ASSESSMENT OF EDUCATIONAL INTERVENTION ON KNOWLEDGE, ATTITUDE, AND PRACTICES OF RURAL COMMUNITY PHARMACISTS OF MYSURU DISTRICT TOWARD ADVERSE DRUG REACTION REPORTING

Objective: A prospective interventional study was conducted to evaluate the impact of educational intervention on knowledge, attitude, and practices (KAP)(of rural community pharmacists toward adverse drug reaction (ADR) reporting.Methods: A validated KAP questionnaire was administered on the enrolled community pharmacists in the study. SPSS software package version-19 was used to calculate the influence of educational intervention on KAP scores of the participants. Pre-training KAP scores were compared with the post-training KAP scores.Results: About 49 community pharmacists have participated in the study, 95.91% (n=47) were males, and 4.08% (n=2) were females. The mean±SD age of the participants was 40.93±7.84 years. The mean ± SD score in the knowledge component was significantly increased from 4.87±2.015 to 7.09 ± 0.68 (n=49, p<0.05). After the educational intervention, 77.55% (n=38) of participants could correctly define the ADRs, and 73.46% (n=36) of participants were aware of the consequence of ADRs. About 57.34% of participants disagree with the statement reporting of ADRs incurs the addtional workload with post education intervention. At the end of the study, the participants' knowledge was significantly increased and participant pharmacists felt responsible toward ADR reporting.Conclusion: Educational interventional program have shown a tremendous change in knowledge and awareness of the respondents towards adverse drug reaction monitoring and reporting. It is well understood that there is a need for promoting the pharmacovigilance activities among community pharmacists

Are Saddles Good Enough for Deep Learning?

Recent years have seen a growing interest in understanding deep neural networks from an optimization perspective. It is understood now that converging to low-cost local minima is sufficient for such models to become effective in practice. However, in this work, we propose a new hypothesis based on recent theoretical findings and empirical studies that deep neural network models actually converge to saddle points with high degeneracy. Our findings from this work are new, and can have a significant impact on the development of gradient descent based methods for training deep networks. We validated our hypotheses using an extensive experimental evaluation on standard datasets such as MNIST and CIFAR-10, and also showed that recent efforts that attempt to escape saddles finally converge to saddles with high degeneracy, which we define as `good saddles'. We also verified the famous Wigner's Semicircle Law in our experimental results

PHARMACOEPIDEMIOLOGICAL EVALUATION OF HIV PHARMACOTHERAPY AT DISTRICT ART CENTER IN SOUTH TELANGANA

Objective: Objective of the study was to assess the drug utilization pattern of antiretroviral drugs, and medication adherence behavior among human immunodeficiency virus (HIV) patients attending a local ART center, Suryapet, South Telangana. Methods: This was a prospective observational study approved by institutional ethics committee. Demographic, clinical, laboratory, and the treatment details were collected on daily basis for new cases and the data add on was collected for old cases. Medication adherence behavior was assessed through Morisky Medication Adherence Scale-8. Results: During the study period, a total of 505 HIV patients were enrolled. Among them, majority patients were women (61%), in the age group of 31–45 (49.7%). Illiterates (52.6%). Major mode of transmission identified was intimate contact (74%), and majority patients were in Stage I (49%). TLE regimen was prescribed in 69.9% patients and for children the prescribed regimen was ABC, 3TC, EFV (5.1%). About 43% patients were found with medium adherence. Conclusion: This study concludes that the most prescribed regimens were combination of TLE, and majority of the patients were found with medium adherence

Temporal Coherence in Energy-based Deep Learning Machines for Action Recognition

Deep Learning, a sub-area of machine learning, has become a buzz word in recent days due to its great successes in many applications of machine learning, including speech processing, computer vision and natural language processing. Deep learning became famous in the initial days through the successful application of Convolutional Neural Networks as well as Energy-based Models -or Restricted Boltzmann Machines (RBMs) - on handwritten digit recognition. While the last decade has seen the growing use of convolution-based deep learning methods for image analysis, limited work has been done in adapting deep learning to video analysus. Existing methods have largely extended the ideas based on convolution applied to images into the video analysis setting. The primary deep learning approaches that have been proposed so far explicitly for video sequences are the 3D Convolutional Neural Networks and the Convolutional Gated RBM

