Article
Gastroenterology & Hepatology
Yinhu Li, Yijing Chen, Yingying Fan, Yuewen Chen, Yu Chen
Summary: The close relationship between the gut microbiota (GM) and the central nervous system suggests potential strategies for treating neurological diseases. However, due to the complexity of the gut microecosystem, there is currently no theoretical framework for selecting the optimal window period and bacteria for GM interventions. In this study, a complex network-based modeling approach was used to evaluate the topological features of the GM and determine the optimal window period and bacterial candidates for interventions in Alzheimer's disease (AD). The results identified the third month after birth as the optimal window period for GM interventions and suggested specific hub bacteria as potential candidates for interventions.
Article
Biochemistry & Molecular Biology
Shahzaib Ahamad, Kanipakam Hema, Vijay Kumar, Dinesh Gupta
Summary: The study revealed that the R142Q mutation on TTBK1 leads to structural instability, disrupting its biological functions, and could be used as future diagnostic markers in treating Alzheimer's disease.
JOURNAL OF CELLULAR BIOCHEMISTRY
(2021)
Article
Clinical Neurology
Karen C. C. Holdridge, Roy Yaari, Deirdre B. B. Hoban, Scott Andersen, John R. R. Sims
Summary: This meta-analysis evaluated the effect of low-dose solanezumab on clinical progression in Alzheimer's disease (AD) with mild dementia. The results showed that low-dose solanezumab can slow down the clinical progression of AD.
ALZHEIMERS & DEMENTIA
(2023)
Article
Clinical Neurology
Kellen K. Petersen, Richard B. Lipton, Ellen Grober, Christos Davatzikos, Reisa A. Sperling, Ali Ezzati
Summary: This study developed and tested a risk score system called Positive A beta Risk Score (PARS) for predicting beta-amyloid (A beta) positivity in cognitively unimpaired individuals. The PARS models showed moderate accuracy in predicting A beta positivity, and may be used to identify individuals at risk for early intervention and treatment.
Article
Agriculture, Multidisciplinary
Binbin Wang, Lei Zhang, Yuan Yuan, Zhiqi Zhao, Haijiao Nan
Summary: This paper proposes a novel arrangement method for shiitake mushrooms by establishing a dynamic model and using computational fluid dynamics to analyze their motion states, improving processing efficiency, and optimizing the device for stable arrangement with the stipe pointing upward.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Clinical Neurology
Gustavo Patow, Leon Stefanovski, Petra Ritter, Gustavo Deco, Xenia Kobeleva
Summary: This study investigates the impact of Aβ and tau on neuronal activity in Alzheimer's disease and reveals their different importance at different stages of the disease. The findings provide new insights for further research on biomarkers and potential therapeutic targets.
ALZHEIMERS RESEARCH & THERAPY
(2023)
Article
Biochemistry & Molecular Biology
Francesco Tavanti, Alfonso Pedone, Maria Cristina Menziani
Summary: This study demonstrated through simulations how gold nanoparticles adsorb amyloid-beta monomers, reducing their tendency to form mature amyloid fibrils.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Mechanics
S. Hadi Seyedi, Ali Akhavan-Safaei, Mohsen Zayernouri
Summary: This article proposes a new subgrid-scale model for large eddy simulation of scalar turbulence, addressing the challenges faced by conventional models. Experimental evidence and numerical tests demonstrate the superiority of this new model.
Article
Immunology
Yanaika. S. S. Hok-A-Hin, Marta del Campo, Walter. A. A. Boiten, Erik Stoops, Melanie Vanhooren, Afina. W. W. Lemstra, Wiesje. M. M. van der Flier, Charlotte. E. E. Teunissen
Summary: The proteins MIF and sTREM1 show different levels of expression in different stages of Alzheimer's disease, and they are associated with tau pathology and inflammation. These neuroinflammatory markers could be useful in clinical trials to monitor the dynamics of inflammatory responses or the efficacy of inflammatory modulating drugs.
