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Introduction More than 80 million people have been infected with coronavirus disease 2019 (COVID-19) infection worldwide till date with more than 17,00000 fatalities. Although COVID-19 commonly affects respiratory system in the form of cough and dyspnea, a neurotropic presentation has been described in one-third of patients. Objective We report an atypical case of COVID-19 with mild symptoms who presented to our hospital with features suggestive of severe Guillain-Barré syndrome (GBS). Discussion The mechanisms by which severe acute respiratory syndrome coronavirus 2 causes neurologic damage are multifactorial, including direct damage to specific receptors, cytokine-mediated injury, secondary hypoxia, and retrograde travel along nerve fibers. The pathogenesis of GBS secondary to COVID-19 is not yet well understood. It is hypothesized that viral illnesses-related GBS could be mediated due to autoantibodies or direct neurotoxic effects of viruses. Conclusion In this ongoing era of pandemic, it is very important for the clinicians to be aware of association of GBS with COVID-19, as early diagnosis and treatment of this complication could have gratifying results. It is also very important to differentiate GBS from critical illness neuropathy and respiratory distress secondary to COVID-19 itself, as treatment to the above conditions is quite different and inability to correctly diagnose could lead to significant increase in morbidity and mortality.

This paper establishes a hybridized intelligent machine learning based currency exchange forecasting model using Extreme Learning Machines (ELMs) and the Jaya optimization technique. This model can very well forecast the exchange price of USD (US Dollar) to INR (Indian Rupee) and USD to EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. The proposed ELM-Jaya model has been compared with existing optimized Neural Network and Functional Link Artificial Neural Network based predictive models. Finally, the model has been validated using various performance measures such as; MAPE, Theil's U, ARV and MAE. The comparison of different features demonstrates that the technical indicators outperform both the statistical measures and a combination of statistical measures and technical indicators in ELM-Jaya forecasting model. Keywords: Currency exchange prediction, Extreme Learning Machine (ELM), Jaya, Neural Network (NN), Functional Link Artificial Neural Network (FLANN)

Autism Spectrum Disorder (ASD) is a highly heterogeneous set of neurodevelopmental disorders with the global prevalence estimates of 2.20%, according to DSM5 criteria. With the advancements of technology and availability of huge amount of data, assistive tools for diagnosis of ASD are being developed using machine learning techniques. The present study examines the possibility of automating the Autism diagnostic tool using various machine learning techniques on a dataset of 701 samples that contains 10 fields from AQ-10-Adult and 10 from individual characteristics. It takes two scenarios into consideration. First one is ideal case, where there are no missing values in the test cases. In this case Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) classifiers are trained and tested on the pre-processed dataset. To reduce computational complexity Recursive Feature Elimination (RFE) based feature selection algorithm is applied. To deal with the real-world data, in the second case missing values are introduced in the test dataset for the fields' 'age', 'gender', 'jaundice', 'autism', 'used_app_before' and their three combinations. Support Vector Machine, Random Forest, Decision Tree and Logistic Regression based RFE algorithm is introduced to handle this scenario. ANN, SVM and RF classifier based learning models are trained with all the cases. Twelve classification models were generated with RFE, out of which best performing models specific to missing value were evaluated using test cases and suggested for ASD Diagnosis.

For the prediction of exchange rate, this paper proposes a hybrid learning frame work model which is a joint estimation of On-Line Sequential Extreme Learning Machine (OS-ELM) along with optimized feature reduction using Krill Herd (KH). The proposed learning scheme is compared with Extreme Learning Machine (ELM) and Recurrent Back Propagation Neural Network (RBPNN), considering three factors such as; without feature reduction, with statistical based feature reduction using Principal Component Analysis (PCA) and with optimized feature reduction techniques such as KH, Bacteria Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The models are applied over USD/INR, USD/EURO, YEN/INR and SGD/INR, constructed using technical indicators and statistical measures considering 3, 5, 7, 12 and 15 as window sizes. The results of comparisons of different performance measures in testing phase and MSE in training process demonstrate that the proposed OSELM-KH exchange rate prediction model is potentiality superior compared to others.

