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| 1 | Magnesium:pathophysiological mechanisms and potential therapeutic roles in intracerebral hemorrhage显示文摘Intracerebral hemorrhage(ICH) remains the second-most common form of stroke with high morbidity and mortality.ICH can be divided into two pathophysiological stages:an acute primary phase,including hematoma volume expansion,and a subacute secondary phase consisting of blood-brain barrier disruption and perihematomal edema expansion.To date,all major trials for ICH have targeted the primary phase with therapies designed to reduce hematoma expansion through blood pressure control,surgical evacuation,and hemostasis.However,none of these trials has resulted in improved clinical outcomes.Magnesium is a ubiquitous element that also plays roles in vasodilation,hemostasis,and blood-brain barrier preservation.Animal models have highlighted potential therapeutic roles for magnesium in neurological diseases specifically targeting these pathophysiological mechanisms.Retrospective studies have also demonstrated inverse associations between admission magnesium levels and hematoma volume,hematoma expansion,and clinical outcome in patients with ICH.These associations,coupled with the multifactorial role of magnesium that targets both primary and secondary phases of ICH,suggest that magnesium may be a viable target of study in future ICH studies. | Jason J.Chang Rocco Armonda Nitin Goyal Adam S.Arthur | 2019 | Neural Regeneration Research2019，14，7： | 6 |
| 2 | An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model显示文摘COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively. | Savita Khurana Gaurav Sharma Neha Miglani Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Nitin Goyal | 2022 | Computers, Materials & Continua2022，，4： | 2 |
| 3 | Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve显示文摘The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data. | Mudita Uppal Deepali Gupta Divya Anand Fahd S.Alharithi Jasem Almotiri Arturo Mansilla Dinesh Singh Nitin Goyal | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 4 | Tale of fat and fib-cardiac lipoma managed with radiofrequency ablation: A case report显示文摘BACKGROUND Cardiac lipoma and lipomatous hypertrophy of interatrial septum(LHIS)are very rare disorders with distinct pathological features.While cardiac lipoma is a well-circumscribed encapsulated tumor of mature adipocytes,LHIS is due to entrapment of fat cells in the interatrial septum during embryogenesis.Although a biopsy is the definitive diagnostic test,these disorders can be differentiated by a cardiac magnetic resonance imaging(MRI).Treatment of LHIS is not warranted in asymptomatic patients.In symptomatic patients,surgical resection is the only recommended treatment,which has shown to improve good long-term prognosis.CASE SUMMARY A 63-year-old Caucasian woman with past medical history significant for hypertension,hypothyroidism,right breast ductal cell carcinoma treated with mastectomy and breast implant,platelet granule disorder,asthma requiring chronic intermittent prednisone use,presented to the outpatient cardiology office with recent onset exertional dyspnea,palpitations,weight gain and weakness.Initial workup with electrocardiogram and holter monitor did not reveal significant findings.During the subsequent hospitalization for community acquired pneumonia,the patient developed symptomatic paroxysmal atrial fibrillation.Transthoracic echocardiogram showed a right ventricular mass.A biopsy was not pursued given the high risk of bleeding due to platelet granule disorder.Cardiac MRI showed characteristic features consistent with cardiac lipoma and LHIS.Prednisone was discontinued.Genetic testing for arrhythmogenic right ventricular dysplasia and 24-h urine cortisol test was negative.As multiple attempts at rhythm control failed with sotalol and flecainide,pulmonary vein isolation and right atrial isthmus radiofrequency ablation were done.She is in follow-up with symptomatic relief and no recurrence of atrial fibrillation for 10 mo.CONCLUSION Benign fatty lesions in heart include solitary lipoma,lipomatous infiltration and lipomatous hypertrophy of interatrial septum.Although transvenous biopsy provides a definitive diagnosis,Cardiac MRI is superior to computed tomography and aids in differentiating benign from malignant lesions.Surgical excision of cardiac lipoma along with capsule and pedicle removal generally prevents recurrence,but with our patient’s unusual tumor features and comorbidities proscribed a surgical approach.Symptom management with antiarrhythmics and ablation techniques were successfully utilized. | Swarna Sri Nalluru Srinivas Nadadur Nitin Trivedi Sunita Trivedi Sanjeev Goyal | 2020 | World Journal of Cardiology2020，12，6： | 1 |
| 5 | An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach显示文摘This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely. | Shobhit Verma Nonita Sharma Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Deepali Gupta Nitin Goyal | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 6 | A Secure and Efficient Signature Scheme for IoT in Healthcare显示文摘To provide faster access to the treatment of patients,healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient.There is a huge limitation in the sensing layer as the IoT devices here have low computational power,limited storage and less battery life.So,this huge amount of data needs to be stored on the cloud.The information and the data sensed by these devices is made accessible on the internet from where medical staff,doctors,relatives and family members can access this information.This helps in improving the treatment as well as getting faster medical assistance,tracking of routine activities and health focus of elderly people on frequent basis.However,the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path.The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data.Certificateless signature,that removes the intricate certificate management and key escrow problem,is one of the practical methods to provide data integrity and identity authentication for the IoT.Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost,encryption and decryption time.This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment. | Latika Kakkar Deepali Gupta Sarvesh Tanwar Sapna Saxena Khalid Alsubhi Divya Anand Irene Delgado Noya Nitin Goyal | 2022 | Computers, Materials & Continua2022，，12： | 0 |
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