Eosinophilia and symptomatic pulmonary amyloidosis.

The case of a man with progressive breathlessness and pulmonary infiltration caused by AL amyloidosis associated with multiple myeloma is presented. There was a marked peripheral eosinophilia, which has not previously been described with amyloidosis.

Adult onset Still's disease and collapsing glomerulopathy: successful treatment with intravenous immunoglobulins and mycophenolate mofetil.

In this Grand Round we present a 32-yr-old African man who became severely ill after a 5-month history of weight loss, pyrexia, arthralgia, sweats and rash. He went on to develop pericarditis, pericardial effusion with tamponade, hepatomegaly with abnormal liver function tests, lymphadenopathy, massive proteinuria and required ventilatory, circulatory and renal support. The differential diagnosis was adult onset Still's disease, systemic lupus erythematosus (SLE), infection and lymphoma. Primary infection and lymphoma were excluded and he was treated, with dramatic success, with intravenous immunoglobulins (i.v.IG). Subsequent renal biopsy excluded SLE but confirmed collapsing glomerulopathy. The proteinuria improved dramatically following treatment with mycophenolate mofetil. We discuss some of the difficult diagnostic and management issues raised by this patient and the different uses and mechanisms of action of i.v.IG.

Probabilistic computation by neuromine networks.

In this paper, we address the question, can biologically feasible neural nets compute more than can be computed by deterministic polynomial time algorithms? Since we want to maintain a claim of plausibility and reasonableness we restrict ourselves to algorithmically easy to construct nets and we rule out infinite precision in parameters and in any analog parts of the computation. Our approach is to consider the recent advances in randomized algorithms and see if such randomized computations can be described by neural nets. We start with a pair of neurons and show that by connecting them with reciprocal inhibition and some tonic input, then the steady-state will be one neuron ON and one neuron OFF, but which neuron will be ON and which neuron will be OFF will be chosen at random (perhaps, it would be better to say that microscopic noise in the analog computation will be turned into a megascale random bit). We then show that we can build a small network that uses this random bit process to generate repeatedly random bits. This random bit generator can then be connected with a neural net representing the deterministic part of randomized algorithm. We, therefore, demonstrate that these neural nets can carry out probabilistic computation and thus be less limited than classical neural nets.

A ternary logic model for recurrent neuromime networks with delay.

In contrast to popular recurrent artificial neural network (RANN) models, biological neural networks have unsymmetric structures and incorporate significant delays as a result of axonal propagation. Consequently, biologically inspired neural network models are more accurately described by nonlinear differential-delay equations rather than nonlinear ordinary differential equations (ODEs), and the standard techniques for studying the dynamics of RANNs are wholly inadequate for these models. This paper develops a ternary-logic based method for analyzing these networks. Key to the technique is the realization that a nonzero delay produces a bounded stability region. This result significantly simplifies the construction of sufficient conditions for characterizing the network equilibria. If the network gain is large enough, each equilibrium can be classified as either asymptotically stable or unstable. To illustrate the analysis technique, the swim central pattern generator (CPG) of the sea slug Tritonia diomedea is examined. For wide range of reasonable parameter values, the ternary analysis shows that none of the network equilibria are stable, and thus the network must oscillate. The results show that complex synaptic dynamics are not necessary for pattern generation.