Purpose The timely management of cancer surgery suffered due to COVID-19 and nationwide lockdown. Continuing cancer surgery was a challenge faced by all. We present our experience on cancer surgery in a cancer centre with high volume of patients and limited resources during early pandemic. Methods We retrospectively analysed our operation theatre database on surgery and anaesthesia from 1st April to 30th June 2020. Results A total of 457 surgeries were done—complex major, major, intermediate and minor surgeries constituted 43%, 25%, 12% and 20%, respectively. Median age of patient was 50 years, and 76% were below 60. The median ASA class was I (I–IV), and 97% were ASA I and II. The median Eastern Cooperative Oncology Group score was 0 (0–3), and 92% had score 0 and 1. Major cases done under regional anaesthesia were 30.7%. Median length of intensive care unit stay was 1 (1–6) days, and length of hospital stay was 7 (7–15) days. Clavien–Dindo Grade II complication in patients above 60 years was 16.4% and below 60 years was 17.6% (p = 0.76). 10% in ASA I compared to 26% of ASA II (p = 0.00) and 15.9% with ECOG 0 and 1 compared to 30.9% with ECOG 3 and 4 (p = 0.01) had grade II complication. Four (1%) patients had Grade ≥ III CD complication. Covid testing was undertaken in 52% patients pre-operatively, and there was no positive case in post-operative period. Conclusions Adopting and implementing institutional policy catering to limited resource available at our centre, we facilitated cancer surgery.

Antenna designers around the globe have extensively researched upon the patch antenna and its array structures for achieving better performance for various operations. One of the novel techniques is to use nanomaterial composites over the substrate of a microstrip patch antenna to enhance its performance. Nanostructures can provide unique characteristics and have the potential to improve the performance of the patch antenna. This paper focuses on the study and design of a nanomaterial-based 1 × 2 rectangular microstrip patch antenna array for X band operation. The proposed antenna array is designed over an FR4 substrate where the individual radiating patches are realized using gold deposited Zinc Oxide (ZnO) nanorods. The comparison of performance parameters of the proposed antenna array with the conventional copper (Cu)-based patch antenna array is made. The results obtained show that there is an improvement in the return loss and bandwidth of the proposed array structure.

Forecasting currency market plays an important role basically in all aspects of international financial management. Based on the claimed poor performance and less efficiency of the popular forecasting methods, an Extreme Learning Machine (ELM)—elitism-based self-adaptive multi-population Jaya model—is designed with the possibilities of getting maximum prediction accuracy. The model has evaluated by using the exchange rate data of USD to INR and USD to EURO as in most of the countries USD is used as the base currency. The experimented model has explored its prediction ability over six-time horizon, which includes the 6 days between 1 day and 1 month in advance. For a standardized comparison, two broadly accepted prediction models such as standard ELM and Back-Propagation Neural Network (BPNN) has been recommended for this research work. Along with these two standard models, a Jaya optimized technique applying on both ELM and BPNN is also considered for experimentation. The simulated result depicted the outstanding performance of the proposed ELM-elitism-based self-adaptive multi-population Jaya model over ELM, BPNN, ELM-Jaya, and BPNN-Jaya.

Mutual fund is an ideal investment basically for those who do not know how to invest money in the security market. Several research methods have been published by the researcher for predicting mutual fund. This study has explored on three prediction models such as back propagation neural network (BPNN), recurrent back propagation neural network (RBPNN) and recurrent radial basis function neural network (RRBFNN), and the prediction models are validated using SBI Magnum Equity and UTI Equity mutual fund dataset. These two mutual funds are outperformed over market cycles since 2010. The dataset has trained and tested in 7:3 ratio and the performance is validated using MSE during the training phase. The study has explored on 1, 5 and 7 days ahead prediction, and the performance of prediction methods are evaluated by RMSE and MAPE during testing. The simulation result shows that RBPNN is outperformed over the rest two prediction methods.

