HEPATOPROTECTIVE ACTIVITY OF COSTUS SPECIOSUS (KOEN. EX. RETZ.) AGAINST PARACETAMOL-INDUCED LIVER INJURY IN MICE

Background: Liver diseases are a common cause of mortality and morbidity over the world. It is caused mainly by toxic chemicals and chemotherapeutic agents. Costus speciosus (Koen ex. Retz.) (Zingiberaceae) is widely employed in various traditional medicines for the prevention and treatment of different aliments. The purpose of this study is to assess the protective effect of C. speciosus rhizomes MeOH extract against the injury of the liver induced by paracetamol (PA) in mice. Material and Methods: The mice were pretreated for seven days with distilled H2O, silymarin 12 mg/kg or 100 and 200 mg/kg MeOH extract. Then, PA (750 mg/kg) was also intra-peritoneal administrated once a day. Animals were euthanatized 24 h after the damage inducement. The levels of the serum enzymes aspartate aminotransferase (AST), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase, in addition to the tumor necrosis factor-alpha (TNF-α), were determined. Moreover, the histopathological examination was carried out. Results: Administration of the MeOH extract (200 mg/kg) showed improvement in the toxic effects of PA through significant fall on the serum markers enzymes of liver damage: AST, ALT, and ALP, as well as TNF-α, compared to silymarin. In parallel, the histopathological profile in the mice` liver also proved that extract markedly minimized the PA toxicity and maintained the liver tissues` histoarchitecture to near the normal ones more than that achieved by silymarin. Conclusion: The findings suggested that C. speciosus extract acts as a potential hepatoprotective agent against PA-induced liver toxicity. This hepato-protection effect may be due to the existence of steroids, saponins, different glycosides, and phenolic compounds in C. speciosus

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Pneumonia Transfer Learning Deep Learning Model from Segmented X-rays

Pneumonia is a common disease that occurs in many countries, more specifically, in poor countries. This disease is an obstructive pneumonia which has the same impression on pulmonary radiographs as other pulmonary diseases, which makes it hard to distinguish even for medical radiologists. Lately, image processing and deep learning models are established to rapidly and precisely diagnose pneumonia disease. In this research, we have predicted pneumonia diseases dependably from the X-ray images, employing image segmentation and machine learning models. A public labelled database is utilized with 4000 pneumonia disease X-rays and 4000 healthy X-rays. ImgNet and SqueezeNet are utilized for transfer learning from their previous computed weights. The proposed deep learning models are trained for classifying pneumonia and non-pneumonia cases. The following processes are presented in this paper: X-ray segmentation utilizing BoxENet architecture, X-ray classification utilizing the segmented chest images. We propose the improved BoxENet model by incorporating transfer learning from both ImgNet and SqueezeNet using a majority fusion model. Performance metrics such as accuracy, specificity, sensitivity and Dice are evaluated. The proposed Improved BoxENet model outperforms the other models in binary and multi-classification models. Additionally, the Improved BoxENet has higher speed compared to other models in both training and classification

Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset

An Integrated Classification and Association Rule Technique for Early-Stage Diabetes Risk Prediction

The number of diabetic patients is increasing yearly worldwide, requiring the need for a quick intervention to help these people. Mortality rates are higher for diabetic patients with other serious health complications. Thus, early prediction for such diseases positively impacts healthcare quality and can prevent serious health complications later. This paper constructs an efficient prediction system for predicting diabetes in its early stage. The proposed system starts with a Local Outlier Factor (LOF)-based outlier detection technique to detect outlier data. A Balanced Bagging Classifier (BBC) technique is used to balance data distribution. Finally, integration between association rules and classification algorithms is used to develop a prediction model based on real data. Four classification algorithms were utilized in addition to an a priori algorithm that discovered relationships between various factors. The named algorithms are Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machines (SVM), and K Nearest Neighbor (KNN) for data classification. Results revealed that KNN provided the highest accuracy of 97.36% compared to the other applied algorithms. An a priori algorithm extracted association rules based on the Lift matrix. Four association rules from 12 attributes with the highest correlation and information gain scores relative to the class attribute were produced

Effect of Muslim Prayer (Salat) positions on the intra-ocular pressure in healthy young individuals

Purpose: There is a lack of research examining the effects of Muslim prayer (Salat) positions on the intra-ocular pressure (IOP). Considering its involvement with postural changes, this study aimed to investigate the changes in the IOP upon assuming Salat positions before, immediately after, and after 2 minutes of prayer in healthy young adults. Methods: This prospective, observational study recruited healthy young individuals aged between 18 and 30 years. The IOP measurements were obtained in one eye using Auto Kerato-Refracto-Tonometer TRK-1P, Topcon at baseline before assuming prayer positions, immediately after, and after 2 minutes of the prayer. Results: Forty female participants were recruited, with a mean age of 21 ± 2.9 years, a mean weight of 59.7 ± 14.8 (kg), and a mean body mass index (BMI) of 23.8 ± 5.7 (kg/m2). Only 16% had a BMI ≥25 kg/m2 (n = 15). All participants started with a mean IOP at baseline of 19.35 ± 1.65 mmHg, which increased to 20 ± 2.38 mmHg and declined to 19.85 ± 2.67 mmHg after 2 minutes of Salat. The difference between the mean IOPs at baseline, immediately after, and after 2 minutes of Salat was not significant (p = 0.06). However, there was a significant difference between the baseline IOP measurements and those immediately after Salat (p = 0.02). Conclusion: A significant difference was found between the IOP measurements at baseline and immediately after Salat; however, this was not clinically significant. Further investigation is warranted to confirm these findings and explore the effect of a longer duration of Salat in glaucoma and glaucoma suspect patients