A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).     

Pyrosequencing Unveils Cystic Fibrosis Lung Microbiome Differences Associated with a Severe Lung Function Decline

Chronic airway infection is a hallmark feature of cystic fibrosis (CF) disease. In the present study, sputum samples from CF patients were collected and characterized by 16S rRNA gene-targeted approach, to assess how lung microbiota composition changes following a severe decline in lung function. In particular, we compared the airway microbiota of two groups of patients with CF, i.e. patients with a substantial decline in their lung function (SD) and patients with a stable lung function (S). The two groups showed a different bacterial composition, with SD patients reporting a more heterogeneous community than the S ones. Pseudomonas was the dominant genus in both S and SD patients followed by Staphylococcus and Prevotella. Other than the classical CF pathogens and the most commonly identified non-classical genera in CF, we found the presence of the unusual anaerobic genus Sneathia. Moreover, the oligotyping analysis revealed the presence of other minor genera described in CF, highlighting the polymicrobial nature of CF infection. Finally, the analysis of correlation and anti-correlation networks showed the presence of antagonism and ecological independence between members of Pseudomonas genus and the rest of CF airways microbiota, with S patients showing a more interconnected community in S patients than in SD ones. This population structure suggests a higher resilience of S microbiota with respect to SD, which in turn may hinder the potential adverse impact of aggressive pathogens (e.g. Pseudomonas). In conclusion, our findings shed a new light on CF airway microbiota ecology, improving current knowledge about its composition and polymicrobial interactions in patients with CF.     

A Simple,Realistic Stochastic Model of Gastric Emptying

Several models of Gastric Emptying (GE) have been employed in the past to represent the rate of delivery of stomach contents to the duodenum and jejunum. These models have all used a deterministic form (algebraic equations or ordinary differential equations), considering GE as a continuous, smooth process in time. However, GE is known to occur as a sequence of spurts, irregular both in size and in timing. Hence, we formulate a simple stochastic process model, able to represent the irregular decrements of gastric contents after a meal. The model is calibrated on existing literature data and provides consistent predictions of the observed variability in the emptying trajectories. This approach may be useful in metabolic modeling, since it describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.     

Mathematical Modeling of Renal Tubular Glucose Absorption after Glucose Load

A partial differential Progressive Tubular Reabsorption (PTR) model, describing renal tubular glucose reabsorption and urinary glucose excretion following a glucose load perturbation, is proposed and fitted to experimental data from five subjects. For each subject the Glomerular Filtration Rate was estimated and both blood and urine glucose were sampled following an Intra-Venous glucose bolus. The PTR model was compared with a model representing the conventional Renal Threshold Hypothesis (RTH). A delay bladder compartment was introduced in both formulations. For the RTH model, the average threshold for glycosuria varied between 9.90±4.50 mmol/L and 10.63±3.64 mmol/L (mean ± Standard Deviation) under different hypotheses; the corresponding average maximal transport rates varied between 0.48±0.45 mmol/min (86.29±81.22 mg/min) and 0.50±0.42 mmol/min (90.62±76.15 mg/min). For the PTR Model, the average maximal transports rates varied between 0.61±0.52 mmol/min (109.57±93.77 mg/min) and 0.83±0.95 mmol/min (150.13±171.85 mg/min). The time spent by glucose inside the tubules before entering the bladder compartment varied between 1.66±0.73 min and 2.45±1.01 min.The PTR model proved much better than RTH at fitting observations, by correctly reproducing the delay of variations of glycosuria with respect to the driving glycemia, and by predicting non-zero urinary glucose elimination at low glycemias. This model is useful when studying both transients and steady-state glucose elimination as well as in assessing drug-related changes in renal glucose excretion.     

An Optimized Method for Manufacturing a Clinical Scale Dendritic Cell-Based Vaccine for the Treatment of Glioblastoma

Immune-based treatments represent a promising new class of therapy designed to boost the immune system to specifically eradicate malignant cells. Immunotherapy may generate specific anti-tumor immune responses, and dendritic cells (DC), professional antigen-presenting cells, are widely used in experimental cancer immunotherapy. Several reports describe methods for the generation of mature, antigen-pulsed DC for clinical use. Improved quality and standardization are desirable to obtain GMP-compliant protocols. In this study we describe the generation of DC from 31 Glioblastoma (GB) patients starting from their monocytes isolated by immunomagnetic CD14 selection using the CliniMACS® device. Upon differentiation of CD14+ with IL-4 and GM-CSF, DC were induced to maturation with TNF-α, PGE₂, IL-1β, and IL-6. Whole tumor lysate was obtained, for the first time, in a closed system using the semi-automated dissociator GentleMACS®. The yield of proteins improved by 130% compared to the manual dissociation method. Interestingly the Mean Fluorescence Intensity for CD83 increased significantly in DC pulsed with “new method” lysate compared to DC pulsed with “classical method” lysate. Our results indicate that immunomagnetic isolation of CD14⁺ monocytes using the CliniMACS® device and their pulsing with whole tumor lysate proteins is a suitable method for clinical-scale generation of high quality, functional DC under GMP-grade conditions.     

Unraveling amyloid toxicity pathway in NIH3T3 cells by a combined proteomic and 1H‐NMR metabonomic approach

A range of debilitating human diseases is known to be associated with the formation of stable highly organized protein aggregates known as amyloid fibrils. The early prefibrillar aggregates behave as cytotoxic agents and their toxicity appears to result from an intrinsic ability to impair fundamental cellular processes by interacting with cellular membranes, causing oxidative stress and increase in free Ca²⁺ that lead to apoptotic or necrotic cell death. However, specific signaling pathways that underlie amyloid pathogenicity remain still unclear. This work aimed to clarify cell impairment induced by amyloid aggregated. To this end, we used a combined proteomic and one‐dimensional ¹H‐NMR approach on NIH‐3T3 cells exposed to prefibrillar aggregates from the amyloidogenic apomyoglobin mutant W7FW14F. The results indicated that cell exposure to prefibrillar aggregates induces changes of the expression level of proteins and metabolites involved in stress response. The majority of the proteins and metabolites detected are reported to be related to oxidative stress, perturbation of calcium homeostasis, apoptotic and survival pathways, and membrane damage. In conclusion, the combined proteomic and ¹H‐NMR metabonomic approach, described in this study, contributes to unveil novel proteins and metabolites that could take part to the general framework of the toxicity induced by amyloid aggregates. These findings offer new insights in therapeutic and diagnostic opportunities. J. Cell. Physiol. 228: 1359–1367, 2013. © 2012 Wiley Periodicals, Inc.