The basic aim of risk management is to recognize, assess, and prioritize risk in order to assure that the uncertainty should not deviate from the intended purpose of the business goals. Risk can take place from various sources, which includes uncertainty in financial markets, recessions, inflation, interest rates, currency fluctuations, etc. Various methods used for this management of risk are faced with various decisions such as the market price, historical data, statistical methodologies, etc. For stock prices, the information derives from the historical data where the next price depends only upon the current price and some of the outside factors. Financial market is very risky to invest money, but the proper prediction with handling the risk will benefit a lot. Various types of risk in the financial market and the appropriate solutions to overcome the risk are analyzed in this study.

Background and Objectives: The EuroQol five-dimensions – 3-level (EQ5D) is a versatile quality of life (QOL) instrument with five dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) and a visual analog scale. It can be used to calculate quality-adjusted life years. We aimed to evaluate the validity, reliability, and responsiveness of an Odia version of EQ5D and to study the QOL of cancer patients in our part of the country as cancer treatment in India still focuses largely on longevity due to scarcity of resources. Materials and Methods: The EQ5D tool was translated into Odia language in collaboration with the EQ group. This tool and the World Health Organization-5 (WHO-5) questionnaires were administered to 155 surgical outpatients and 150 cancer patients in two hospitals of Eastern India. The convergent and discriminant validities (construct validity), concurrent validity, reliability (test-retest method of administering the tool to a part of the population after 7–14 days), and the internal consistency (Cronbach's alpha) were measured using preestablished hypotheses. The data from the cancer patients were analyzed separately. Results: The QOL worsened with age and was worse in cancer patients proved that the tool had good construct validity. The Anxiety Depression dimension had good correlation with all the dimensions WHO-5 (rho > 0.4) indicating a good concurrent validity. Internal consistency and reliability of the tool were good (Cronbach's alpha > 0.7). Cancer patients had a poor QOL (mean EQ5D index 0.37SD 0.4) with male patients, patients with Grade II cancer or referred for pain care services and those with living spouses reporting worse QOL. Conclusions: The Odia version of the EQ5D has good reliability and validity for the measurement of health status in cancer and outpatient department patients. Cancer patients in this part of the country have a poor QOL and may need a closer look at pain management and improved societal support systems.

Successful prediction of mutual fund with maximum accuracy is a great challenge because of highly fluctuating behaviour of the financial market. The prediction of Net Asset Value (NAV) of mutual fund helps investors to wisely plunge money into profitable mutual funds. This survey covers more than 40 related published articles in the field of mutual fund and gone through a systematic review of the various nature inspired techniques, used for NAV prediction with its performance analysis. The performance of mutual fund is highly correlated with the stock market, hence some of the stock market prediction analysis is also well throughout. Through this survey it is found that very few works is done in the field of NAV prediction using optimization techniques while for performance analysis of mutual fund many nature inspired optimization (NIO) techniques have been employed. On the whole, this paper gives a comprehensive review of the literature in the field of mutual fund.

Patients in the perioperative period and intensive care unit are commonly exposed to blood transfusion (BT). They are at increased risk of transfusion transmitted bacterial, viral and protozoal diseases. The risk of viral transmission has decreased steadily, but the risk of bacterial transmission remains same. Bacterial contamination is more in platelet concentrates than in red cells and least in plasma. The chances of sepsis, morbidity and mortality depend on the number of transfusions and underlying condition of the patient. Challenges to safe BT continue due to new emerging pathogens and various management problems. Strategies to restrict BT, optimal surgical and anaesthetic techniques to reduce blood loss and efforts to develop transfusion alternatives should be made. Literature search was performed using search words/phrases blood transfusion, transfusion, transfusion transmitted diseases, transfusion transmitted bacterial diseases, transfusion transmitted viral diseases, transfusion transmitted protozoal diseases or combinations, on PubMed and Google Scholar from 1990 to 2014.

