Experience with direct acting anti-viral agents for treating hepatitis C virus infection in renal transplant recipients

In recent past, direct-acting anti-viral drugs (DAAs) have become the standard of care for the treatment of hepatitis C virus (HCV) infection. However, the experience with the use of these drugs in Indian renal transplant recipients is limited. We retrospectively reviewed our experience with DAA-based treatment for HCV infection in such patients. Between April 2015 and December 2016, six adults (median age 41 [range 34–52] years, male 5; GT1 2, GT3 3, and GT4 1; including three with prior failed interferon-based treatment) had received genotype-guided, DAA-based anti-HCV treatment 1 to 158 (median 15) months after renal transplantation. Of them, four completed the planned 24-week treatment without any significant adverse event. One of them had increase in serum creatinine after 16 weeks of treatment with sofosbuvir and daclatasvir, with acute interstitial nephritis on kidney biopsy; his renal function improved on stopping the drugs. The other patient had preexisting mild renal dysfunction, which worsened after 8 weeks of sofosbuvir-ledipasvir treatment; this did not reverse on stopping treatment. All the six patients achieved undetectable HCV RNA after 4 weeks of treatment and also achieved sustained virologic response, i.e. lack of detectable HCV RNA in serum 12 weeks after stopping treatment. Overall, DAA-based treatment was effective in treating HCV infection in our renal transplant recipients; however, caution and monitoring of renal function during such treatment is advisable in patients who have additional factors that predispose to renal injury.     

The Impact of Hot Melt Extrusion and Spray Drying on Mechanical Properties and Tableting Indices of Materials Used in Pharmaceutical Development

The impact of melt extrusion (HME) and spray drying (SD) on mechanical properties of hypromellose acetate succinate (HPMCAS), copovidone, and their formulated blends was studied and compared with that of reference excipients. Tensile strength (TS), compression pressure (CP), elastic modulus (E), and dynamic hardness (H_d) were determined along with Hiestand indices using compacts prepared at a solid fraction of ～0.85. HPMCAS and copovidone exhibited lower H_d, lower CP, and lower E than the reference excipients and moderate TS. HPMCAS was found to be highly brittle based on brittle fracture index values. The CP was 24% and 61% higher for HPMCAS after SD and HME, respectively, than for unprocessed material along with a higher H_d. Furthermore, the TS of HPMCAS and copovidone decreased upon HME. Upon blending melt-extruded HPMCAS with plastic materials such as microcrystalline cellulose, the TS increased. These results suggest that SD and HME could impact reworkability by reducing deformation of materials and in case of HME, likely by increasing density due to heating and shear stress in a screw extruder. A somewhat similar effect was observed for the dynamic binding index (BI_d) of the excipients and formulated blends. Such data can be used to quantitate the impact of processing on mechanical properties of materials during tablet formulation development.     

Introduction of new vaccines in state immunization schedule — Delhi’s experience

Immunization is an established, cost-effective, preventive intervention to improve child survival. To provide protection against vaccine preventable diseases, all countries in the world have an immunization program that offers selected vaccines to the eligible beneficiaries. In India, Expanded Program of Immunization was started in 1978, and then Universal Immunization Program was launched in 1985 with six antigens. This article describes the experience with institutionalization of four state-specific vaccines by Delhi in its immunization schedule to enlarge the ambit of immunization services. It attempts to highlight the state’s perspective in terms of the implementation policy, operational strategy adopted and evolution of immunization program in the state over 16 years.     

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.     

The plaque lipid lysophosphatidic acid stimulates platelet activation and platelet-monocyte aggregate formation in whole blood: involvement of P2Y1 and P2Y12 receptors

Despite the fact that lysophosphatidic acid (LPA) has been identified as a main platelet-activating lipid of mildly oxidized low-density lipoprotein (LDL) and human atherosclerotic lesions, it remains unknown whether it is capable of activating platelets in blood. We found that LPA at concentrations slightly above plasma levels induces platelet shape change, aggregation, and platelet-monocyte aggregate formation in blood. 1-alkyl-LPA (16:0 fatty acid) was almost 20-fold more potent than 1-acyl-LPA (16:0). LPA directly induced platelet shape change in blood and platelet-rich plasma obtained from all blood donors. However, LPA-stimulated platelet aggregation in blood was donor dependent. It could be completely blocked by apyrase and antagonists of the platelet adenosine diphosphate (ADP) receptors P2Y1 and P2Y12. These substances also inhibited LPA-induced aggregation of platelet-rich plasma and aggregation and serotonin secretion of washed platelets. These results indicate a central role for ADP-mediated P2Y1 and P2Y12 receptor activation in supporting LPA-induced platelet aggregation. Platelet aggregation and platelet-monocyte aggregate formation stimulated by LPA was insensitive to inhibition by aspirin. We conclude that LPA at concentrations approaching those found in vivo can induce platelet shape change, aggregation, and platelet-monocyte aggregate formation in whole blood and suggest that antagonists of platelet P2Y1 and P2Y12 receptors might be useful preventing LPA-elicited thrombus formation in patients with cardiovascular diseases.     

Comparative Evaluation of PCR and Imprint Smear Microscopy Analyses of Skin Biopsy Specimens in Diagnosis of Macular,Papular, and Mixed Papulo-Nodular Lesions of Post-Kala-Azar Dermal Leishmaniasis

Diagnosis of post-kala-azar dermal leishmaniasis (PKDL), particularly the macular form, is difficult when based on microscopy. This study compared the results of nested PCR (91.9% positive samples) with imprint smear microscopy (70.9% positive samples) for 62 PKDL samples. We found that nested PCR, which indicated 87.5% positivity for the macular lesions, compared to 41.6% positivity by imprint smear microscopy, is an efficient method for early diagnosis of PKDL.