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Emotion Recognition from Electroencephalogram Signals based on Deep Neural Networks
Get PDFEmotion recognition using deep learning methods through electroencephalogram (EEG) analysis has marked significant progress. Nevertheless, the complexities and time-intensive nature of EEG analysis present challenges. This study proposes an efficient EEG analysis method that foregoes feature extraction and sliding windows, instead employing one-dimensional Neural Networks for emotion classification. The analysis utilizes EEG signals from the Database for Emotion Analysis using Physiological Signals (DEAP) and focuses on thirteen EEG electrode positions closely associated with emotion changes. Three distinct Neural Models are explored for emotion classification: two Convolutional Neural Networks (CNN) and a combined approach using Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM). Additionally, two emotion labels are considered: four emotional ranges encompassing low arousal and low valence (LALV), low arousal and high valence (LAHV), high arousal and high valence (HAHV), and high arousal and low valence (HALV); and high valence (HV) and low valence (LV). Results demonstrate CNN_1 achieving an average accuracy of 97.7% for classifying four emotional ranges, CNN_2 with 97.1%, and CNN-LSTM reaching an impressive 99.5%. Notably, in classifying HV and LV labels, our methods attained remarkable accuracies of 100%, 98.8%, and 99.7% for CNN_1, CNN_2, and CNN-LSTM, respectively. The performance of our models surpasses that of previously reported studies, showcasing their potential as highly effective classifiers for emotion recognition using EEG signals
Revolutionizing Endovascular Treatment: The Transformative Role of Artificial Intelligence in Healthcare
Get PDFArtificial Intelligence (AI) has emerged as a revolutionary force in various industries, transforming processes and enhancing outcomes through its advanced capabilities. In the realm of healthcare, AI is making significant strides, particularly in the field of endovascular treatment, a minimally invasive medical procedure conducted within blood vessels. This editorial explores the multifaceted applications of AI in endovascular treatment, shedding light on its pivotal role in improving patient care and procedural efficiency
A Update on Early Detection and Management of Diabetes: Comparison of Fasting Serum Glucagon among Diabetics and Non-Diabetics
No full textBackground: Diabetes is a well- known disease that is spreading globally and causing many other complications like nephropathy, eye disorders, foot disease and other heart related diseases. It also has a high mortality rate. Glucagon is the principal hyperglycemic hormone, and acts as a counterbalancing hormone to insulin. Its level increases in early stages of diabetes, however with the progression of disease it gradually decreases in diabetic patients.
Aim: The aim of this study was to compare the glucagon levels in diabetics and non-diabetics.
Materials and Methods: This was a cross-sectional study in which the data and blood samples of diabetics and non-diabetics with or without family history of diabetes were collected during December 2022 to May 2023. Then by SDS Page analysis glucagon levels were estimated in the blood samples of participants and results were analyzed by SPSS statistical version 25.0.
Results: In Group A, 25 (83.33%) had high levels of fasting serum glucagon while in group B only 18 (60%) had raised serum glucagon levels with the p value of 0.001. High glucagon level was observed in non-diabetics with family history of diabetes, as maximum number of participants 60% (n=18) with high level glucagon fall in this group. The p value is 0.001 shows the association between glucagon and family history of nondiabetic is significant.
Conclusion: Fasting serum glucagon could be an early sign of insulin resistance in non-diabetics and diabetics. Therefore, in clinical management of glucagon level must be accounted and managed accordingly
Sternalis Muscle: A Case Report and Literature Review
Get PDFIntroduction and Objective: The sternal muscle is a rare anatomical variation found in the anterior chest wall. This study's objective was to make a narrative review of the anatomical and epidemiological aspects of the sternal muscle, as well as to present its finding in one of this group’s dissections.
Materials and Methods: This study consisted of a literature review using Pubmed and LILACS platforms. Articles were analyzed regarding incidence, laterality, action, innervation, and vascularization of the sternal muscle. We also present a case report based on a cadaveric dissection of the Hospital das Clínicas, Faculty of Medicine, University of São Paulo (HCFMUSP).
Results: Our review included 22 articles. The anatomical variation incidence ranged from 1.96% to 5.55%, with a higher predominance of a bilateral presentation. The action of the sternal muscle is predominantly accessory and there are divergences in the literature regarding its innervation and vascularization. Our case reports the finding of a unilateral sternal muscle in the left paramedian line inserted in both sternocleidomastoid muscles, in a female patient.
Conclusion: The sternal muscle has a small incidence in the population and it is usually bilateral. The anatomical knowledge of this muscle is important to prevent it from being confused with other structures commonly found in the chest and cervical region.
