DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present "DenRAM", the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM's dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM's ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems

The brain's connectivity is locally dense and globally sparse, forming a small-world graph-a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We've designed, fabricated, and experimentally demonstrated the Mosaic's building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing.

Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.

Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware.

One gap between the neuro-inspired computing and its applications lies in the intrinsic variability of the devices. Here, Payvand et al. suggest a technologically plausible co-design of the hardware architecture which takes into account and exploits the physics behind memristors.

Bio-Inspired Architectures Substantially Reduce the Memory Requirements of Neural Network Models

We propose a neural network model for the jumping escape response behavior observed in the cricket cercal sensory system. This sensory system processes low-intensity air currents in the animal's immediate environment generated by predators, competitors, and mates. Our model is inspired by decades of physiological and anatomical studies. We compare the performance of our model with a model derived through a universal approximation, or a generic deep learning, approach, and demonstrate that, to achieve the same performance, these models required between one and two orders of magnitude more parameters. Furthermore, since the architecture of the bio-inspired model is defined by a set of logical relations between neurons, we find that the model is open to interpretation and can be understood. This work demonstrates the potential of incorporating bio-inspired architectural motifs, which have evolved in animal nervous systems, into memory efficient neural network models.

Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks

Spiking neural networks (SNNs) are a computational tool in which the information is coded into spikes, as in some parts of the brain, differently from conventional neural networks (NNs) that compute over real-numbers. Therefore, SNNs can implement intelligent information extraction in real-time at the edge of data acquisition and correspond to a complementary solution to conventional NNs working for cloud-computing. Both NN classes face hardware constraints due to limited computing parallelism and separation of logic and memory. Emerging memory devices, like resistive switching memories, phase change memories, or memristive devices in general are strong candidates to remove these hurdles for NN applications. The well-established training procedures of conventional NNs helped in defining the desiderata for memristive device dynamics implementing synaptic units. The generally agreed requirements are a linear evolution of memristive conductance upon stimulation with train of identical pulses and a symmetric conductance change for conductance increase and decrease. Conversely, little work has been done to understand the main properties of memristive devices supporting efficient SNN operation. The reason lies in the lack of a background theory for their training. As a consequence, requirements for NNs have been taken as a reference to develop memristive devices for SNNs. In the present work, we show that, for efficient CMOS/memristive SNNs, the requirements for synaptic memristive dynamics are very different from the needs of a conventional NN. System-level simulations of a SNN trained to classify hand-written digit images through a spike timing dependent plasticity protocol are performed considering various linear and non-linear plausible synaptic memristive dynamics. We consider memristive dynamics bounded by artificial hard conductance values and limited by the natural dynamics evolution toward asymptotic values (soft-boundaries). We quantitatively analyze the impact of resolution and non-linearity properties of the synapses on the network training and classification performance. Finally, we demonstrate that the non-linear synapses with hard boundary values enable higher classification performance and realize the best trade-off between classification accuracy and required training time. With reference to the obtained results, we discuss how memristive devices with non-linear dynamics constitute a technologically convenient solution for the development of on-line SNN training.

Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

The brain performs intelligent tasks with extremely low energy consumption. This work takes its inspiration from two strategies used by the brain to achieve this energy efficiency: the absence of separation between computing and memory functions and reliance on low-precision computation. The emergence of resistive memory technologies indeed provides an opportunity to tightly co-integrate logic and memory in hardware. In parallel, the recently proposed concept of a Binarized Neural Network, where multiplications are replaced by exclusive NOR (XNOR) logic gates, offers a way to implement artificial intelligence using very low precision computation. In this work, we therefore propose a strategy for implementing low-energy Binarized Neural Networks that employs brain-inspired concepts while retaining the energy benefits of digital electronics. We design, fabricate, and test a memory array, including periphery and sensing circuits, that is optimized for this in-memory computing scheme. Our circuit employs hafnium oxide resistive memory integrated in the back end of line of a 130-nm CMOS process, in a two-transistor, two-resistor cell, which allows the exclusive NOR operations of the neural network to be performed directly within the sense amplifiers. We show, based on extensive electrical measurements, that our design allows a reduction in the number of bit errors on the synaptic weights without the use of formal error-correcting codes. We design a whole system using this memory array. We show on standard machine learning tasks (MNIST, CIFAR-10, ImageNet, and an ECG task) that the system has inherent resilience to bit errors. We evidence that its energy consumption is attractive compared to more standard approaches and that it can use memory devices in regimes where they exhibit particularly low programming energy and high endurance. We conclude the work by discussing how it associates biologically plausible ideas with more traditional digital electronics concepts.

