Abstract Context Representations in Primate Amygdala and Prefrontal Cortex.

Neurons in prefrontal cortex (PFC) encode rules, goals, and other abstract information thought to underlie cognitive, emotional, and behavioral flexibility. Here we show that the amygdala, a brain area traditionally thought to mediate emotions, also encodes abstract information that could underlie this flexibility. Monkeys performed a task in which stimulus-reinforcement contingencies varied between two sets of associations, each defining a context. Reinforcement prediction required identifying a stimulus and knowing the current context. Behavioral evidence indicated that monkeys utilized this information to perform inference and adjust their behavior. Neural representations in both amygdala and PFC reflected the linked sets of associations implicitly defining each context, a process requiring a level of abstraction characteristic of cognitive operations. Surprisingly, when errors were made, the context signal weakened substantially in the amygdala. These data emphasize the importance of maintaining abstract cognitive information in the amygdala to support flexible behavior.

Correlation between in vivo stresses and oxidation of UHMWPE in total hip arthroplasty.

The possibility of in vivo, stress-induced oxidation in orthopaedic UHMWPE has been investigated. EtO sterilised, uncrosslinked UHMWPE liners, explanted or shelf-aged, have been collected. Linear wear and wear rate were assessed and FTIR spectroscopy was employed to detect oxidation and to build up oxidation products spatial maps across the liners section. Oxidation profiles have been compared to stress distribution profiles, resulting from a FE analysis conducted on the same liners geometries and couplings. It was found that oxidised and stressed areas followed the same asymmetrical, localized distribution profile. It was therefore possible to establish a correlation between stressed areas and observed oxidation.

Hybrid diffusive/PVD treatments to improve the tribological resistance of Ti-6Al-4V.

Titanium alloys are nowadays used for a wide range of biomedical applications thanks to their combination of high mechanical resistance, high corrosion resistance and biocompatibility. Nevertheless, the applicability of titanium alloys is sometimes limited due to their low microhardness and tribological resistance. Thus the titanium alloys cannot be successfully applied to prosthetic joint couplings. A wide range of surface treatments, in particular PVD coatings such as CrN and TiN, have been used in order to improve the tribological behaviour of titanium alloys. However, the low microhardness of the titanium substrate often results in coating failure due to cracks and delamination. For this reason, hybrid technologies based on diffusive treatments and subsequent PVD coatings may improve the overall coating resistance. In this work, conventional PVD coatings of CrN or TiCN, deposited on Titanium Grade 5, were characterized and then combined with a standard thermal diffusive nitriding treatment in order to improve the tribological resistance of the titanium alloys and avoid coating delamination. The different treatments were studied by means of scanning electron microscopy both on the sample surface and in cross-section. In-depth composition profiles were obtained using glow discharge optical emission spectrometry (GDOES) and localized energy dispersive X-ray diffraction on linear scan-lines. The microhardness and adhesion properties of the different treatments were evaluated using Vickers microhardness tests at different load conditions. The indentations were observed by means of SEM in order to evaluate delaminated areas and the crack's shape and density. The tribological behaviour of the different treatments was tested in dry conditions and in solution, in alternate pin-on-flat configuration, with a frequency of 0.5 Hz. After testing, the surface was investigated by means of stylus profilometry and SEM both on the surface and in cross-section. The standalone PVD coatings show a limited tribological resistance due to the low hardness of the substrate, which results in fractures and delamination. The combination of a diffusive process and a subsequent PVD coating shows a stronger effect in improving the tribological resistance of the substrate.

Characterization of grade 2 commercially pure Trabecular Titanium structures.

In this work, two different cellular solid structures, obtained by EBM using grade 2 Titanium powders, were investigated. Relative density was evaluated comparing different methods, the mean diameter of the singular open porosity was calculated by SEM image post-processing; the chemical composition was evaluated using Energy Dispersive X-Ray Spectroscopy (EDXS); the microstructure and grain dimension were investigated using chemical etching and, the mechanical properties were investigated using UMTS 810 Materials Test System. The mean porosity values resulted to be similar to spongy bone (around 77% for sample A and 89% for sample B). The mean diameter of the single porosity resulted to be 640 µm for A and 1250 µm for B. The Vickers microhardness results were homogeneous among the structure and the chemical etching showed a complex microstructure characterized by irregular shaped grains. Sample A, as expected, is more resistant than sample B, while sample B shows a lower elastic modulus.