Dissimilarity Based Contrastive Divergence for Anomaly Detection

This paper describes training of a Re- stricted Boltzmann Machine(RBM) using dissimilarity-based contrastive divergence to obtain an anomaly detector. We go over the merits of the method over other approaches and describe the method's usefulness to ob- tain a generative model

FINITE ELEMENT ANALYSIS OF FRONT UNDER-RUN PROTECTION DEVICE (FUPD) FOR IMPACT LOADING

Under-running of passenger automobiles is among the important parameters that need considering during development and design of truck chassis. Front Under-run Protection Device (FUPD) plays a huge role in staying away from under-running of automobiles from top of the truck. In India, the legal needs of the FUPD are fixed in regulation IS 14812-2005. To lessen quantity of iterations throughout the development process, the computational simulation technique is utilized in FUPD analysis for impact loading. An explicit finite element code like Altair Radios can be used for that simulation. The deformation of FUPD bar and plastic strains in FUPD components can be established prior to the physical test for predicting failure from the system to satisfy the compliance needs according to IS 14812-2005. Furthermore, failure from the FUPD attachment points with chassis can be established. Physical testing could be reduced considerably with this particular approach which ultimately cuts down on the total cycle time along with the cost involved with product. This paper describes the FE analysis of FUPD for impact loading. All of the results acquired in the CAE analysis are evaluated from the needs of IS 14812-2005 that could lessen the process development cost and time active in the same

BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification

Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and thus reduce human annotation effort in inducing a classification model. More recently, Batch Mode Active Learning (BMAL) techniques have been proposed, where a batch of data samples is selected simultaneously from an un- labeled set. Most active learning algorithms assume a at label space, that is, they consider the class labels to be in- dependent. However, in many applications, the set of class labels are organized in a hierarchical tree structure, with the leaf nodes as outputs and the internal nodes as clusters of outputs at multiple levels of granularity. In this paper, we propose a novel BMAL algorithm (BatchRank) for hi- erarchical classification. The sample selection is posed as an NP-hard integer quadratic programming problem and a convex relaxation (based on linear programming) is derived, whose solution is further improved by an iterative truncated power method. Finally, a deterministic bound is established on the quality of the solution. Our empirical results on sev- eral challenging, real-world datasets from multiple domains, corroborate the potential of the proposed framework for real- world hierarchical classification applications

Systematic Review of Medicine-Related Problems in Adult Patients with Atrial Fibrillation on Direct Oral Anticoagulants

New oral anticoagulant agents continue to emerge on the market and their safety requires assessment to provide evidence of their suitability for clinical use. There-fore, we searched standard databases to summarize the English language literature on medicine-related problems (MRPs) of direct oral anticoagulants DOACs (dabigtran, rivaroxban, apixban, and edoxban) in the treatment of adults with atri-al fibrillation. Electronic databases including Medline, Embase, International Pharmaceutical Abstract (IPA), Scopus, CINAHL, the Web of Science and Cochrane were searched from 2008 through 2016 for original articles. Studies pub-lished in English reporting MRPs of DOACs in adult patients with AF were in-cluded. Seventeen studies were identified using standardized protocols, and two reviewers serially abstracted data from each article. Most articles were inconclusive on major safety end points including major bleeding. Data on major safety end points were combined with efficacy. Most studies inconsistently reported adverse drug reactions and not adverse events or medication error, and no definitions were consistent across studies. Some harmful drug effects were not assessed in studies and may have been overlooked. Little evidence is provided on MRPs of DOACs in patients with AF and, therefore, further studies are needed to establish the safety of DOACs in real-life clinical practice