Forecasting future trends of the stock market using the historical data is the exigent demand in the field of academia as well as business. This work has explored the feature optimization capacity of firefly with an evolutionary framework considering the biochemical and social aspects of Firefly algorithm, along with the selection procedure of objective value in evolutionary notion. The performance of the proposed model is evaluated using four different stock market datasets, such as BSE Sensex, NSE Sensex, S&P 500 index and FTSE index. The datasets are regenerated using the proper mathematical formulation of the fundamental part belonging to technical analysis, such as technical indicators and statistical measures. The feature reduction through transformation is carried out on the enhanced dataset before employing the experimented dataset to the prediction models such as Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN). For feature reduction, both statistical and optimized based feature reduction strategies are considered, where Principal Component Analysis (PCA) and Factor Analysis (FA) are examined for statistical based feature reduction and Firefly Optimization (FO), Genetic Algorithm (GA) and Firefly algorithm with evolutionary framework are well thought out for optimized feature reduction techniques. An empirical comparison is established among the experimented prediction models considering all the feature reduction techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. From the simulation result, it can be clearly figured out that firefly with evolutionary framework optimized feature reduction applying to OSELM prediction model outperformed over the rest experimented models.

Present study is an attempt to investigate the fertility performance with reference to ABO incompatibility among 89 Bengali Muslim fertile couples of Cachar District, Assam. The findings of the present study reveal that mean conception and mean live birth are more among the compatible couples but mean pregnancy wastage, particularly miscarriage, is high among the incompatible couples.

Background: Typhoid fever is classically caused by Salmonella enterica serotype typhi.Recently the frequency of isolation of S. paratyphi A (SPA) has been increased in comparison to S. typhi in Indian scenario. Aim: To observe the rate of isolation and antimicrobial susceptibility pattern of SPA from suspected enteric fever cases attending tertiary care centres of Eastern Orissa. Settings and Design : Retrospective study Materials and Methods: 1488 blood samples were collected during different duration of fever and cultured in BACTEC blood culture system and bottles showing signal for growth were subcultured and identified as Salmonella spp. by standard procedure and mini API (Biomeriux) and antimicrobial susceptibility by disc diffusion method. Statistical Analysis: Chi square test. Results: 167 Salmonella spp. were isolated including 83.8% Salmonella paratyphi A and 16.6% S. typhi . Among them 102 were males and 65 were females with mean age of 22.7 yrs. S. paratyphi A was the predominant spp. each year but during 2008 – 2011, there was a dramatic rise (significant P value- 0.034). Multidrug resistance was noticed in 10.2% of the isolates. 98% of S. paratyphi A were resistant to nalidixic acid and 41% to ciprofloxacin, but the MIC of ciprofloxacin was raised between 1-2 μgm/dl showing the relation between nalidixic acid resistance and raised MIC of ciprofloxacin. Conclusion: Nalidixic acid should be tested along with ciprofloxacin disc while testing for susceptibility and MIC of ciprofloxacin is mandatory before advocating therapy to prevent treatment failure.

Classification is a vital tool for understanding the relationships of living things using which similar things can be grouped together. Classification of elements into groups makes the study relatively easy. Therefore, classification is necessary to know salient features and characteristics of living organisms as well as their inter relationship among different group of organisms, as the correct classification of a person's disease is important for proper treatment. Support vector machine (SVM) was the first proposed kernel-based method, which uses a kernel function to transfer data from input space into high dimensional feature space; it searches for a separating hyper-plane. SVM is based on simple ideas which originated in statistical learning theory; hence the aim is to solve only the problem of interest without solving a more difficult problem as an intermediate step. SVM apply a simple linear method to the data but in a high-dimensional feature space non-linearly related to the input space. Even though we can think of SVM as a linear algorithm in high dimensional space, but in practice it does not involve any computations in that high-dimensional space. As dimensionality is curse to gene expression data set, in this paper Principal Component Analysis (PCA) is used for feature reduction to breast cancer, lung cancer and cardiotography data sets, and SVM is trained by linear, polynomial and radial basis function (RBF) kernels applied on each of these data sets and the comparison among them shows that RBF is better for the three data sets.