Insect-inspired neuromorphic computing

The steady improvement in the performance of computing systems seen for many decades is levelling off as the miniaturization of semiconducting technology approaches the atomic limit, facing severe physical and technological issues. Neuromorphic computing is an emerging solution which makes use of silicon technology in a different way, inline with the computational principles observed in animal nervous systems. In this article, we argue that the nervous systems of insects in particular offer themselves as an ideal starting point for incorporation into realistic neuromorphic systems and review research in developing insect-inspired neuromorphic systems. We conclude with an exciting yet tangible vision of a full neuromorphic sensory-motor system where a liquid state machine modelling the function of the insect mushroom body links input to output and allows for amalgamation of the work discussed in a hierarchical framework of a full system inspired by the concept of how information flows through insects.

Plasma fatty acid profile as biomarker of coronary artery disease: a pilot study using fourth generation artificial neural networks

Many studies, focused on identifying new biomarkers for coronary artery disease (CAD) risk computation and monitoring, suggested a potential diagnostic role for fatty acids (FA). In the present study, we explored the potential diagnostic role of FA by using a data mining approach based on fourth generation artificial neural networks (ANN). Forty-one male subjects were enrolled. According to coronary angiography, 31 displayed CAD and 10 did not (non-CAD, control group). FA analysis was performed on plasma samples using a gas chromatography-mass spectrometry system and analyses were performed by an ANN method. The variables most closely related to CAD were low levels of alpha-linolenic acid, eicosapentaenoic acid, eicosatetraenoic and docosahexaenoic acids. High levels of 1,1-dimethoxyhexadecane, total dimethyl acetals and docosatetraenoic acid were related to non-CAD condition. This subset of variables, which were most closely correlated to the target diagnosis, achieved a consistent predictive rate. The average accuracy obtained was 76.5%, with 93% of sensitivity and 60% of specificity. The area under the ROC curve was equal to 0.79. In conclusion, our study highlighted the association between different plasma FA species, CAD and non-CAD conditions. The specific subset of variables could be of interest as a new diagnostic tool for CAD management.

Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

[This corrects the article on p. 474 in vol. 10, PMID: 27857680.].

A pilot observational study on magnesium and calcium imbalance in elderly patients with acute aortic dissection

Background

Magnesium (Mg) and calcium (Ca) are the principal essential elements involved in endothelial cell homeostasis. Extracellular changes in the levels of either alter endothelial contraction and dilatation. Consequently Mg and Ca imbalance is associated with a high risk of endothelial dysfunction, the main process observed during acute aortic dissection (AAD); in this clinical condition, which mainly affects elderly men, smooth muscle cell alterations lead to intimal tears, creating a false new lumen in the media of the aorta. AAD patients have a high risk of mortality as a result of late diagnosis because often it is not distinguished from other cardiovascular diseases.

We investigated Mg and Ca total circulating levels and the associated pro-inflammatory mediators in elderly AAD patients, to gain further information on the pathophysiology of this disorder, with a view to suggesting newer and earlier potential biomarkers of AAD.

Results

Total circulating Mg and Ca levels were both lower in AAD patients than controls (p < 0.0001). Using Ca as cut-off, 90% of AAD patients with low Ca (<8.4 mg/dL) came into the type A classification of AAD. Stratifying AAD according to this cut-off, Mg was lower in patients with lower total Ca.

Compared to controls, both type A and B AAD patients had higher levels of all the pro-coagulant and pro-inflammatory mediators analyzed, including sP-sel, D-dimer, TNF-α, IL-6, and CRP (p < 0.05). Dividing types A and B using the Stanford classification, no significant differences were found (p > 0.05) The levels of both ICAM-1 and EN-1 were lower in AAD than in a control group (p < 0.0001 and p < 0.05 respectively).

