleeding Complications In patients undergoing Stent implantation and subsEquent Dual Antiplatelet Therapy (PRECISE-DAPT) score has been validated to predict bleeding complications in patients undergoing stent implantation and dual antiplatelet therapy. This score does not include the platelet count (PC), which has been shown to be an independent marker of mortality in patients with acute coronary syndrome (ACS). We assessed the role of the PRECISE-DAPT score calculated on admission for mortality risk prediction and evaluated whether the predictive accuracy of this score improved by adding the PC. In a retrospective cohort study of 1000 patients with ACS, after adjustment for relevant covariates, a PRECISE-DAPT score ≥25 was independently associated with mortality (hazard ratio [HR]: 7.91; 95% confidence interval [CI]: 4.37-14.30). When this score was combined with PC, compared to patients with PRECISE-DAPT <25 and PC ≥150 × 109/L, the adjusted HR was 7.2 (95% CI 2.4-21.6) for those with PRECISE-DAPT <25 and PC <150 × 109/L; 10.7 (95% CI: 5.2-21.9) for those with PRECISE-DAPT ≥25 and PC ≥150 × 109/L; and 17.9 (95% CI 7.0-45.4) for those with PRECISE-DAPT ≥25 and PC <150 × 109/L. Selecting thresholds for high-risk designation, the PRECISE-DAPT score integrated with PC had a higher prediction value, compared to the PRECISE-DAPT and Global Registry of Acute Coronary Events scores.