A retrospective study of adverse drug reactions in a tertiary care center

Background: Adverse drug reactions (ADRs) are a major concern in clinical practice. Reporting of ADRs either through health care professionals or the patients themselves is of utmost importance to give an accurate estimate of the prevalence, severity and preventability of ADRs. Present study was conducted to evaluate the prevalence of adverse drug reactions in a tertiary care hospital in Hubballi, Karnataka, India.Methods: This was a retrospective observational study, extending over 6 months (May 2019 to October 2019). A total of 124 cases comprising patients of either sex and age group ranging from 1month to 72 years were studied. The data was collected using CDSCO ADR reporting form. “Naranjo’s Assessment Scale” was used for causality assessment and severity assessment was done in accordance with “Hartwig and Siegel scale”.Results: The study showed majority of ADRs were from General Medicine department and affected skin and appendages (59%). Skin rashes 44 (31.7%) were found to be the most commonly reported ADRs most of them were with antimicrobials 67 (54%).  After causality assessment 83 (66.9%) of the cases were classified as probable and 41 (33.1%) were classified as possible. Majority of serious ADRs were not preventable in our study.Conclusions: ADRs are a major cause of morbidity worldwide. Frequency of ADRs can be reduced by careful follow up and a robust hospital-based pharmacovigilance setup. Measures to improve detection and reporting of adverse drug reactions by all health care professionals is recommended

Spatiotemporal Cluster Analysis of Gridded Temperature Data -- A Comparison Between K-means and MiSTIC

The Earth is a system of numerous interconnected spheres, such as the climate. Climate's global and regional influence requires understanding its evolution in space and time to improve knowledge and forecasts. Analyzing and studying decades of climate data is a data mining challenge. Cluster analysis minimizes data volumes and analyzes behavior by cluster. Understanding invariant behavior is as crucial as understanding variable behavior. Gridded data from two sources: Grided IMD data and CMIP5 HadCM3 decadal experiments, are studied using K-Means and MiSTIC clustering techniques to explore spatiotemporal clustering of maximum and minimum temperatures. The boundaries of k-means clustering correspond with topography. The Indian subcontinent's physiographic, climatic, and topographical characteristics affect MiSTIC's core areas. Both techniques yield overlapping clusters. The datasets' MiSTIC cluster counts varied significantly. The impact of data on this technique is shown in how the datasets group the Himalayas.Comment: 6 pages, 7 figures, Published with International Journal of Scientific Research and Engineering Development - Volume 6 Issue

ANTI-INFLAMMATORY ACTIVITY OF ROOT AND FRUIT OF GOKSHURA (TRIBULUS TERRESTRIS LINN.) IN ALBINO RATS

Gokshura Moola (root of Tribulus terrestris Linn.) is one of the ingredients of the group of ten medicinal plant roots called Dasamoola. It is a major ingredient of Ayurvedic formulations so that the Ayurvedic manufacturing industry is consuming them in abundantly. Instead of roots, the fruits of Tribulus terrestris is roughly using in all the preparations of Dasamoola. In Ayurvedic classics Gokshura is said to be useful in the treatment of dysurea (Mutrakrichra), inflammation (sopham), renal calculi (Asmari), cardiac diseases (Hridroga), rheumatoid arthritis (Amavata), rejuvenation (Rasayana), aphrodisiac (Vajeekarana), etc. Ancient Ayurvedic literature opines that the properties and actions attributed to one part of the plant will be the same for the other parts too. If the fruit of the plant is equally effective as the root then the destruction of the whole plant can be prevented. Hence, the present study is carried to evaluate and compare the anti-inflammatory activity of both root and fruit Kashaya (decoction) experimentally by- Carrageenan induced rat paw oedema method with Diclofenac sodium (20 mg/kg) as standard. The results were analysed statistically by ANOVA and LSD post hoc pair wise comparison test. Both root and fruit of Gokshura, showed significant anti inflammatory activity in albino rats. But the root of Gokshura (Tribulus terrestris Linn) showed a greater anti inflammatory action in comparison to its fruit. Thus the current substitution of Gokshura roots with fruits can be substantiated by this study but effect may be less compared to root part

The study of serum calcium and serum magnesium in pregnancy induced hypertension and normal pregnancy

Background: Preeclampsia along with its complications is one of the major causes of maternal and fetal mortality and morbidity. Association of calcium and magnesium with pregnancy induced hypertension is known since decades. Evidence of decreased serum calcium and decreased serum magnesium has been observed in patients with pregnancy induced hypertension and has been implicated in the etiopathogenesis of preeclampsia.Methods: The present study was undertaken in 100 pregnant women. Data for the study was collected from 50 normotensive pregnant women with more than 20 weeks of gestational age (control group) and 50 pregnancy induced hypertension patients (study group) attending for the antenatal care in department of obstetrics and gynaecology in Vanivilas hospital, Bowring and Lady Curzon hospital attached to Bangalore medical college and research institute. Cases and controls were matched. Serum calcium and serum magnesium levels were estimated by spectrophotometry method.Results: The mean serum calcium is significantly lower in pregnancy induced hypertension group (8.15 ± 0.37 mg/dl) compared to normal pregnancy (9.16 ± 0.82 mg/dl). The mean serum magnesium is lower in pregnancy induced hypertension group (1.78 ± 0.70 mEq/L) than normal pregnancy (2.08 ± 0.46 mEq/L) which is moderately significant.  Conclusions: The serum calcium and serum magnesium levels are decreased in pregnancy induced hypertension patients compared to normotensive normal pregnant women, suggesting the possible role of calcium and magnesium in etiopathophysiology of pregnancy induced hypertension

Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring

Automatic Speech Recognition (ASR) has witnessed a profound research interest. Recent breakthroughs have given ASR systems different prospects such as faithfully transcribing spoken language, which is a pivotal advancement in building conversational agents. However, there is still an imminent challenge of accurately discerning context-dependent words and phrases. In this work, we propose a novel approach for enhancing contextual recognition within ASR systems via semantic lattice processing leveraging the power of deep learning models in accurately delivering spot-on transcriptions across a wide variety of vocabularies and speaking styles. Our solution consists of using Hidden Markov Models and Gaussian Mixture Models (HMM-GMM) along with Deep Neural Networks (DNN) models integrating both language and acoustic modeling for better accuracy. We infused our network with the use of a transformer-based model to properly rescore the word lattice achieving remarkable capabilities with a palpable reduction in Word Error Rate (WER). We demonstrate the effectiveness of our proposed framework on the LibriSpeech dataset with empirical analyses

Parachordoma: a rare recurring case at a rare site

Parachordoma is an uncommon tumor of soft tissue and the orign is not clear. This soft tissue tumor resembles chordomas as well as extraskeletal myxoid chondrosarcomas and has only recently been fully characterized. Although it is considered a benign lesion, its behavior tends to be locally aggressive, with reports of a recurrence rate of up to 20% and of several cases of metastasis. In this article, we report a case of parachordoma in the neck with recurrence that we met in clinical works

Microfluidics and Neural Interfaces Development for the Safe Direct Current Stimulator

Safety of commercial neural implants fundamentally limits its working to the use of charge-balanced, biphasic pulses to interact with target neurons using metal electrodes. Short biphasic pulses are used to avoid toxic electrochemical reactions at the electrode-tissue interfaces. Biphasic pulses are effective at exciting neurons, but quite limited in inhibiting their activity. In contrast, direct current can both excite and inhibit neurons, however it leads to the formation of harmful, Faradaic reactions at the metal electrode/tissue interface. To address this challenge of safety over chronic use, we are developing the Safe Direct Current Stimulator (SDCS) technology, that generates an ionic direct current (iDC) from a biphasic input signal using a network of microfluidic channels and mechanical valves. This rectified iDC is applied to the target neural tissue through an ionically conductive neural interface. A key enabler towards transforming the SDCS concept from a benchtop design to an implantable neural prosthesis is the design of a miniature valve. Several valve architectures and actuation mechanism were studied for the development of the microfluidics in SDCS technology, before settling on the plunger-membrane microvalve design. This thesis characterizes a miniature polydimethylsiloxane (PDMS) based elastomeric normally closed (NC) mechanical valve actuated using a shape-memory alloy (SMA) wire through distinct tests and examines its current capability for iDC delivery. The analysis of the test outputs confirmed the feasibility of using this design for rectifying the charge-balanced alternating current (AC) into iDC. As metal electrodes are unsuitable for delivering iDC to the neural tissue safely, an ionic conductive neural lead is built. These gel-based, PDMS electrodes should be designed within the acceptable pressure limits that a nerve can handle safely. Preliminary experiments were conducted to verify the design and conductivity of the lead. While the results suggest that the lead design maintains the pressure below the maximum limit, its high impedance raises concerns. Although this thesis forms a basis for development of the SDCS device, further experimentation and progress is required for a reliable, safe, chronic, and fully functional device

The Influence of Cause-Related Marketing on Millennials’ Purchase Intentions: Evidence of CSR from an Emerging Economy

Corporate social responsibility (CSR) has been implemented through sponsorships, philanthropy, and cause-related marketing (CRM), amongst which CRM has aroused the interest of many academicians and stakeholders. The study aims to examine the antecedents of cause-related marketing while considering attitude as a mediator to test its relationship with the purchase intention. The snowball sampling technique for data collection was administered to Indian millennial consumers from the regions of Karnataka and Kerala. A total of 313 valid cases were selected for the analysis, which employed partial least squares (PLS) based on structural equation modeling (SEM). The findings have shown that a positive relationship exists between cause participation and purchase intention. Further, product/cause congruence & consumer/cause identification had a positive impact on attitude, while attitude, in turn, showed a favorable association with the purchase intention. This study disclosed the relative importance of the compatibility between the social causes supported by the company with its engaged business while adopting CRM campaigns, and highlighted the need for the involvement of consumers in the CRM programs for their effectiveness

A Review on Predictive Analysis for Diabetic Blood Glucose and Reductionof over fitting inDiabetes using Deep Learning Neural Network

In this research, a prediction system is developed for the illness of diabetes and dropout strategy is made use to minimize the issues of overfitting. The key idea is arbitrarily drop unit from neural network during preparing. Expectation of blood glucose levels Measured by continuous glucose observing gadgets, by utilizing clinical information. The certain rate of a patients in the data set take as a training data and test on the left-over portion of the patients, i.e., the machine need not re-calibrate on other patients in the data set