Phthalimido-thiazole as privileged scaffold: activity against immature and adult worms of Schistosoma mansoni

Parasitology Research - Phthalimide, 1,3-thiazole, and thiazolidinone cores are considered privileged scaffolds and represent an attractive starting point to design new bioactive compounds for...     

Oxidative Stress and Respiratory System: Pharmacological and Clinical Reappraisal of N-Acetylcysteine

The large surface area for gas exchange makes the respiratory system particularly susceptible to oxidative stress-mediated injury. Both endogenous and exogenous pro-oxidants (e.g. cigarette smoke) trigger activation of leukocytes and host defenses. These mechanisms interact in a “multilevel cycle” responsible for the control of the oxidant/antioxidant homeostasis. Several studies have demonstrated the presence of increased oxidative stress and decreased antioxidants (e.g. reduced glutathione [GSH]) in subjects with chronic obstructive pulmonary disease (COPD), but the contribution of oxidative stress to the pathophysiology of COPD is generally only minimally discussed. The aim of this review was to provide a comprehensive overview of the role of oxidative stress in the pathogenesis of respiratory diseases, particularly COPD, and to examine the available clinical and experimental evidence on the use of the antioxidant N-acetylcysteine (NAC), a precursor of GSH, as an adjunct to standard therapy for the treatment of COPD. The proposed concept of “multilevel cycle” helps understand the relationship between respiratory diseases and oxidative stress, thus clarifying the rationale for using NAC in COPD. Until recently, antioxidant drugs such as NAC have been regarded only as mucolytic agents. Nevertheless, several clinical trials indicate that NAC may reduce the rate of COPD exacerbations and improve small airways function. The most plausible explanation for the beneficial effects observed in patients with COPD treated with NAC lies in the mucolytic and antioxidant effects of this drug. Modulation of bronchial inflammation by NAC may further account for these favorable clinical results.     

Is fluorine-18-fluorodeoxyglucose positron emission tomography useful in monitoring the response to treatment in patients with multiple myeloma?

Fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) is a useful diagnostic tool for the staging of patients with multiple myeloma (MM). The aim of this study is to perform a systematic review of the usefulness of FDG-PET or PET/CT in monitoring response to treatment in patients with MM. A comprehensive computer literature search of the PubMed/MEDLINE, Scopus and Embase databases was carried out to identify relevant peer-reviewed articles on the use of FDG-PET or PET/CT in monitoring the response to treatment in patients with MM. Ten studies described investigations of the role of FDG-PET or PET/CT in monitoring the response to treatment in 690 patients with MM or solitary plasmacytoma: six of these were conducted prospectively, while four studies were retrospective. These articles were retrieved in full-text version and analyzed. Based on these findings from the literature, FDG-PET or PET/CT appear to be useful in the assessment of treatment response in patients with MM.     

The clinical value of laryngeal electromyography in laryngeal immobility

Although laryngeal electromyography (LEMG) is commonly performed, there are no data confirming its efficacy. We evaluated 40 patients with a laryngoscopic diagnosis of unilateral vocal-fold immobility who underwent LEMG of the thyroarytenoid (TA) and cricothyroid (CT) muscle, with the immobile side of each muscle being compared to the normal side. The immobile side compared to the normal side showed more fibrillation potentials and positive sharp waves for the TA (p = 0.04), longer MUAP duration for the TA (p = 0.04) and CT (p = 0.01), more polyphasic potentials for the TA (p = 0.002), and more frequent decreased recruitment for the TA (p < 0.01) and CT (p = 0.008). Specificity and positive predictive value were around 90%. Sensitivity, negative predictive value and accuracy were around 50%. These results suggest that altered LEMG findings are reliable and they can be used to determine the innervation status of an immobile muscle. Conversely, when the LEMG is normal, the results should be reviewed.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Salmeterol/fluticasone propionate in a Single Inhaler Device versus theophylline+fluticasone propionate in patients with COPD

STUDY OBJECTIVES: The aim of this study was to compare the relative efficacy in terms of improvement in symptoms and lung function of salmeterol/fluticasone propionate (SLM/FP) combination administered through the Diskus inhaler versus theophylline (THEO) added to FP Diskus in patients with stable chronic obstructive pulmonary disease (COPD). METHODS AND MEASUREMENTS: Eighty patients were randomized to receive 4 months of treatment in one of two treatment groups: (1) fixed combination of SLM 50 microg and FP 500 microg Diskus, 1 inhalation twice daily; or (2) FP Diskus 500 microg, 1 inhalation twice daily, plus oral titrated THEO twice daily. Patients attended the clinic before and after 4, 8, 12 and 16 weeks of treatment for evaluations of pulmonary function, and dyspnea, which was assessed using an analogic visual scale. Also the supplemental salbutamol use was measured. RESULTS:. Sixty-six patients completed the 4-month treatment period: 37 patients receiving SLM/FP and 29 patients receiving THEO+FP. Patients were withdrawn for various reasons, the most common of which were poor compliance with the protocol, exacerbation and GI events. A gradual increase in FEV(1) was observed with each treatment. Maximum significant increases in FEV(1) over baseline values that were observed after 4 months of treatment were as follows: SLM/FP 0.172 l (95% CI: 0.084-0.260) and THEO+FP 0.155 l (95% CI: 0.054-0.256). SLM/FP experienced significantly (p<0.05) greater improvements in dyspnea, and required significantly fewer supplemental salbutamol treatments than the THEO+FP group. CONCLUSIONS: Our results suggest that SLM/FP combination may provide substantial benefits in both physiologic and clinical outcomes in symptomatic patients with COPD. It also causes a more effective control than THEO+FP.