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   Table of Contents - Current issue
November-December 2020
Volume 10 | Issue 6
Page Nos. 257-318

Online since Thursday, December 24, 2020

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A transformative document: National Education Policy p. 257
Ameeta Mulla Wattal
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Patient preference for a particular hospital: A prospective questionnaire-based investigation from India p. 260
Srinivas Bojanapu, Mayank Mathur, Jini Jacob, Anju Sharma, Samiran Nundy
Background: The ubiquitous access to media, the Internet, and suggestions from friends relatives have empowered patients not only choose but also make informed decisions about their health needs and how and where to be addressed. Although there are many guides and studies on the main factors which infl choice, particularly in the USA. There are very few from India and also hospitals from the large private sector constantly advertise their strengths. Aim: We studied the factors which influenced a patient's decision to seek medical attention at a not-for-profit tertiary care centre in India. Materials and Methods: Four hundred consecutive patients or their relatives were provided with a printed sheet that contained a total of 15 questions, which included the unit's and hospital's trustworthiness, reputation, infrastructure, affordability and accessibility, among others. SPSS (version 24) was used, and reliability and validity tests were done. Principal component analysis with Varimax rotation method was done. Results: There were four constructs from 15 observed variables with a cumulative variance of 55%. Construct 1 had a variance of 31%, and it included variables such as trust in hospital and doctor, infrastructure, diagnostic facilities and ancillary programmes. Construct 2, with a variance of 8.9%, had accreditation, academics and research. Construct 3, with a variance of 7%, had information seeking through the Internet and easy accessibility. Construct 4 had a variance of 7% with health insurance and a suggestion from a neighbour/family or from a friend. Nearly 64% of the patients in the study group travelled more than 100 km. Conclusion: The most influential factors for choosing a hospital are trust in hospital and doctor, along with infrastructure and ancillary services, followed by certified standards of care and academics and research. Surprisingly, the distance was not a deciding factor in the majority, nor was empanelment with insurance companies.
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Feature extraction for epileptic seizure detection using machine learning p. 266
Renuka Mohan Khati, Rajesh Ingle
Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming. Aim: An automated approach is required that can assist the neurologists in seizure diagnosis in order to minimise the diagnostic time. Materials and Methods: Electroencephalogram (EEG) signals record the electrical activities of the brain and can be used effectively in seizure diagnosis. We have evaluated our system using Bonn university database. Appropriate feature selection is very important in pattern recognition problems. We used recursive feature elimination (wrapper method), for the selection of best features. In our study, we have used EEG signals to evaluate the performance of seven machine learning algorithms. Results: We obtained a classification accuracy of 99%, which is a significant improvement over the state-of-the-art methods. The classification performance across 10 folds of cross validation of Logistic regression and Adaboost was the highest with an accuracy of 99%. While using naive bayes and random forest, the Area under the ROC curve value obtained was 1. The sensitivity obtained using naive bayes classifier was 100%. Conclusion: Our approach requires minimum feature extraction that makes it an efficient approach. Recursive feature elimination was used to select the features which yield good accuracy results. It was observed that logistic regression and Adaboost performed superior with an accuracy of 99%.
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Quality by design-enabled development and characterisation of nanocarrier of azithromycin p. 272
Swapnil Patil, SG Gattani, Pradip Nirbhavane, OP Katare, Kiran Patil
Background: Azithromycin is an antibiotic, which is preferentially used in the treatment of Mycobacterium Avium Complex (MAC). However, it suffers from drawbacks such as poor solubility, poor bioavailability and adverse effects such as gastrointestinal intolerance, resulting into poor patient compliance. Aim: To develop and optimize lipid based nanocarriers of Azithromycin using QbD approach. Methods: Nanostructured lipid carrier (NLC) were developed by using the solvent diffusion evaporation method with view to release drug in a sustained release manner. The initial factors screening and risk assessment done through Taguchi design and afterwards the optimization of independent factors along with final outcome i.e. critical quality attributes (CQAs) were done by Box Behnken Design (BBD). Results: The results of Physicochemical analysis revealed that a size of optimized Azithromycin Nanocarrier is 346.18 ± 12.90 nm and Polydispersity Index (PDI) of 0.21 ± 0.08; the entrapment efficiency (% EE) of 68.45 ± 4.42% w/w and drug loading of 47.16 ± 0.80 % w/w. The in vitro release study of the Azithromycin Nanocarrier showed a biphasic drug release pattern in simulated intestinal fluid (SIF) nanocarrier shown sustained release kinetics up to 02 days and similar pattern shown in Phosphate buffer saline (PBS) pH 7.4, release kinetics shows initial burst release upto 12 hr followed by sustained release upto 04 days in PBS, pH7.4. The sustained release pattern of the formulation is beneficial to improve the oral bioavailability of the drug. Conclusion: Thus, present research leads to development of formulations which may reduce dose of drug, dosing frequency and enhanced efficaciously of drug. Ultimately it reducing the patient avoidance in treatment.
