Interventricular mechanical dyssynchrony: a novel marker of cardiac events in Brugada syndrome.

BACKGROUND: Risk stratification in Brugada syndrome is controversial, especially in asymptomatic individuals. OBJECTIVE: The purpose of this study was to evaluate tissue Doppler echocardiography in risk stratification of Brugada syndrome. METHODS: Patients with Brugada ECG pattern were enrolled in the study. Left ventricular (LV) preejection period was defined as the time interval between onset of the QRS complex and onset of LV lateral wall systolic wave. Right ventricular (RV) preejection period was defined as the time interval between onset of the QRS complex and onset of RV lateral wall systolic wave. Delay in onset of contraction between RV and LV was defined as RV preejection time - LV preejection time [PET((RV-LV))]. RESULTS: Type 1, 2, and 3 Brugada ECG pattern was found in 30, 56, and 31 patients, respectively. PET((RV-LV)) was significantly greater in type 1 Brugada patients (39.2 +/- 3.2 ms) compared with type 2 (5 +/- 0.3 ms) and 3 (5 +/- 0.4 ms) Brugada patients as well as controls (4.6 +/- 0.3 ms, P <.01 for all comparisons). Among type 1 Brugada patients, PET((RV-LV)) was significantly greater in patients who had previous cardiac events compared with asymptomatic subjects (48.2 +/- 4.3 ms vs 29.5 +/- 3.6 ms, P <.05). In the presence of type 1 Brugada ECG pattern, PET((RV-LV)) > or =40 ms identifies patients likely to have cardiac events, with 85.7% sensitivity and 93.7% specificity. CONCLUSION: PET((RV-LV)) is an important risk indicator for Brugada syndrome.     

Physical and pH properties of gastroesophagopharyngeal refluxate: a 24-hour simultaneous ambulatory impedance and pH monitoring study

OBJECTIVES: Frequency occurrence of nonacidic and nonliquid reflux events in the pharynx has not been systematically studied. The aim of the present study was to characterize the physical (liquid, gas, and mixed gas/liquid) and pH properties of the gastroesophagopharyngeal refluxate. METHODS: We performed a total of 31 24-h simultaneous ambulatory pharyngoesophageal impedance and pH recordings in 11 GERD patients, 10 patients with reflux-attributed laryngitis, and 10 healthy controls. RESULTS: On average, the total number of reflux events (all kinds) in the pharynx was less than half of that in the proximal esophagus (18 +/- 4 vs 50 +/- 4, p < 0.01). Most of the pharyngeal reflux events were gas events and were observed in all three studied groups. Prevalence of these gas reflux events ranged between 0 and 74. The number of gas reflux events accompanied by a minor pH drop in laryngitis patients (1 (0-36)) was significantly higher than those in GERD and controls (0 (0-2) and 0 (0-1), respectively, p < 0.05). There was no significant difference in the number of nonacidic gas reflux events among the three groups (GERD: 10 (2-57), laryngitis: 11.5 (0-51), controls: 10.5 (0-27)). Impedance recording identified a total number of 566 events in the pharynx. Of these, a total of 563 events were compatible with gas reflux events, 101 events were accompanied by minor drops in intrapharyngeal pH, whereas 460 events were not accompanied by any pharyngeal pH change. CONCLUSIONS: Concurrent impedance and pH recordings detect significantly more events qualifying as reflux in the pharynx than pH recordings alone. A substantial majority of these events are gaseous refluxes both with and without minor pH drops. Gas reflux events with weak acidity appear to be more common among patients with reflux-attributed laryngeal lesions compared to GERD patients and controls.     

Morphological modeling of cardiac signals based on signal decomposition

In this paper a general framework is presented for morphological modeling of cardiac signals from a signal decomposition perspective. General properties of a desired morphological model are presented and special cases of the model are studied in detail. The presented approach is studied for modeling the morphology of electrocardiogram (ECG) signals. Specifically, three types of ECG modeling techniques, including polynomial spline models, sinusoidal model and a model previously presented by McSharry et al., are studied within this framework. The proposed method is applied to datasets from the PhysioNet ECG database for compression and modeling of normal and abnormal ECG signals. Quantitative and qualitative results of these applications are also presented and discussed.     

Tumor-derived microvesicles: The metastasomes

Metastasis is the leading cause of cancer death, yet it is mechanistically considered a very inefficient process suggesting the presence of some sort of (e.g. systemic) routes for fuelling the process. The pre-metastatic niche formation is described as one such metastasis promoting route. Now, the emerging potentials of tumor-derived microvesicles (TDMVs), not only in formulating the pre-metastatic niche, but also conferring neoplastic phenotypes onto normal cells, has integrated new concepts into the field. Here, we note as an ancillary proposition that, exerting functional disturbances in other sites, TDMVs (we have termed them metastasomes) may aid foundation of the secondary lesions via two seemingly interrelated models: (i) tumor-organ-training (TOTr), training a proper niche for the growth of the disseminated tumor cells; (ii) tumor-organ-targeting (TOTa), contribution to the propagation of the transformed phenotype via direct or indirect (TOTr-mediated disturbed stroma) transformation and/or heightened growth/survival states of the normal resident cells in the secondary organs. Respecting the high content of the RNA molecules (particularly microRNAs) identified in the secretory MVs, they may play crucial parts in such “malignant trait” spreading system. That is, the interactions between tumor tissue-specific RNA signatures, being transferred via metastasomes, and the cell-type/tissue-specific RNA stockrooms in other areas may settle a unique outcome in each organ. Thus, serving as tumor-organ matchmakers, the RNA molecules may also play substantial roles in the seeding and tropism of the process.     

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

     

Neurological manifestations of COVID-19: with emphasis on Iranian patients

Journal of NeuroVirology - The novel coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has instigated a global pandemic as a formidable...     

Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine

PurposeTo compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA).Materials and MethodsSix pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement.ResultsQuantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078). The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08).ConclusionOur feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.