MATERIALS AND METHODS: Atrial arrhythmogenesis was investigated in Langendorff-perfused young (3-4 month) and aged (>12 month), wild type (WT) and peroxisome proliferator activated receptor-γ coactivator-1β deficient (Pgc-1β-/-) murine hearts modeling age-dependent chronic mitochondrial dysfunction during regular pacing and programmed electrical stimulation (PES).
RESULTS AND DISCUSSION: The Pgc-1β-/- genotype was associated with a pro-arrhythmic phenotype progressing with age. Young and aged Pgc-1β-/- hearts showed compromised maximum action potential (AP) depolarization rates, (dV/dt)max, prolonged AP latencies reflecting slowed action potential (AP) conduction, similar effective refractory periods and baseline action potential durations (APD90) but shortened APD90 in APs in response to extrasystolic stimuli at short stimulation intervals. Electrical properties of APs triggering arrhythmia were similar in WT and Pgc-1β-/- hearts. Pgc-1β-/- hearts showed accelerated age-dependent fibrotic change relative to WT, with young Pgc-1β-/- hearts displaying similar fibrotic change as aged WT, and aged Pgc-1β-/- hearts the greatest fibrotic change. Mitochondrial deficits thus result in an arrhythmic substrate, through slowed AP conduction and altered repolarisation characteristics, arising from alterations in electrophysiological properties and accelerated structural change.
METHODS: This was an observational manikin-based study. A total of 96 participants as well as two types of mechanical compression devices: Lucas-2 and AutoPulse, performed one minute of continuous chest compression on BT-CPEA programmed manikin while the ambulance travelled at different speeds, i.e., idle state, 30km/hr and 60km/hr. Seven outcome variables of chest compression were measured. Performance data of different groups of compressor were compared and analysed using repeated measures analysis of variance (ANOVA).
RESULTS: In manual chest compression, significant variation were noted among different speeds in term of average compression rate (p<0.001), average compression depth (p=0.007), fraction of adequate/insufficient compression depth and fraction of normal hands positioning with p=0.018, 0.022 and 0.034 respectively. Overall, AutoPulse and Lucas-2 were not affected by ambulance speed. Lucas- 2 showed more consistent average compression rate, higher fraction of adequate compression depth and reduced fraction of insufficient compression depth as compared to manual compression with p<0.001, 0.001 and 0.043 respectively.
CONCLUSION: In this study we found that ambulance speed significantly affected certain aspects of manual chest compression most notably compression depth, rate and hand positioning. AutoPulse and Lucas-2 can improve these aspects by providing more consistent compression rate, depth and fraction of adequate compression depth during transport.
METHODS: This was a single-center, retrospective study. Echocardiographic assessment of the LV geometry, mass, and free wall thickness was performed before stenting and before the arterial switch operation. Patients then underwent the arterial switch operation, and the postoperative outcomes were reviewed.
RESULTS: There were 11 consecutive patients (male, 81.8%; mean age at stenting, 43.11 ± 18.19 days) with TGA-IVS with involuted LV who underwent LV retraining by ductal stenting from July 2013 to December 2017. Retraining by ductus stenting failed in 4 patients (36.3%). Two patients required pulmonary artery banding, and another 2 had an LV mass index of less than 35 g/m2. Patients in the successful group had improved LV mass index from 45.14 ± 17.91 to 81.86 ± 33.11g/m2 (p = 0.023) compared with 34.50 ± 10.47 to 20.50 ± 9.88 g/m2 (p = 0.169) and improved LV geometry after ductal stenting. The failed group was associated with an increased need for extracorporeal support (14.5% vs 50%, p = 0.012). An atrial septal defect-to-interatrial septum length ratio of more than 0.38 was associated with failed LV retraining.
CONCLUSIONS: Ductal stenting is an effective method to retrain the involuted LV in TGA-IVS. A large atrial septal defect (atrial septal defect-to-interatrial septum length ratio >0.38) was associated with poor response to LV retraining.
METHODS: This is a retrospective study done in neonates and infants up to 3 months of age with duct-dependent pulmonary circulation who underwent DS from January 2014 to December 2015. Post-stenting PA growth, surgical outcomes of PA reconstruction, post-surgical re-interventions, morbidity and mortality were analysed.
RESULTS: During the study period, 46 patients underwent successful DS, of whom 38 underwent presurgery catheterization and definite surgery. There was significant growth of PAs in these patients. Biventricular repair was done in 31 patients while 7 had univentricular palliation. Left PA augmentation was required in 13 patients, and 10 required central PA augmentation during surgery. The mean follow-up period post-surgery was 4.5 ± 1.5 years. No significant postoperative complications were seen. No early or follow-up post-surgery mortality was seen. Four patients required re-interventions in the form of left PA stenting based on the echocardiography or computed tomography evidence of significant stenosis.
CONCLUSIONS: DS provides good short-term palliation and the growth of PAs. However, a significant number of stented patients require reparative procedure on PAs at the time of surgical intervention. Acquired changes in the PAs following DS may be the reason for reintervention following PA reconstruction.
Methods: Twenty-seven patients with history of anterior myocardial infarction (MI) and baseline left ventricular ejection fraction (LVEF) of less than 35% were recruited into this study. Patients who are eligible for revascularization were grouped into group A (MSCs infusion with concurrent revascularization) or group B (revascularization only) while patients who were not eligible for revascularization were allocated in group C to receive intracoronary MSCs infusion. LV function was measured using echocardiography.
Results: Patients who received MSCs infusion (either with or without revascularization) demonstrated significant LVEF improvements at 3, 6 and 12 months post-infusion when compared to baseline LVEF within its own group. When comparing the groups, the magnitude of change in LVEF from baseline for third visits i.e., 12 months post-infusion was significant for patients who received MSCs infusion plus concurrent revascularization in comparison to patients who only had the revascularization procedure.
Conclusions: MSCs infusion significantly improves LV function in ICM patients. MSCs infusion plus concurrent revascularization procedure worked synergistically to improve cardiac function in patients with severe ICM.
METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.
RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).
CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.