Search

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain’s function. EEG is an effective instrument for deciphering the brain’s complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader’s level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.

Stress is one of the main factors that might lead to depression in the long term; medical students are susceptible to stress and depression more than other students. Aim: to look at the prevalence of depression among medical students. Methodology: The authors looked at published studies on the internet about the depression rates among medical students in different areas around the world. Results: Most of the literature showed that there is a significant prevalence of depression among students during medical school. Conclusion: Due to high rates of depression among medical students, coping strategies for stress and depression should be offered to medical students to prevent future complications.

Background Hypertension is one of the leading global risk factors for cardiovascular disease-related morbidity and mortality. Females have historically been underrepresented in clinical trials resulting in presumed sex-related disparities in antihypertensive treatment effects. The past decade, widespread attention has been paid to this shortcoming aiming at increasing females' representation in clinical trials. Purpose To investigate whether in studies investigating the effect of antihypertensive drugs 1) the representation of females and 2) presentation of sex-stratified data has increased over the past decades. Methods We performed a scoping review after systematically searching PubMed and Embase for studies evaluating the effects of the five major groups of antihypertensive medication from inception (1945) until May 2020. The review was registered in Prospero database. Studies were only included if they 1) investigated one class of the five main groups of antihypertensive medications (beta-blockers, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, and diuretics, 2) human studies, 3) investigated adults ≥18 years of age, 4) were written in English or Dutch. We excluded articles if 1) only abstract was available and full report was not found, 2) unsuitable study design, 3) no reference group included, 4) outcome not related to cardiovascular health, 5) no registration of specific dose and duration information. The primary outcome was the proportion of included females over time, stratified per decade. The secondary outcome was whether sex-stratification was reported. Linear regression analysis with beta coefficient (β) and 95% CI was performed to explore the associations between the percentage of females included in the studies over time. Results The search strategy resulted in 73,867 potential articles. After study selection based on title/abstract and full text, 2,079 original studies were eligible for our study. These included 1,395,264 adults of which the mean percentage of females participating in all included studies was 27.9% (Figure 1, Table 1). The percentage of females participating in antihypertensive studies showed a slight increase each year by 0.4% (95% CI 0.36–0.53, P Conclusion Despite yearly increase in female participation in antihypertensive studies, females still only account for only one third of the study population. Moreover, less than 10% of studies report sex stratified data. Considering the global burden of hypertension, more differentiated sex-specific attention remains critically needed. Funding Acknowledgement Type of funding sources: None.

The enhancing effect of extraframework Al (EFAl) species on the acidity of bridging hydroxyl groups in a steam-calcined faujasite zeolite (ultrastabilized Y, USY) was investigated by in situ monitoring the H/D exchange reaction between benzene and deuterated zeolites by 1H MAS NMR spectroscopy. This exchange reaction involves Brønsted acid sites (BAS) located in sodalite cages and supercages. In a reference faujasite zeolite free from EFAl, both populations of BAS are equally and relatively slowly reactive toward C6H6. In USY, in stark contrast, the H/D exchange of sodalite hydroxyl groups is significantly faster than that of hydroxyl groups located in the faujasite supercages, even though benzene has only access to the supercages. This evidences selective enhancement of BAS near Lewis acidic EFAl species, which according to the NMR findings are located in the faujasite sodalite cages.

There are very few studies in Saudi Arabia on stigmatization and discrimination against people living with HIV/AIDS (PLWHA), a critical step for the prevention and control of HIV. The aim of this study was to assess the level of stigmatization and discrimination against PLWHA by healthcare workers (HCWs) as well as their knowledge, attitude, and practices (KAP).This cross-sectional study included 182 HCWs at primary healthcare centers in Medina, Saudi Arabia. The validated short version of the Healthcare Provider HIV/AIDS Stigma Scale and the AIDS Attitude Scale were used in this research. Student'sMost participants were males (58.2%) and aged more than 30 years (60.4%). The tendency to stigmatizing behavior was present in 24.2%-68.17% of the participants, and discriminatory practice was present in 11.5%-50% of the participants. In multiple linear regression analysis, factors that independently predicted the knowledge score were being a doctor compared to nurses (This study found that stigmatization and discrimination were less prevalent in HCWs who had good HIV-related knowledge and had received in-service training for PLWHA. The results highlight the significance of continuing education and training opportunities for HCWs to provide effective and appropriate treatment to PLWHA.

