A single-dose comparison of the acute effects between the new somatostatin analog SOM230 and octreotide in acromegalic patients

Treatment with the somatostatin receptor (sst) subtype 2 predominant analogs octreotide and lanreotide induces clinical and biochemical cure in approximately 65% of acromegalic patients. GH-secreting pituitary adenomas, which are not controlled, also express sst(5). We compared the acute effects of octreotide and SOM230, a new somatostatin analog with high affinity for sst(1,2,3,5) on hormone release in acromegalic patients. In a single-dose, proof-of-concept study, 100 microg octreotide and 100 and 250 microg SOM230 were given s.c. to 12 patients with active acromegaly. Doses of 100 and 250 microg SOM230 dose-dependently suppressed GH levels from 2-8 h after administration (-38 +/- 7.7 vs. -61 +/- 6.7%, respectively; P < 0.01). A comparable suppression of GH levels by octreotide and 250 microg SOM230 was observed in eight patients (-65 +/- 7 vs. -72 +/- 7%, respectively). In three patients, the acute GH-lowering effect of 250 microg SOM230 was significantly superior to that of octreotide (-70 +/- 2 vs. -17 +/- 15%, respectively; P < 0.01). In one patient, the GH-lowering effect of octreotide was better than that of SOM230. Tolerability for SOM230 was good. Glucose levels were initially slightly elevated after octreotide and SOM230, compared with control day, whereas insulin levels were only significantly suppressed by octreotide. We conclude that SOM230 is an effective GH-lowering drug in acromegalic patients with the potential to increase the number of patients controlled during long-term medical treatment

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Mathematical Model of Plasmid-Mediated Resistance to Ceftiofur in Commensal Enteric Escherichia coli of Cattle

Antimicrobial use in food animals may contribute to antimicrobial resistance in bacteria of animals and humans. Commensal bacteria of animal intestine may serve as a reservoir of resistance-genes. To understand the dynamics of plasmid-mediated resistance to cephalosporin ceftiofur in enteric commensals of cattle, we developed a deterministic mathematical model of the dynamics of ceftiofur-sensitive and resistant commensal enteric Escherichia coli (E. coli) in the absence of and during parenteral therapy with ceftiofur. The most common treatment scenarios including those using a sustained-release drug formulation were simulated; the model outputs were in agreement with the available experimental data. The model indicated that a low but stable fraction of resistant enteric E. coli could persist in the absence of immediate ceftiofur pressure, being sustained by horizontal and vertical transfers of plasmids carrying resistance-genes, and ingestion of resistant E. coli. During parenteral therapy with ceftiofur, resistant enteric E. coli expanded in absolute number and relative frequency. This expansion was most influenced by parameters of antimicrobial action of ceftiofur against E. coli. After treatment (>5 weeks from start of therapy) the fraction of ceftiofur-resistant cells among enteric E. coli, similar to that in the absence of treatment, was most influenced by the parameters of ecology of enteric E. coli, such as the frequency of transfer of plasmids carrying resistance-genes, the rate of replacement of enteric E. coli by ingested E. coli, and the frequency of ceftiofur resistance in the latter

Dogs Leaving the ICU Carry a Very Large Multi-Drug Resistant Enterococcal Population with Capacity for Biofilm Formation and Horizontal Gene Transfer

The enterococcal community from feces of seven dogs treated with antibiotics for 2–9 days in the veterinary intensive care unit (ICU) was characterized. Both, culture-based approach and culture-independent 16S rDNA amplicon 454 pyrosequencing, revealed an abnormally large enterococcal community: 1.4±0.8×108 CFU gram−1 of feces and 48.9±11.5% of the total 16,228 sequences, respectively. The diversity of the overall microbial community was very low which likely reflects a high selective antibiotic pressure. The enterococcal diversity based on 210 isolates was also low as represented by Enterococcus faecium (54.6%) and Enterococcus faecalis (45.4%). E. faecium was frequently resistant to enrofloxacin (97.3%), ampicillin (96.5%), tetracycline (84.1%), doxycycline (60.2%), erythromycin (53.1%), gentamicin (48.7%), streptomycin (42.5%), and nitrofurantoin (26.5%). In E. faecalis, resistance was common to tetracycline (59.6%), erythromycin (56.4%), doxycycline (53.2%), and enrofloxacin (31.9%). No resistance was detected to vancomycin, tigecycline, linezolid, and quinupristin/dalfopristin in either species. Many isolates carried virulence traits including gelatinase, aggregation substance, cytolysin, and enterococcal surface protein. All E. faecalis strains were biofilm formers in vitro and this phenotype correlated with the presence of gelE and/or esp. In vitro intra-species conjugation assays demonstrated that E. faecium were capable of transferring tetracycline, doxycycline, streptomycin, gentamicin, and erythromycin resistance traits to human clinical strains. Multi-locus variable number tandem repeat analysis (MLVA) and pulsed-field gel electrophoresis (PFGE) of E. faecium strains showed very low genotypic diversity. Interestingly, three E. faecium clones were shared among four dogs suggesting their nosocomial origin. Furthermore, multi-locus sequence typing (MLST) of nine representative MLVA types revealed that six sequence types (STs) originating from five dogs were identical or closely related to STs of human clinical isolates and isolates from hospital outbreaks. It is recommended to restrict close physical contact between pets released from the ICU and their owners to avoid potential health risks

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Comparison of PCR-based DNA fingerprinting techniques for the identification of Listeria species and their use for atypical Listeria isolates

Four PCR-based DNA fingerprinting techniques were compared for their ability to identify at the species level a heterogeneous collection of isolates belonging to the six valid Listeria species. 16S rDNA-RFLP analysis identified all species and 16S rDNA-SSCP analysis identified almost all species. Also, isolates with unusual biochemical characteristics and/or unusual antigenic composition could be identified correctly. rRNA-intracistronic length polymorphism analysis suffered from high intraspecific variability, a limited number of fragments per profile, and small length differences between the spacers of different species. tRNA-intergenic length polymorphism analysis resulted in identification of all isolates but one, when fluorescent DNA capillary electrophoresis was used such that fragment length differences of 1 bp could be resolved. The four techniques yielded comparable results relevant to the taxonomy of Listeria. They all indicate a high degree of genetic relatedness between L. innocua and L. welshimeri, homogeneity of L. grayi, distinct but clear relatedness of L. grayi to the other Listeria species, a clear distinction between the two subspecies of L. ivanovii, and a clear distinction between Listeria isolates and isolates from closely related taxa or from species which are phenotypically difficult to distinguish from Listeria. New sequence determination of the 16S rRNA gene was necessary to obtain sequences in accordance with the findings of 16S rDNA-RFLP analysis