Characterization of cellular solids in Ti6Al4V for orthopaedic implant applications: Trabecular titanium.

EBM (Electron Beam Melting) technology can be used successfully to obtain cellular solids in metallic biomaterials that can greatly increase osseointegration in arthroprothesis and at the same time maintain good mechanical properties. The investigated structures, called Trabecular Titanium, usually cannot be obtained by traditional machining. Two samples: (A) with a smaller single cell area and, (B) with a bigger single cell area, were produced and studied in this project. They have been completely characterized and compared with the results in similar literature pertinent to Ti6Al4V EBM structures. Relative density was evaluated using different methods, the mean diameter of the open porosities was calculated by Scanning Electron Microscope images; the composition was evaluated using Energy-Dispersive X-Ray Spectroscopy; the microstructure (alpha-beta) was investigated using chemical etching and, the mechanical proprieties were investigated using UMTS. The mean porosity values resulted comparable with spongy bone (63% for A and 72% for B). The mean diameter of the single porosity (650 mum for A and 1400 mum for B) resulted compatible with the osseointegration data from the literature, in particular for sample A. The Vickers micro-hardness tests and the chemical etching demonstrated that the structure is fine, uniform and well distributed. The mechanical test proved that sample (A) was more resistant than sample (B), but sample (B) showed an elastic modulus almost equal to the value of spongy bone. The results of this study suggest that the two Ti6Al4V cellular solids can be used in biomedical applications to promote osseointegration demonstrating that they maybe successfully used in prosthetic implants. Additional implant results will be published in the near future.

Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.

Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.

Cardioventilatory responses during real or imagined walking at low speed.

There is increasing evidence that motor imagery involves at least in part central processes used in motor control. In order to deepen our understanding on the neural mechanisms underlying vegetative responses to real and imagined exercise, we determined cardioventilatory variables during actual or imagined treadmill walking on flat terrain at speeds of 2, 3.5 or 5 km/h, in a group of 14 healthy volunteers. During actual walking, as expected, a comparable intensity-dependent increase was found in ventilation, oxygen consumption, tidal volume and respiratory rate. Imagined walking led to a significant, albeit small (less than 10%), increase in ventilation and oxygen consumption, and to larger increases (up to 40%) in respiratory rate, which was paralleled by a non significant trend towards a decline of tidal volume. These results confirm and extend previous observations showing that motor imagery is accompanied by centrally induced changes in vegetative responses, and provide evidence for a differential control on respiratory rate and tidal volume.

Sprint running: a new energetic approach.

The speed of the initial 30 m of an all-out run from a stationary start on a flat track was determined for 12 medium level male sprinters by means of a radar device. The peak speed of 9.46+/-0.19 m s(-1) (mean +/- s.d.) was attained after about 5 s, the highest forward acceleration (a(f)), attained immediately after the start, amounting to 6.42+/-0.61 m s(-2). During acceleration, the runner's body (assumed to coincide with the segment joining the centre of mass and the point of contact foot terrain) must lean forward, as compared to constant speed running, by an angle alpha = arctang/a(f) (g = acceleration of gravity). The complement (90-alpha) is the angle, with respect to the horizontal, by which the terrain should be tilted upwards to bring the runner's body to a position identical to that of constant speed running. Therefore, accelerated running is similar to running at constant speed up an ;equivalent slope' ES = tan(90-alpha). Maximum ES was 0.643+/-0.059. Knowledge of ES allowed us to estimate the energy cost of sprint running (C(sr), J kg(-1) m(-1)) from literature data on the energy cost measured during uphill running at constant speed. Peak Csr was 43.8+/-10.4 J kg(-1) m(-1); its average over the acceleration phase (30 m) was 10.7+/-0.59 J kg(-1) m(-1), as compared with 3.8 for running at constant speed on flat terrain. The corresponding metabolic powers (in W kg(-1)) amounted to 91.9+/-20.5 (peak) and 61.0+/-4.7 (mean).

Development of comprehensive functional genomic screens to identify novel mediators of osteoarthritis.