Significance/Implication: The importance of sternal muscle importance cannot be overlooked due to possible misinterpretations in imaging and its possible influence on mastectomies. Wider case series are necessary for a better definition of its irrigation and innervation
A Case Series Cadaveric Study on Acquired and Congenital Azygos Venous System Variations
Get PDFThe azygos venous system holds essential clinical relevance as it can provide collateral circulation in cases where the superior and inferior vena cava become obstructed. Additionally, it is important in imaging and mediastinal procedures because variations in this system may be confused with pathology. In this study, the azygos system of 31 embalmed anatomy donors was dissected, analyzed, and classified according to the Anson McVay system and Dahran and Saomes subclassification. Out of 31 donors, one (3.22%) donor was classified as Type I, 27 (87.09%) were Type II, two (6.45%) were Type III, and one (3.22%) was unobservable. These values closely replicated values previously reported in literature; however, four subjects exhibited variations that are rare or not previously reported in literature. In this paper, we describe those rare cases and consider their development and clinical relevance
Striving for Excellence in Diabetes Management Research and Practice
Get PDFThe research articles featured in this issue represent the pioneering efforts of scientists, clinicians, and researchers who strive to unravel the intricacies of diabetes. These studies shed light on novel therapeutic approaches, elucidate the underlying mechanisms of the disease, and explore the impact of lifestyle interventions on diabetes management. Through rigorous study design, meticulous data analysis, and robust conclusions, these research contributions form the foundation for evidence-based practice and inform clinical decision-making
Evaluation of the Effect of Zinc, Quercetin, Bromelain and Vitamin C on COVID-19 Patients
No full textCoronavirus disease 2019 (COVID-19) is an infectious disease caused by a new strain of coronavirus. There are three phases of COVID-19: early infection stage, pulmonary stage and hyper-inflammation stage respectively. It is important to prevent lung or other organs injuries by preventing phase-II and phase-III via pharmacological or non-pharmacological treatments. This was a case series study done on twenty-two patients confirmed to be infected with SARS-CoV-2 and diagnosed with COVID-19. Patients in this study have been used quercetin 800 mg, bromelain 165 mg, zinc acetate 50 mg and ascorbic acid 1 g once daily as supplements for 3 to 5 days during SARS-CoV-2 infection. The aim of this study is to evaluate the safety and efficacy of quercetin, bromelain, zinc and ascorbic acid combination supplements on patients with COVID-19. The mean levels of WBC, ANC, ALC, AMC and AST were normal among all included patients before and after taking quercetin, bromelain, zinc and ascorbic acid supplements (P-value >0.05). Quercetin 800 mg, bromelain 165 mg, zinc acetate 50 mg and ascorbic acid 1 g once daily supplements were safe for patients infected with SARS-CoV-2 and may prevent poor prognosis. Randomized clinical trials needed in the future to ensure the efficacy of quercetin, bromelain, zinc and vitamin C combination. 
Establishing an Online Writing Center for Health Professions Education
Get PDFPurpose: This study aimed to identify the commonly-used modules of Online Writing Centers worldwide to establish the first Online Writing Center in the context of health professions education in Iran.
Design/methodology/approach: This observational study was conducted during 2019-2021. In the initial round of search, we identified 61 eligible OWCs whose websites comprised 14 common modules. Then, we searched the top universities according to 2021 Times Higher Education World University Ranking. We probed the modules of the writing centers of the included universities by using a Google form. We identified the modules repeatedly appearing on the websites of the writing centers, and then reviewed the content of each module in order to find a common label which could later be used for constructing our online writing center.
Findings: Our final search yielded 26 universities with 22 common modules. The researchers came to a consensus about the modules to be included and the labels assigned to them. Finally, a website was created, and the modules were included.
Value of paper: Online Writing Centers scaffold students by giving feedback on word choice errors and assist them in producing grammatically accurate texts. The Online Writing Centers being designed based on the findings of this study can assist many researchers who intend to publish their scientific findings in English
Identification of Selected Kinetoplastids 18S rRNA Residues required for Efficient Recruitment of Initiator tRNA Met and AUG Selection in silico
Get PDFHigh Resolution 18S rRNA structures of kinetoplastids ribosomes from theoretical methods have provided atomic level details about the process of translation. This process entails detailed information on the mRNA and tRNA binding and decoding centers within the 18S rRNA that was previously not very well understood. We identified residues in selected kinetoplastids 18S rRNA critical in recruiting the first methionyl tRNA to the small ribosome subunit during initiation and comparing them to see the differences. The Kozak sequence presence on eukaryotic mRNAs tethers it to the AUG start codon. Kinetoplastids are a closely related group, and the three chosen exhibited differences in the A-site in terms of position and nucleotides found there. Interactions are found at the A-site (543-UUU-546 for T. cruzi, 560-CCUA-563 for T. brucei, and 540-UUUG-543 for Leishmania major), where the different mRNA get complementary sequences at the 16th helix. The current findings show that each messenger RNA has a sequence that is complementary to the appropriate 18S rRNA sequence, tethering the mRNA to the small ribosomal subunit, which then recruits the bigger subunit. When compared to the Kozak region that flanks the AUG start codon, this method effectively promotes start codon placement
ProCbA: Protein Function Prediction based on Clique Analysis
Get PDFProtein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for unannotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods. To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which does not include a new algorithm; we attempt to solve the essence of incomplete and noisy data of the PPI era in order to achieve a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis demonstrates that both ProCbA and ProNC-FA are generally able to outperform all the other methods