Conclusions

These findings suggest that low Mg and Ca in AAD elderly patients may contribute to altering normal endothelial physiology and also concur in changing the normal concentrations of different mediators involved in vasodilatation and constriction, associated with AAD onset and severity.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Circulating sRAGE in the diagnosis of osteolytic bone metastasis

Despite the clinical importance of metastasis to the skeleton, the diagnostic tools for early detection and monitoring of bone metastasis lack sensitivity and specificity. We evaluated a promising new serum biomarker, the soluble form of the Receptor of Advanced Glycosylated End-products (sRAGE). sRAGE is involved in the Wnt-signaling pathway, and has been reported to reduce the risk of cancer. We investigated the diagnostic potential of sRAGE to improve the detection and monitoring of bone metastasis. We measured sRAGE in the serum of control healthy subjects, patients with primary tumors and patients with bone metastasis. sRAGE was also correlated with the Wnt inhibitors DKK-1 and sclerostin, the bone resorption markers MMP-2, MMP-9 and TRAP5, and the metastatic marker survivin. sRAGE was significantly lower in primary tumor and metastatic patients than in healthy subjects. sRAGE also showed a strong negative correlation with DKK-1, sclerostin, MMP-2, MMP-9, TRAP5b and survivin. These results indicated that sRAGE might play a protective role in bone metastasis progression, and it may diagnostic significance for detecting and monitoring osteolytic metastases.

Acute phase of aortic dissection: a pilot study on CD40L, MPO, and MMP-1, -2, 9 and TIMP-1 circulating levels in elderly patients

Background

Acute aortic dissection (AAD) is an event which may be rapidly fatal without early diagnosis and treatment. Aging is one of the main risk factors that could leading to AAD. To date, no specific biomarkers are available to increase the speed of diagnosis. CD40 ligand (CD40L), myeloperoxidase (MPO), matrix metalloproteinase (MMP)-1, -2, -9 and metallopeptidase tissue inhibitor 1 (TIMP-1) are biologically related molecules which integrate inflammation, tissue injury and remodeling, all events associated to AAD.

Our is a pilot study to evaluate whether circulating levels of these molecules may be used as potential biomarkers in timely diagnosis of AAD.

Results

Within 24 h of symptom onset, circulating CD40L, MPO, MMP-1,-2,-9 and TIMP-1 were quantified by enzyme-linked immunosorbent assays in 22 patients (40–86 years of age) with AAD of ascending aorta (type A according to Stanford classification) and 11 patients with AAD of descending aorta (type B). 30 healthy individuals age matched were used as control group compared to controls, both type A and B AAD patients had higher CD40L (p < 0.001) and MPO (p < 0.01) levels. MMP-1 was higher in the overall AAD group (p < 0.01). After Stanford classification, type A group had increased level compared to both control and type B (p < 0.01 and p < 0.05, respectively). TIMP-1 was higher in both A and B groups compared to controls (p < 0.001). No differences were observed in MMP-2 and MMP-9 levels.

Conclusions

The simultaneous evaluation of CD40L, MPO and MMP-1 and TIMP-1, which may contribute to structural changes in aortic tissue in AAD patients, seems to be a novel promising diagnostic panel.

Epicardial adipocyte hypertrophy: Association with M1-polarization and toll-like receptor pathways in coronary artery disease patients

BACKGROUND AND AIMS

In coronary artery disease (CAD) epicardial adipose tissue (EAT) shows an elevated inflammatory infiltrate. Toll-like receptors (TLRs) are important mediators of adipose tissue inflammation and they are able to recognize endogenous products released by damaged cells. Because adipocyte death may be driven by hypertrophy, our aim was to investigate in CAD and non-CAD patients the association between EAT adipocyte size, macrophage infiltration/polarization and TLR-2 and TLR-4 expression.

METHODS AND RESULTS

EAT biopsies were collected from CAD and non-CAD patients. The adipocyte size was determined by morphometric analysis. Microarray technology was used for gene expression analysis; macrophage phenotype and TLRs expression were analyzed by immunofluorescence and immunohistochemical techniques. Inflammatory mediator levels were determined by immunoassays. EAT adipocytes were larger in CAD than non-CAD patients and do not express perilipin A, a marker of lipid droplet integrity. In CAD, EAT is more infiltrated by CD68-positive cells which are polarized toward an M1 state (CD11c positive) and presents an increased pro-inflammatory profile. Both TLR-2 and TLR-4 expression is higher in EAT from CAD and observed on all the CD68-positive cells.

CONCLUSIONS

Our findings suggested that EAT hypertrophy in CAD promotes adipocyte degeneration and drives local inflammation through increased infiltration of macrophages which are mainly polarized towards an M1 state and express both TLR-2 and TLR-4.

