A modality-specific somatosensory evoked potential test protocol for clinical evaluation: A feasibility study

ObjectiveWe aimed to establish an objective neurophysiological test protocol that can be used to assess the somatosensory nervous system.MethodsIn order to assess most fiber subtypes of the somatosensory nervous system, repetitive stimuli of seven different modalities (touch, vibration, pinprick, cold, contact heat, laser, and warmth) were synchronized with the electroencephalogram (EEG) and applied on the cheek and dorsum of the hand and dorsum of the foot in 21 healthy subjects and three polyneuropathy (PNP) patients. Latencies and amplitudes of the modalities were assessed and compared. Patients received quantitative sensory testing (QST) as reference.ResultsWe found reproducible evoked potentials recordings for touch, vibration, pinprick, contact-heat, and laser stimuli. The recording of warm-evoked potentials was challenging in young healthy subjects and not applicable in patients. Latencies were shortest within Aβ-fiber-mediated signals and longest within C-fibers. The test protocol detected function loss within the Aβ-fiber and Aδ-fiber-range in PNP patients. This function loss corresponded with QST findings.ConclusionIn this pilot study, we developed a neurophysiological test protocol that can specifically assess most of the somatosensory modalities. Despite technical challenges, initial patient data appear promising regarding a possible future clinical application.SignificanceEstablished and custom-made stimulators were combined to assess different fiber subtypes of the somatosensory nervous system using modality-specific evoked potentials.     

The critical role of the inferior frontal cortex in establishing a prediction model for generating subsequent mismatch negativity (MMN): A TMS-EEG study

BackgroundThe prediction violation account of automatic or pre-attentive change detection assumed that the inferior frontal cortex (IFC) is involved in establishing a prediction model for detecting unexpected changes. Evidence supporting the IFC’s contribution to prediction model is mainly based on the Mismatch Negativity (MMN) to deviants violating predictions that are established based on the frequently presented standard events. However, deviant detection involves processes, such as events comparison, other than prediction model establishment.ObjectiveThe current study investigated the critical role of the IFC in establishing a prediction model during standards processing for subsequent deviant detection.MethodsTranscranial Magnetic Stimulation (TMS) was applied at the IFC to disrupt the processing of the initial 2 or 5 standards of a 3-, 6-, or 9-standard train, while the MMN responses to pitch deviant presented after the standard trains were recorded and compared.ResultsAn abolishment of MMN was only observed when TMS was delivered to the IFC at the initial 2 standards of the 3-standard train, but not at the initial 5 standards, or when TMS at the vertex or TMS sound recording was applied. The MMNs were also preserved when IFC TMS, vertex TMS, or TMS sound recording was applied at the initial 2 or 5 standards of longer trains.ConclusionThe IFC plays a critical role in processing the initial standards of a short standard train for subsequent deviant detection. This result is consistent with the prediction violation account that the IFC is important for establishing the prediction model.     

Transarterial Chemoembolization vs Radioembolization for Neuroendocrine Liver Metastases: A Multi-Institutional Analysis

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Angiographic profile and outcomes in persistent non-valvular atrial fibrillation: A study from tertiary care center in North India

BackgroundThe relationship of atrial fibrillation (AF) with coronary artery disease (CAD) is well established, yet it is often missed. There is evidence of myocardial ischemia on stress imaging in AF patients in the absence of obstructive CAD. In this prospective cohort, we studied the angiographic profiles of non-valvular AF patients.MethodsThe study was a nonrandomized, prospective, single-center observational study of consecutive patients of persistent non-valvular AF. Patients symptomatic for AF despite optimal medical therapy for 3 months were recruited and all underwent coronary angiograms (CAG). Patients with prior history of CAD were excluded.ResultsA total of 70 patients were followed for a mean duration of 12 ± 1.4 months. The mean age of the study group was 66.07 (±11.49) years. Hypertension was the commonest comorbidity seen in 74% patients. Obstructive CAD was present in 32 (46%) patients, non-obstructive (<50% stenosis) CAD in 17 (24%) patients and normal coronaries in 21 (30%) patients. Overall 49 (70%) patients had evidence of CAD. Amongst patients without obstructive CAD, slow flow was seen in 16 (42%) patients. Lower baseline ejection fraction, lower haemoglobin & albumin levels and higher creatinine levels was associated with increased mortality. In patients without obstructive CAD, hospitalizations for fast ventricular rate were significantly increased in those having slow flow on CAG (p = 0.005).ConclusionsMajority (70%) of our patients had evidence of atherosclerotic CAD on CAG. A large proportion of patients without obstructive CAD had slow flow on CAG.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).     

Associations between heart rate variability and maximal fat oxidation in two different cohorts of healthy sedentary adults

Background and aimsResting heart rate variability (HRV) and maximal fat oxidation (MFO) during exercise are both considered as a noninvasive biomarkers for early detection of cardiovascular risk factors. Thus, this study aimed to analyze the relationship between resting HRV parameters and MFO during exercise, and the intensity of exercise that elicit MFO (Fat_max) in healthy sedentary adults.Methods and resultsA total of 103 healthy young adults (22.2 ± 2.3 years old, 67% female; from the ACTIBATE cohort) and 67 healthy middle-aged adults (53.1 ± 5.0 years old, 52% female; from the FIT-AGEING cohort) were included in this cross-sectional study. HRV was assessed using a Polar RS800CX heart rate monitor, while MFO and Fat_max were determined during a graded exercise treadmill test using indirect calorimetry. No significant associations were observed for healthy young adults (standardized β coefficients ranged from ?0.063 to 0.094, and all P ≥ 0.347) and for middle-aged adults (standardized β coefficients ranged from ?0.234 to 0.090, and all P ≥ 0.056). Nevertheless, only a weak association was observed between one HRV parameter in time-domain (the percentage of R-R intervals that shows a difference higher than 50 ms [pNN50]) and MFO in the cohort of middle-aged adults (β coefficient = ?0.279, and P = 0.033).ConclusionThe results of this study suggest that resting HRV parameters are not associated with MFO and Fat_max during exercise in two independent cohorts of healthy sedentary young and middle-aged adults, respectively.     

On estimands arising from misspecified semiparametric rate-based analysis of recurrent episodic conditions

Marginal rate-based analyses are widely used for the analysis of recurrent events in clinical trials. In many areas of application, the events are not instantaneous but rather signal the onset of a symptomatic episode representing a recurrent infection, respiratory exacerbation, or bout of acute depression. In rate-based analyses, it is unclear how to best handle the time during which individuals are experiencing symptoms and hence are not at risk. We derive the limiting value of the Nelson-Aalen estimator and estimators of the regression coefficients under a semiparametric rate-based model in terms of an underlying two-state process. We investigate the impact of the distribution of the episode durations, heterogeneity, and dependence on the asymptotic and finite sample properties of standard estimators. We also consider the impact of these features on power in trials designed to test intervention effects on rate functions. An application to a trial of individuals with herpes simplex virus is given for illustration.