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

English has become a global language in modern day world. It is widely spoken among the non-native English communities as well. However, due to the lack of resources, such as, teaching literature and skilled English teachers, the teaching of English language becomes inefficient. Educational institutions and non-native English students are equally affected by these problems. In order to efficiently cope with the aforementioned issues, we propose an AI-based English learning framework. The proposed AI-based framework combines the previously proposed algorithms and other newly developed techniques to enhance the language learning. The proposed framework enables the individual learners to interact with the framework for improving and enhancing the process of learning English language. In addition, the proposed system has the capability to effectively cope with the lack of resources available to the educational institutes providing English learning courses. In addition, we also perform data collection and develop new modules that are integrated in the proposed framework. We assess the proposed framework by allowing a group of participants to learn English language. These participants are non-native speakers from a foreign land. The results make it evident, that the proposed framework assists the students in learning English as a foreign language.

Ischemic and hemorrhagic stroke are the common types of stroke that lead to brain injury neurological deficits and mortality. All forms of stroke remain a serious health issue, and there is little successful development of drugs for treating stroke. Incomplete understanding of stroke pathophysiology is considered the main barrier that limits this research progress. Besides mitochondria and free radical-producing enzymes, labile iron is an important contributor to oxidative stress. Although iron regulation and metabolism in cerebral stroke are not fully understood, much progress has been achieved in recent years. For example, hepcidin has recently been recognized as the principal regulator of systemic iron homeostasis and a bridge between inflammation and iron regulation. This review discusses recent research progress in iron pathophysiology following cerebral stroke, focusing molecular regulation of iron metabolism and potential treatment targets.

C10H13ClN2O4, orthorhombic, Pnma, a = 14.903(2) Å, b = 9.1004(15) Å, c = 8.7560(14) Å, V = 1187.5(3) Å3, Z = 4, Rint = 0.036, R gt(F) = 0.0284, wRref (F2 ) = 0.0795, T = 296 K.

A series of faujasite zeolites was modified by extraframework Al (AlEF) with the goal to investigate the influence of such species on the intrinsic Brønsted acidity and catalytic activity towards paraffin cracking. The chemical state of AlEF and zeolite acidity were investigated by 27Al MAS NMR and COads IR spectroscopy, H/D exchange reaction, and propane cracking. Strongly acidic defect-free Y zeolites were obtained by substitution of framework Al by Si with (NH4)2SiF6. In accordance with the next-nearest-neighbor model, the intrinsic acidity of the protons increased with decreasing framework Al density. This increased acidity was evidenced by an increased shift of the OH stretching vibration upon CO adsorption in COads IR spectroscopy and by an increased H/D exchange rate in H/D exchange reactions with perdeuterobenzene. All of the acid sites in these zeolites were of equal strength beyond a certain Si/Al ratio. The increased acidity resulted in an enhanced propane cracking activity. Modification of a model dealuminated Y zeolite by AlEF only resulted in a small fraction of cationic AlEF species, because it was difficult to control the ion exchange process. In comparison, commercial ultrastabilized Y zeolites contained less AlEF and these species were predominantly present in cationic form. The rate of propane cracking strongly correlated to the concentration of Brønsted acid sites perturbed by cationic AlEF species. The results of MQMAS 27Al NMR spectroscopy confirmed the presence of sites perturbed by AlEF and unaffected framework Al sites. Zeolites with higher intrinsic cracking activities contained a higher proportion of perturbed sites. Although COads IR and H/D exchange methods proved to be suitable methods to probe the acidity of Y zeolites free from AlEF, they were less suitable to predict the reactivity if the Brønsted acid sites were affected by cationic AlEF species.

Backgrounds and Study Aims. Common bile duct (CBD) injury is one of the most serious complications of laparoscopic cholecystectomy (LC). Misidentification of the CBD during dissection of the Calot's triangle can lead to such injuries. The aim of the authors in this study is to present a new safe triangle of dissection. Patients and Method. 501 patients under went LC in the following approach; The cystic artery is identified and mobilized from the gall bladder (GB) medial wall down towards the cystic duct which would simultaneously divide the medial GB peritoneal attachment. This is then followed by dividing the lateral peritoneal attachment. The GB will be unfolded and the borders of the triangle of safety (TST) are achieved: cystic artery medially, cystic duct laterally and the gallbladder wall superiorly. The floor of the triangle is then divided to delineate both cystic duct and artery in an area relatively far from CBD. Results. There were little significant immediate or delayed complications. The mean operating time was 68 minutes, nearly equivalent to the conventional method. Conclusions. Dissection at TST appears to be a safe procedure which clearly demonstrates the cystic duct and may help to reduce the CBD injuries.