Epicardial adipose tissue inflammation is related to vitamin D deficiency in patients affected by coronary artery disease

BACKGROUND AND AIMS

Alterations in epicardial adipose tissue (EAT) biology (i.e. increased fat thickness and inflammation) have been described in coronary artery disease (CAD) patients. In addition to its classic role in the regulation of calcium-phosphate homeostasis, vitamin D may exert immune-regulatory and anti-inflammatory effects. Whether EAT inflammation may be linked to vitamin D deficiency is still unknown. In the present study we evaluated plasma 25-hydroxycholecalciferol (25OHD) level in CAD patients and its relationship with EAT ability to locally metabolize vitamin D, EAT expression of inflammation-related molecules and EAT thickness.

METHODS AND RESULTS

Plasma 25OHD level was quantified by an immunoluminometric assay. EAT expression of inflammation-related molecules (MCP-1, PTX3, TNFα, IL-6, adiponectin), vitamin D receptor (VDR), CYP27B1 (25OHD-activating enzyme) and CYP24A1 (1,25-dihydroxycholecalciferol-metabolizing enzyme) was performed by microarray. EAT thickness was quantified by echocardiography. Median plasma 25OHD level was 10.85 ng/mL and 83% of CAD patients displayed 25OHD level below 20 ng/mL. At decreasing plasma 25OHD concentration, we observed a down-regulation in CYP27B1 and CYP24A1 level and an increased expression of VDR and pro-inflammatory cytokines (MCP-1, PTX3, TNFα, IL-6) at EAT level. No correlation was observed between plasma 25OHD level and EAT thickness.

CONCLUSION

Our data suggest an increased activation of inflammatory pathways at EAT level possibly related to systemic and local vitamin D deficiency in CAD patients. Whether maintaining an optimal vitamin D status may be helpful to reduce EAT inflammation and to prevent CAD and its progression needs further investigation.

Plasma myeloperoxidase in patients with erectile dysfunction of arteriogenic- and non-arteriogenic origin: association with markers of endothelial dysfunction

Endothelial dysfunction and the disruption of the nitric oxide-cyclic guanosine monophosphate (cGMP) pathway have been considered the early mechanisms for the development of erectile dysfunction (ED). Myeloperoxidase (MPO), a heme-containing enzyme mainly released by activated neutrophils and monocytes, may contribute to endothelial dysfunction by promoting oxidation of different substrates and thus may play a role in ED. MPO level and its correlation with different plasma biomarkers of endothelial dysfunction were studied in patient with ED of arteriogenic (A-ED) and non-arteriogenic (NA-ED) to assess potential differences between the two ED subgroups. Diagnosis of ED was based on the International Index of Erectile Function Score. Its etiology was classified with penile echo-color Doppler at baseline and after intracavernous injection of prostaglandin E1. MPO, soluble (s) cGMP, sICAM-1, sVCAM-1 and sP-Selectin were measured by enzyme-linked immunosorbent assay. MPO concentration in A-ED was significantly higher compared to control subjects and NA-ED patients. Plasmatic cGMP level resulted lower both in A-ED and in NA-ED patients, whereas no difference has been observed between the two ED groups. sICAM-1 concentration resulted higher in A-ED compared both to controls and NA-ED. sVCAM-1 level was the same in controls, A-ED and NA-ED patients. sP-Selectin concentration resulted higher both in A-ED and in NA-ED patients than in controls, whereas no difference has been observed between the two ED groups. Correlation analysis indicated a positive correlation between plasmatic MPO, sICAM-1 and sP-Selectin levels. MPO may represent an important link between oxidation, inflammation and cardiovascular diseases and may also represent a potential marker to distinguish between the two subgroups of ED patients. Moreover, in ED subjects circulating cGMP may reflect the local signaling dysfunction. The use cGMP as a potential marker for monitoring the disease needs further investigation.

Reciprocal regulation of calcium-/phosphate-regulating hormones in cyclists during the Giro d'Italia 3-week stage race