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

Background and Aims: Breast cancer surgery can be carried out as day-care procedure to increase patient turnover, decrease disease progression and financial burden. The present study was carried out to assess the role of dexmedetomidine in breast cancer surgery as a day-care procedure. Methods: This prospective randomised, double-blind study was carried out on 100 patients screened for day-care breast cancer surgery. They were divided into two groups of 50 each; who received either normal saline (Group NS) or 0.6 μg/kg/h dexmedetomidine (Group D) infusion from 10 min before induction until skin closure. All patients were given general anaesthesia. The incidence of discharge, post-operative pain (POP), average rescue analgesia (fentanyl) required and side effects were noted. Statistical analysis was performed using Student's t-test and Chi-square test. Results: Incidence of discharge in group NS was 60% compared to 88% in Group D (P = 0.001). Average rescue analgesia requirement by group NS was 136.07 ± 43.06 μg, whereas it was 77.5 ± 29.86 μg in Group D (P = 0.01). The incidence of POP in 6 h and within 2 h of expected discharge time in Group NS was 56% and 28%, respectively, and in Group D, it was 8% in both the periods (P < 0.001 and 0.01). Side effects such as post-operative nausea, vomiting and bleeding were encountered in eight and two patients, respectively, in Group NS and two and one patients, respectively, in Group D. Conclusion: Dexmedetomidine as an anaesthetic adjuvant makes breast cancer surgery feasible on day-care basis.

The present paper deals with the fertility performance of 249 women working in the tea industry. The tea garden where these women are working is called Rajgarh Tea Estate in the district of Dibrugarh Assam. These women are mainly engaged as waged labourer for plucking tea leaves. The women considered for the present study are classified age specifically into eight different groups. The fertility performances of these women have been studied age specifically. Biological as well as sociocultural factors are considered to see its influence on fertility. Age at marriage age at first childbirth birth interval pregnancy wastage and age at menopause are the biological factors; while education and occupations are the social factors that have been considered for the present study. (authors)

Objective: Cartap hydrochloride poisoning is an uncommon poisoning encountered in India. The aim of this study was to assess the characteristics, severity, management, and outcome of patients with Cartap poisoning, treated with N-acetylcysteine (NAC) in intensive care unit (ICU) of a tertiary referral hospital.Methods: This is a retrospective study where 11 cases of Cartap poisoning admitted to hospital ICU between 2011 and 2016 were identified from the case records. The demographic data, mode of ingestion, time to treat, ingested dose and severity of poisoning, presenting features, duration of mechanical ventilation (MV), dose of NAC used, complications, and outcome were noted.Results: Patients were scored as moderate or severe cases of poisoning. Severe cases consumed >10 g, underwent gastric lavage > 4 hrs, and took Cartap concomitantly with alcohol or in empty stomach. Duration of MV varied from 68-94 hrs in severe cases and 12-48 hrs in moderate cases. Average dose of NAC in severe case was 11.19 g and moderate case was 8.89 g. The most common presenting symptoms were vomiting, altered sensorium, and breathlessness. Severe cases had more complications, and the most common complication was hypotension followed by seizures. Survival was 100%.Conclusion: 50% of Cartap poisoning cases had good survival outcome. Severity of poisoning depends on amount of Cartap ingested, time taken for gastric lavage, and concomitant administration of alcohol. Duration of MV and dose of NAC and complications encountered correlates with the severity of poisoning.