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A glimpse of unfolding surgical crisis amid the COVID-19 pandemic: Need urgent global attention and universal recommendation Highly accessed article p. 279
Jitendra Kumar, Rajni Raina
The COVID-19 pandemic has resulted in serious consequences for all those patients who need treatment for any other routine ailments. The worst sufferers are the patients who need to undergo elective or non-emergency surgical procedures. But globally, no concrete steps have so far been taken to reduce the huge suffering of routine surgical patients either during or the aftermath of the pandemic. The aim of this study is not only to draw global attention by highlighting the magnitude of suffering faced by routine surgical patients but also to put the much-needed proposal for the urgent need of universal guidance to counter the huge challenges waiting ahead. In an attempt to find the scale of problems faced by patients in need of routine surgical procedures and their suggested solutions available to date, we did an extensive search online on different search engines and scientific databases. Unfortunately, no country is tracking or reporting the data of routine surgical patients affected by this pandemic. Whatever estimated data in this regard is projected, is catastrophic. The worst at the global level is that no step has so far been taken to look into any solution for this huge suffering faced by millions of people. The COVID-19 pandemic has posed a huge challenge for routine surgical patients across most of the affected parts of the world, and we cannot turn a blind eye for long time on this issue. All stakeholders at the global level must come forward with uniform guidance to handle this global emergency during and post-pandemic time.
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Artificial intelligence in medical diagnosis p. 286
Ronit Jaiswal, Ramesh Lal Sapra, Girish K Jha, Samiran Nundy
The use of artificial intelligence (AI) in medicine made its beginning five decades ago in 1972 when researchers at Stanford University in the USA developed an expert system MYCIN for treating blood infections. Since then, AI-based research in medicine is evolving every day to bring efficiency into medical diagnosis, early prediction of outcome, better clinical treatment and for rapid prognosis and recovery of patients. With the availability of an increasingly growing patients' associated information such as texts, audios, images and videos in structured and unstructured formats, it has become a challenge for computer scientists to process such information and to understand its insights into meaningful conclusions. With the development of powerful algorithms, AI has proven its ability in understanding the insights into data and taking health-related decisions in real time and improving clinical practice. This article provides an overview of various AI techniques in healthcare, especially deep learning used in medical diagnosis. The article also highlights the challenges and concerns of AI implementation.
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Microglandular adenosis of the breast: A case report and a highlight on main diagnosis and management aspects p. 293
Omar Hamdy, AbdAllah Elzeiny, Gehad A Saleh, Amany Hassan
Microglandular adenosis (MA) is a basically benign–recently borderline-breast disease which mimics well differentiated breast carcinoma clinically and pathologically. The incidence of carcinoma associated with MA reaches up to 27%. In this manuscript, we present a 64-year-old female patient presented with a right breast swelling. Sonomammogram revealed a breast imaging, radiology, and data system IVa right breast architectural distortion. Breast magnetic resonance imaging showed a heterogeneous segmental non mass enhancement in the upper outer quadrant with less extension to lower outer quadrant. Core needle biopsy revealed small uniform glandular structures lined by single layer cuboidal cells with luminal eosinophilic secretions. The cells showed positivity for cytokeratin (CK) and S-100 while P63, smooth muscle actin and CK5/6 were negative. Wide local excision of the mass was done. Microscopic examination revealed proliferation of small glands surrounded by collagenous stroma, some of them infiltrated into the adipose tissue, these glands were lined by a single layer of epithelial cells that lacks a myoepithelial cell layer. They showed positive reaction for S100 and were negative for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. This led to the diagnosis of breast MA. MA should be considered as a precursor for malignancy rather than an innocent lesion. Adequate surgical excision is recommended.
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Post-operative ischaemic stroke after transpulmonary embolism: A diagnostic challenge p. 297
Sagarika Panda, Gaurav Jain, Sonu Sama, Vamshi Krishna
Fixing a femoral neck fracture in elderly patients restores their independent mobility, and decreases the associated mortality and morbidity. Perioperative stroke is the most undesired complication in such patients, having a miserable outcome. We report a similar case where the patient developed ischaemic stroke in the immediate post-operative period, where a diagnostic workup revealed the presence of a new-onset pulmonary arterial hypertension with suspected embolism, in the absence of cardiopulmonary shunt, visible thrombus or embolus of cardiac origin. This case highlights the diagnostic dilemma in evaluating the cause of post-operative stroke and emphasises the need for a multimodal approach to investigate the suspected acute pulmonary embolism and to identify the possible sites for the origin of an embolus.
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Doctor–patient relationship: Rust in trust p. 300
Bhuwan Chand Panday, Prabhat Kumar Choudhary
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Staged ventral hernia repair using “Foker-” type traction sutures: A novel technique p. 303
Rupa Banerjee, Gaurav Singh, Muni Varma, Aparajita Mitra, Satish Kumar Aggarwal
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Food safety amid coronavirus disease 2019 p. 305
Ravinder Thaper
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Association of obesity with COVID-19 deaths p. 308
Bhupalam Pradeepkumar, Narayana Goruntla
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Lockdown versus no lockdown: Strategies for COVID-19 and its impact on mental health and economy p. 310
Ramananda Ningthoujam
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Corona warriors under risk during COVID-19 pandemic p. 314
Shashi Bahl, Tarun Goyal
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Professor R. K. Tandon (1941–2020): A tribute to an inspiring mentor p. 316
Anil C Anand
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