OBJECTIVE: The aim of this study was to develop high-throughput assays for the analysis of major chondrocyte functions that are important in osteoarthritis (OA) pathogenesis and methods for high-level gene expression and analysis in primary human chondrocytes. METHODS: In the first approach, complementary DNA (cDNA) libraries were constructed from OA cartilage RNA and full-length clones were selected. These cDNAs were transferred into a retroviral vector using Gateway Technology. Full-length clones were over-expressed in human articular chondrocytes (HAC) by retroviral-mediated gene transfer. The induction of OA-associated markers, including aggrecanase-1 (Agg-1), matrix metalloproteinase-13 (MMP-13), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), collagen IIA and collagen X was measured by quantitative real-time polymerase chain reaction (QPCR). Induction of a marker gene was verified by independent isolation of 2-3 clones per gene, re-transfection followed by QPCR as well as nucleotide sequencing. In the second approach, whole cDNA libraries were transduced into chondrocytes and screened for chondrocyte cluster formation in three-dimensional agarose cultures. RESULTS: Using green fluorescent protein (eGFP) as a marker gene, it was shown that the retroviral method has a transduction efficiency of >90%. A total of 40 verified hits were identified in the QPCR screen. The first set of 19 hits coordinately induced iNOS, COX-2, Agg-1 and MMP-13. The most potent of these genes were the tyrosine kinases Axl and Tyro-3, receptor interacting kinase-2 (RIPK2), tumor necrosis factor receptor 1A (TNFR1A), fibroblast growth factor (FGF) and its receptor FGFR, MUS81 endonuclease and Sentrin/SUMO-specific protease 3. The second set of seven hits induced both Agg-1 and MMP-13 but none of the other markers. Five of these seven genes regulate the phosphoinositide-3-kinase pathway. The most potently induced OA marker was iNOS. This marker was induced 20-500 fold by seven genes. Collagen IIA was also induced by seven genes, the most potent being transforming growth factor beta (TGFbeta)-stimulated protein TSC22, vascular endothelial growth factor (VEGF) and splicing factor 3a. This screening assay did not identify inducers of collagen X. The second chondrocyte cluster formation screen identified 14 verified hits. Most of the genes inducing cluster formation were kinases. Additional genes had not been previously known to regulate chondrocyte cluster formation or any other chondrocyte function. CONCLUSIONS: The methods developed in this study can be applied to screen for genes capable of inducing an OA-like phenotype in chondrocytes on a genome-wide scale and identify novel mediators of OA pathogenesis. Thus, coordinated functional genomic approaches can be used to delineate key genes and pathways activated in complex human diseases such as OA.

Multiple views of the response of an ensemble of spectro-temporal features support concurrent classification of utterance, prosody, sex and speaker identity.

Models of auditory processing, particularly of speech, face many difficulties. These difficulties include variability among speakers, variability in speech rate and robustness to moderate distortions such as time compression. In contrast to the 'invariance of percept' (across different speakers, of different sexes, using different intonation, and so on) is the observation that we are sensitive to the identity, sex and intonation of the speaker. In previous work we have reported that a model based on ensembles of spectro-temporal feature detectors, derived from onset sensitive pre-processing of a limited class of stimuli, preserves significant information about the stimulus class. We have also shown that this is robust with respect to the exact choice of feature set, moderate time compression in the stimulus and speaker variation. Here we extend these results to show a) that by using a classifier based on a network of spiking neurons with spike-driven plasticity, the output of the ensemble constitutes an effective rate coding representation of complex sounds; and b) that the same set of spectro-temporal features concurrently preserve information about a range of qualitatively different classes into which the stimulus might fall. We show that it is possible for multiple views of the same pattern of responses to generate different percepts. This is consistent with suggestions that multiple parallel processes exist within the auditory 'what' pathway with attentional modulation enhancing the task-relevant classification type. We also show that the responses of the ensemble are sparse in the sense that a small number of features respond for each stimulus type. This has implications for the ensembles' ability to generalise, and to respond differentially to a wide variety of stimulus classes.

Spike-driven synaptic plasticity for learning correlated patterns of mean firing rates.

Long term synaptic changes induced by neural spike activity are believed to underlie learning and memory. Spike-driven long-term synaptic plasticity has been investigated in simplified situations in which the patterns of mean rates to be encoded were statistically independent. An additional regulatory mechanism is required to extend the learning capability to more complex and natural stimuli. This mechanism can be provided by those effects of the action potentials that are believed to be responsible for spike-timing dependent plasticity. These effects, when combined with the dependence of synaptic plasticity on the post-synaptic depolarization, produce the non-monotonic learning rule needed for storing correlated patterns of mean rates.

A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory.

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network.

The locomotory index: a new proposal for evaluating walking impairments.