Calcium and phosphate are essential for cell functions, and their serum concentrations result from the balance between intestinal absorption, bony storage, and urinary excretion. Fibroblast growth factor 23 (FGF23), expressed by osteocytes and osteoblasts, acts in the kidney, leading to hypophosphatemia and low 1,25-dihydroxycholecalciferol synthesis, but suppresses parathyroid function. The aim of this study was to explore the effects of a high-energy demanding cycling race on this bone-kidney-parathyroid axis. We studied nine cyclists during the 2011 Giro d'Italia stage race. Pre-analytical and analytical phases followed academic and anti-doping recommendations. Serum parathyroid hormone (PTH), 25(OH)D, total calcium, inorganic phosphorus, and plasma FGF23 were measured on days -1, 12, and 22 and corrected for changes in plasma volume. Dietary calcium and phosphorus, anthropometric parameters (height, weight, and body mass index) and indexes of metabolic effort (net energy expenditure, power output) were recorded. Dietary calcium and phosphorus intakes were kept at the same levels throughout the race. Twenty-five (OH)D, PTH, and calcium concentrations remained stable. FGF23 increased 50% with a positive correlation with the indexes of metabolic effort and, consequently, phosphorous decreased, although only in the first half. The strong metabolic effort acts on the bone-kidney-parathyroid system, and the rise in FGF23 plasma concentration might be aimed at maintaining calcium and phosphorus homeostasis.

IL-18 level in patients undergoing coronary artery bypass grafting surgery or valve replacement: which link with epicardial fat depot?

Interleukin-18 (IL-18) is a member of the interleukin-1 family of cytokines produced constitutively by different cell types and by adipose tissue. Due to the link between obesity, inflammation and cardiovascular diseases, we aimed to measure IL-18 circulating level in patients undergoing open-heart surgery both for elective coronary artery bypass grafting (CABG) or for valve replacement (VR), and we also evaluated whether epicardial adipose tissue (EAT) depot may be a potential source of IL-18. Circulating IL-18 protein was quantified by enzyme-linked immunosorbent assay. IL-18, IL-18 receptor 1 (IL-18 R1) and IL-18 receptor accessory protein (IL-18-RAP) gene expression in EAT depot were evaluated by one colour microarray platform. EAT thickness was measured by echocardiography. In this study we found that all cardiovascular patients (CABG and VR) have increased circulating IL-18 level compared to healthy control subjects (p &#60; 0.0001), but no statistical significant difference was observed between CABG and VR groups (p = 0.35). A great increase in the gene expression of IL-18 (p &#60; 0.05), IL-18 R1 (p &#60; 0.01) and IL-18 RAP (p &#60; 0.001) was observed in EAT samples obtained from CABG vs VR patients. In conclusion, CABG and VR patients had similar increased level of circulating IL-18 protein, but in EAT depots isolated from CABG gene expression of IL-18, IL-18 R1 and IL-18-RAP resulted higher than in VR patients. Future investigation on local IL-18 protein production, its autocrine-paracrine effect and its correlation with plasmatic IL-18 level could give more information on the relationship between IL-18 and coronary artery disease.

Modulation of biofilm of strains isolated from patients with chronic obstructive pulmonary disease by levofloxacin, moxifloxacin, ciprofloxacin, amoxicillin/clavulanic acid and ceftriaxone

The ability of levofloxacin, moxifloxacin, ciprofloxacin, amoxicillin/clavulanic acid and ceftriaxone to interfere on biofilm produced by Pseudomonas aeruginosa, Haemophilus influenzae and Streptococcus pneumoniae isolated from patients with chronic obstructive pulmonary disease was evaluated. The effects of antibiotics were evaluated on formation of biofilm (at 1/2, 1/4 and 1/8 X MIC) and on preformed biofilm (at epithelial lining fluid peak concentrations) by means of a spectrophotometric method. Levofloxacin was the most active compound followed by ciprofloxacin, moxifloxacin and amoxicillin/clavulanic acid and ceftriaxone. Levofloxacin may contribute to clear the reservoir of pathogens involved in chronic obstructive pulmonary disease, thus leading to decreased occurrence of acute exacerbations.

Biodegradable microcarriers as cell delivery vehicle for in vivo transplantation and magnetic resonance monitoring

Microcarrier culture systems offer an attractive method for cell amplification and as delivery vehicle. At the same time, super paramagnetic iron oxide (SPIO) nanoparticles represent a unique in vivo tracking system, already approved for clinical use. In our study, we tested the combination of clinically approved microcarriers and SPIO nanoparticles for cell-construct delivery and subsequent tracking after implantation. In order to mimic better a clinical setting, biodegradable macroporous microcarriers were employed as an alternative approach to expand human primary chondrocytes in a dynamic culture system for subsequent direct transplantation. In addition, cellseeded microcarriers were labeled with SPIO nanoparticles to evaluate the benefits of cell-constructs tracking with magnetic resonance. In vivo subcutaneous implants were monitored for up to 3 weeks and orthotopic implantation was simulated and monitored in ex vivo osteochondral defects.