COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. ...The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.
The paper examines the impact of energy prices on electricity generation by different fuel sources (i.e., oil, gas, and hydropower) in Egypt by employing the Autoregressive Distributed Lag approach ...and bounds test. Two models are estimated where the first accounts for oil prices only whereas the second include both gas and oil prices. In the first model, oil prices negatively affect the electricity produced from oil in the short-run with no impact in the long-run. Also, hydropower is complementary for oil in electricity production only in the short-term whereas gas is a substitute for oil in both long and short terms. In the second model, both energy prices influence electricity generation from oil in both short and long runs while gas and hydropower are respectively, substitute and complementary to oil in both long and short-run.
The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key ...role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.
Transplant is the optimal therapy for patients with end-stage renal disease. Acute cellular rejection refractory to treatment remains a major risk factor for graft loss and poor outcomes. In this ...study, we describe a 39-year-old man who received a living-related kidney transplant. Two days after transplant, the patient displayed acute deterioration of graft function. Conventional anti-rejection therapy was initiated, but graft function did not improve. Biopsy revealed acute cellular rejection (grade IIA), and C4d and HLA antibodies were negative. Immunohistochemistry phenotyping revealed clusters of CD20-positive lymphocytes, with 80% being CD3 positive. Rituximab was prescribed, and graft function improved dramatically. After 1 week, a second graft biopsy was done due to lagging of graft function, shown by serum creatinine of 2.1 mg/dL. Biopsy revealed regenerating acute tubular necrosis with disappearance of the CD20-positive lymphocyte cluster infiltrates. Two year, after transplant, the patient's graft function maintained stable. Phenotyping of the cellular infiltrate is important as it may lead to a proper selection of immunosuppression and consequent improvement of graft outcome.
Vaccination hesitance for the COVID-19 booster dosage among hemodialysis patients is an important barrier in reducing morbidity and mortality linked to COVID-19 infection. Hence, this study aimed to ...explore the predictors of the third (booster) dose of COVID-19 vaccine intention among CKD patients on hemodialysis from the Kingdom of Saudi Arabia (KSA).
This study was a multi-center cross-sectional study conducted at four dialysis centers in KSA from 13 February 2022 to 21 June 2022. The data was collected by the nephrologist in charge of the unit using a structured study questionnaire, which consisted of four parts; socio-demographic and clinical variables, questions about COVID-19 infection and subjective assessment of health state, COVID-19 booster dose vaccination intention and confidence in vaccines and preferences, and a health belief model. The study population consisted of 179 hemodialysis patients.
Participants in the study had conflicting health beliefs about their vulnerability to COVID-19 infection and the severity of the COVID-19 infection. Study participants expressed positive health beliefs about the advantages of the COVID-19 booster dose, and reported less perceived obstacles in receiving the vaccine. The influence of cues on action among the study population was high. A total of 140 (78.2%) hemodialysis patients expressed their intention to receive the COVID-19 booster dose. Patients who reported poor health in the self-rating of their health status had a substantially higher definite intention to take the COVID-19 booster dose, according to the chi-square test (11.16, df = 3,
= 0.01). There was a significant association between the constructs in the HBM model and COVID-19 vaccine (booster) intention. Marital status (OR = 1.67, CI 1.07-2.58) was found to be the strongest predictors of a definite intention to receive a COVID-19 booster dose. Confidence in the locally manufactured vaccine (OR = 0.33, CI 0.17-0.60), education (OR = 0.62, CI 0.41-0.93), and rating of health status (OR = 0.43 CI 0.25-0.74) were the strongest significant correlates of having no definite intention to take the COVID-19 vaccination.
HBM constructs were found to be significantly associated with vaccination intention, which can be considered while planning policies to promote COVID-19 booster vaccination among hemodialysis patients. The study results could be utilized in drafting policies to improve COVID-19 booster dose vaccination uptake among hemodialysis population.
PurposeThis article investigates the determinants of cross-section variation of initial public offerings' (IPOs) first-day returns in a sample of 710 issues across seven emerging markets between 2013 ...and 2017.Design/methodology/approachOrdinary least squares regression (OLS) and the semi-parametric quantile regression (QR) technique are employed. QR enables to analyse beyond the explanatory variables' relative mean effect at various points in the endogenous variable distribution. Furthermore, parameter estimates under QR are robust to the existence of outliers and long tails in the data distribution.FindingsUnderpricing varies across countries with an average of 78%. According to the OLS results, independent variables explain 26% of the variation of IPOs' first-day returns. Findings show that employing QR is important, given the non-normality of the data and because each quantile is associated with a different effect of explanatory variables.Originality/valueIn addition to firm-specific, market-specific and issue-specific factors, the paper extends IPOs' underpricing literature through studying the impact of country-specific characteristics, largely neglected by literature, on IPO underpricing.
Spasticity is a common complication of many neurological diseases and despite contributing much disability; the available therapeutic options are limited. Peripheral magnetic stimulation is one ...promising option. In this study, we investigated whether peripheral intermittent theta burst stimulation (piTBS) will reduce spasticity when applied directly on spastic muscles.
In this sham-controlled study, eight successive sessions of piTBS were applied directly to spastic muscles with supra threshold intensity. Assessment was done by modified Ashworth scale (mAS) and estimated Botulinum toxin dose (eBTD) at baseline and after the 8th session in both active and sham groups.
A total of 120 spastic muscles of 36 patients were included in the analysis. Significant reduction of mAS and eBTD was found in the active compared to sham group (p < 0.001). The difference in mAS was also significant when tested in upper limb and lower limb subgroups. The degree of reduction in mAS was positively correlated with the baseline scores in the active group.
piTBS could be a promising method to reduce spasticity and eBTD. It consumes less time than standard high frequency protocols without compromising treatment efficacy.
Clinical trial registry number: PACTR202009622405087. Retrospectively Registered 14th September, 2020.
An artificial neural networks (ANNs) model was developed to predict 5-year graft survival of living-donor kidney transplants. Predictions from the validated ANNs were compared with Cox ...regression-based nomogram.
Out of 1900 patients with living-donor kidney transplant; 1581 patients were used for training of the ANNs (training group), the remainder 319 patients were used for its validation (testing group). Many variables were correlated with the graft survival by univariate analysis. Significant ones were used for ANNs construction of a predictive model. The same variables were subjected to a multivariate statistics using Cox regression model; their result was the basis of a nomogram construction. The ANNs predictive model and the nomogram were used to predict the graft survival of the testing group. The predicted probability(s) was compared with the actual survival estimates.
The ANNs sensitivity was 88.43% (95% confidence interval CI 86.4-90.3), specificity was 73.26% (95% CI 70-76.3), and predictive accuracy was 88% (95% CI 87-90) in the testing group, whereas nomogram sensitivity was 61.84% (95% CI 50-72.8) with 74.9% (95% CI 69-80.2) specificity and predictive accuracy was 72% (95% CI 67-77). The positive predictive value of graft survival was 82.1% and 43.5% for the ANNs and Cox regression-based nomogram, respectively, and the negative predictive value was 82% and 86.3% for the ANNs and Cox regression-based nomogram, respectively. Predictions by both models fitted well with the observed findings.
These results suggest that ANNs was more accurate and sensitive than Cox regression-based nomogram in predicting 5-year graft survival.
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