The energy cost of walking per unit of body mass and of distance (Cw) was determined before total hip (n = 17) or total knee (n = 16) arthroplasty and ten days, two, six or twelve months after surgery. The ratio of Cw observed on patients (at the self selected speed) relative to that observed in healthy subjects of the same age at the same speed was defined Locomotory Index (Index Locomotorius, IL). So, IL is a quantitative measure of the economy of walking; for both groups of patients IL amounted to about 1.40 before surgery; it increased significantly 10 days after the operation to 1.86 or to 1.58, for hip or knee patients, respectively. Two, six and twelve months after the operation IL had decreased significantly below presurgery values (1.29, 1.30 and 1.30) for knee replacement, whereas it was still larger, albeit not significantly so, after hip replacement (IL = 1.61; 1.56 and 1.50). It was also observed that the self selected speed increased and the traditional clinical scores (Harris for hip and B.O.A. for knee) improved, with decreasing IL. It is suggested that IL is a useful quantitative tool, in addition to the more traditional clinical scores, for evaluating ambulatory disability in patients.

Forming classes by stimulus frequency: behavior and theory.

Visual classification is the way we relate to different images in our environment as if they were the same, while relating differently to other collections of stimuli (e.g., human vs. animal faces). It is still not clear, however, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlying class representation, we studied unsupervised classification of an incoming stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Responses were also faster for more frequent stimuli. Using a minimal, biologically based neural-network model, we demonstrate that a simple, self-organizing representation mechanism based on overlapping tuning curves and slow Hebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior infero-temporal cortex as a possible site of classification.

Spike-driven synaptic plasticity: theory, simulation, VLSI implementation.

We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite (stimulation) interval fluctuate, even at fixed pre- and postsynaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory (Takacs process). The model of the synapse is implemented in aVLSI and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP/LTD probabilities versus rates are robust to fluctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities are in very good agreement with both the simulations and measurements. Moreover, the probabilities are readily manipulable by variations of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity (3-4 Hz) to stimulus-driven rates (50 Hz). Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short (approximately 100 ms). Synaptic transitions are triggered by elevated presynaptic rates: for low presynaptic rates, there are essentially no transitions. The synaptic device can preserve its memory for years in the absence of stimulation. Stochasticity of learning is a result of the variability of interspike intervals; noise is a feature of the distributed dynamics of the network. The fact that the synapse is binary on long timescales solves the stability problem of synaptic efficacies in the absence of stimulation. Yet stochastic learning theory ensures that it does not affect the collective behavior of the network, if the transition probabilities are low and LTP is balanced against LTD.

Collective behavior of networks with linear (VLSI) integrate-and-fire neurons.

We analyze in detail the statistical properties of the spike emission process of a canonical integrate-and-fire neuron, with a linear integrator and a lower bound for the depolarization, as often used in VLSI implementations (Mead, 1989). The spike statistics of such neurons appear to be qualitatively similar to conventional (exponential) integrate-and-fire neurons, which exhibit a wide variety of characteristics observed in cortical recordings. We also show that, contrary to current opinion, the dynamics of a network composed of such neurons has two stable fixed points, even in the purely excitatory network, corresponding to two different states of reverberating activity. The analytical results are compared with numerical simulations and are found to be in good agreement.

Carboxy-substituted cinnamides: a novel series of potent, orally active LTB4 receptor antagonists.

A series of carboxy-substituted cinnamides were investigated as antagonists of the human cell surface leukotriene B4 (LTB4) receptor. Binding was determined through measurement of [3H]LTB4 displacement from human neutrophils. Receptor antagonism was confirmed through a functional assay, which measures inhibition of Ca2+ release in human neutrophils. Potent antagonists were discovered through optimization of a random screening hit, a p-(alpha-methylbenzyloxy)cinnamide, having low-micromolar activity. Substantial improvement of in vitro potency was realized by the attachment of a carboxylic acid moiety to the cinnamide phenyl ring through a flexible tether, leading to identification of compounds with low-nanomolar potency. Modification of the benzyloxy substituent, either through ortho-substitution on the benzyloxy phenyl group or through replacement of the ether oxygen with a methylene or sulfur atom, produced achiral antagonists of equal or greater potency. The most potent compounds in vitro were assayed for oral activity using the arachidonic acid-induced mouse ear edema model of inflammation. Several compounds in this series were found to significantly inhibit edema formation and myeloperoxidase activity in this model up to 17 h after oral administration. Representatives of this series have been shown to be potent and long-acting orally active inhibitors of the LTB4 receptor.

Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.

We study unsupervised Hebbian learning in a recurrent network in which synapses have a finite number of stable states. Stimuli received by the network are drawn at random at each presentation from a set of classes. Each class is defined as a cluster in stimulus space, centred on the class prototype. The presentation protocol is chosen to mimic the protocols of visual memory experiments in which a set of stimuli is presented repeatedly in a random way. The statistics of the input stream may be stationary, or changing. Each stimulus induces, in a stochastic way, transitions between stable synaptic states. Learning dynamics is studied analytically in the slow learning limit, in which a given stimulus has to be presented many times before it is memorized, i.e. before synaptic modifications enable a pattern of activity correlated with the stimulus to become an attractor of the recurrent network. We show that in this limit the synaptic matrix becomes more correlated with the class prototypes than with any of the instances of the class. We also show that the number of classes that can be learned increases sharply when the coding level decreases, and determine the speeds of learning and forgetting of classes in the case of changes in the statistics of the input stream.

Learning attractors in an asynchronous, stochastic electronic neural network.

LANN27 is an electronic device implementing in discrete electronics a fully connected (full feedback) network of 27 neurons and 351 plastic synapses with stochastic Hebbian learning. Both neurons and synapses are dynamic elements, with two time constants--fast for neurons and slow for synapses. Learning, synaptic dynamics, is analogue and is driven in a Hebbian way by neural activities. Long-term memorization takes place on a discrete set of synaptic efficacies and is effected in a stochastic manner. The intense feedback between the nonlinear neural elements, via the learned synaptic structure, creates in an organic way a set of attractors for the collective retrieval dynamics of the neural system, akin to Hebbian learned reverberations. The resulting structure of the attractors is a record of the large-scale statistics in the uncontrolled, incoming flow of stimuli. As the statistics in the stimulus flow changes significantly, the attractors slowly follow it and the network behaves as a palimpsest--old is gradually replaced by new. Moreover, the slow learning creates attractors which render the network a prototype extractor: entire clouds of stimuli, noisy versions of a prototype, used in training, all retrieve the attractor corresponding to the prototype upon retrieval. Here we describe the process of studying the collective dynamics of the network, before, during and following learning, which is rendered complex by the richness of the possible stimulus streams and the large dimensionality of the space of states of the network. We propose sampling techniques and modes of representation for the outcome.

Novel C5a receptor antagonists regulate neutrophil functions in vitro and in vivo.

Novel recombinant human C5a receptor antagonists were discovered through modification of the C terminus of C5a. The C5a1-71T1M,C27S,Q71C monomer, (C5aRAM; CGS 27913), was a pure and potent functional antagonist. The importance of a C-terminal cysteine at position 71 to antagonist properties of C5aRAM was confirmed by studying C5a1-71 derivatives with replacements of Q71, C5a derivatives of various lengths (70-74) with C-terminal cysteines, and C5a derivatives of various lengths (71-74) with Q71C replacements. The majority of C5a1-71Q71 derivatives were agonists (C5a-like) in the human neutrophil C5a-induced intracellular calcium mobilization assay. The C5a1-71Q71C derivative was an antagonist. C5a derivatives of lengths 73 and 74 with C-terminal cysteines were agonists, while lengths 70 to 72 were antagonists. C5a derivatives of lengths 72, 73, and 74 with Q71C replacements were agonists, while, again, C5a1-71Q71C was an antagonist. C5aRAM and its adducts, including its dimer, C5aRAD (CGS 32359), were pure antagonists. Additionally, CSaRAM and CSaRAD inhibited binding of 125I-labeled recombinant human C5a to neutrophil membranes (Ki = 79 and 2 pM, respectively), C5a-stimulated neutrophil intracellular calcium mobilization (8 and 13 nM), CD11b integrin up-regulation (10 and 1 nM), superoxide generation (182 and 282 nM), lysozyme release (1 and 2 microM), and chemotaxis (11 and 7 microM). In vivo, intradermal injection of C5aRAM inhibited C5a-induced dermal edema in rabbits. Furthermore, a 5-mg/kg i.v. bolus of C5aRAD significantly inhibited C5a-induced neutropenia in micropigs when challenged with C5a 30 min after C5aRAD administration. C5aRAM and C5aRAD are novel, potent C5a receptor antagonists devoid of agonist or proinflammatory activity with demonstrated efficacy in vitro and in vivo.