Objective: Endogenous production of glutamine may be reduced during critical illness. The shortage of glutamine is reflected as a decrease in plasma concentration, which is a prognostic factor for outcome in sepsis. Therefore, we have studied the effect of enteral or parenteral glutamine therapy on biochemical parameters and the hospital stay of critically ill patients.Methods: A total of 66 critically ill patients aged 18-70 years, admitted to central and medical intensive care unit of a tertiary care hospital were randomly divided into three equal groups; 22 in each group: Group 1 (control group) received no glutamine, Group 2 received oral glutamine 0.5 g/kg/d for 5 days, and Group 3 received parenteral glutamine 0.5 g/kg/d by intravenous infusion for 5 days. All patients received glutamine-free tube feed throughout the study period. Total leukocyte count (TLC), total lymphocyte count, total protein and serum albumin, serum lactate, and sequential organ failure assessment (SOFA) score were recorded on each day for 7 days and were compared.Results: Decrease in the TLC and increase in lymphocyte count was most evident in Group 3 compared to Groups 2 and 1 which was statistically significant. Decrease in serum lactate and increase in serum protein and albumin was maximum in Group 3 compared to Groups 1 and 2 which was statistically significant. The mean duration of hospital stay of Group 3 was the least followed by Groups 2 and 1 which was statistically not significant. There was an improvement in SOFA score in all the three groups.Conclusion: Parenteral glutamine in a dose of 0.5 g/kg/d was more potent than oral glutamine in improving the biochemical parameters. The duration of hospital stay was similar in all the groups after treatment.

Background: Among several parameters to understand the living standard of a country, child mortality is a vital sensitive indicator. The study of child mortality has long been largely neglected by research in developing nations of world including India. Objectives: In this paper, an attempt has been made to see the main causes of child death and also try to find out the various factors associated with it. Materials and Methods: Interview with structured schedule was the prime method of data collection, while observation method was also applied whenever necessary. The cause of death was ascertained using verbal autopsy procedure. Percentage distribution, chi-square, and logistic regression model have been used to determine the factors affecting child mortality. Result: The study reveals that out of 604 live births, 562 (93.06%) children are surviving while 42 (6.95%) are dead. Female infant and child mortality are higher than that of male. The main causes of death among the infants, 1–5 years, and 5–14 years children are low birth weight (30.00%), diarrhea (44.44%), and fever unspecified (42.86%), respectively. Conclusion: In this study, factors such as mothers’ age at first child birth, birth order, mothers’ education, institutional delivery, and immunization have played a very important role in reducing child mortality.

Objectives Health risk due to extensive use of organic pesticides both organochlorine and organophosphate is obvious in the third world countries and therefore to study the safety of the workers, health surveillance is required. Methods The study has been attempted among 180 field workers in Jagroad who were engaged in handling pesticide extensively in agriculture. In the Jagiroad area some places of agricultural field were selected randomly. There was no definite work schedule for the workers, exposures were recorded in terms of weeks. All the workers selected for study were male and within the age group of 30.00±2.5 exposed for a period of more than 10–30 years. A group of 60 subjects was selected with comparable age, sex, and socio-economic status but not occupationally exposed to pesticides. Protective devices and hygienic measure taken by the applicators were also recorded in recoded pro forma through personal interview. Results O.C. and O.P. pesticide used in the field mainly consist were Aldrine, BHC, Dimethoate, phosphomedon, Endosulphan, Melathion, Carbofuron. The morbid health conditions found were chiefly neurological (38%), eye irritation (42.2%), skin irritation (25%), stomach trouble (45%), joint pain, vomiting, excessive salivation(35%), and heart trouble. Biochemical study revealed blood ChE activity were reduced in exposed people. However biochemical test showed that blood urea, SGPT, SGOT, alkaline phosphates were in normal range. Conclusions The present finding suggested the application of the safety measure, public awareness and training of the farmer about safe handling of pesticide along with protective devices were suggested.