JOURNAL OF NEUROINFLAMMATION
(2023)
Article
Construction & Building Technology
M. F. Khaled, A. M. Aly, A. Elshaer
Summary: In computational wind engineering, Large Eddy Simulation (LES) offers higher accuracy but comes with increased computational cost compared to Reynolds Averaged Navier-Stokes (RANS) models. Strategies proposed in the study include turbulence synthesis for efficient inflow generation, interpolation concept to reduce tedious computations, and investigation of larger time-step and coarser mesh impact on LES performance. Results suggest that using the wall-adapting eddy viscosity (WALE) SGS model with LES can reduce computational time within an acceptable error range.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Kamila Sofinska, Piotr Batys, Adrian Cernescu, Dhiman Ghosh, Katarzyna Skirlinska-Nosek, Jakub Barbasz, Sara Seweryn, Natalia Wilkosz, Roland Riek, Marek Szymonski, Ewelina Lipiec
Summary: Through experimental and theoretical studies, we have revealed the influence of the anti-aggregation drug bexarotene on the secondary structure and primary aggregation of amyloid-beta aggregates. We observed that bexarotene limits the formation of parallel beta-sheets in the aggregates and slows down the aggregation process. Molecular dynamics simulations also provided insights into the interaction mechanism between bexarotene and the protein.
Editorial Material
History & Philosophy Of Science
Simon Scheller, Christoph Merdes, Stephan Hartmann
Summary: This article discusses the central role of computational modeling in philosophy, pointing out that models in philosophy are usually simpler compared to models in the sciences, and philosophers can contribute to the development of computational modeling by building their own models and thinking about the applications of this method in philosophy and science.
Article
Geriatrics & Gerontology
Andrew R. Bender, Arkaprabha Ganguli, Melinda Meiring, Benjamin M. Hampstead, Charles C. Driver
Summary: This study aimed to evaluate the differences in practice effects (PE) and verbal recall in a clinically characterized aging cohort, demonstrating that the pattern of PE changes is largely unaffected by age, sex, or educational attainment in individuals with mild cognitive impairment (MCI) and dementia.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Biochemistry & Molecular Biology
Nilesh Kumar, Bharat K. Mishra, Jinbao Liu, Binoop Mohan, Doni Thingujam, Karolina M. Pajerowska-Mukhtar, M. Shahid Mukhtar
Summary: Drought is a serious stressor in the environment that reduces plant growth and agricultural productivity. To understand its effects on plants, a systems biology approach was used to study the transcriptome of Arabidopsis during drought. Co-expression network analysis identified 117 transcription factors (TFs) with important properties. Mathematical modeling and simulations revealed major transcriptional events and the involvement of TFs. Experimental evidence validated the predictions, showcasing the potential of these TFs in crop engineering programs.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Nikolas Dovrolis, Maria Nikou, Alexandra Gkrouzoudi, Nikolaos Dimitriadis, Ioanna Maroulakou
Summary: Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by memory loss and cognitive decline. Recent research has shown that the mechanisms involved in AD are complex and diverse. This study presents a computational approach to analyze genes and biological processes specific to brain regions in AD. The identified genes participate in various biological processes and provide new insights into the molecular background of AD.
Article
Multidisciplinary Sciences
Qasim Khan, Edda Kalbus, Nazar Zaki, Mohamed Mostafa Mohamed
Summary: This study investigates the reliability of flood-related data collected from social media, particularly in arid regions. The results show that social media data can be a reliable alternative when real-time flow gauge data is unavailable.
Review
Acoustics
Hanan Aldarmaki, Asad Ullah, Sreepratha Ram, Nazar Zaki
Summary: This paper reviews the research literature to examine the challenges and potential solutions for achieving fully unsupervised ASR, with the aim of optimizing ASR development for low-resource languages.
SPEECH COMMUNICATION
(2022)
Article
Immunology
Fatmah Al Zahmi, Tetiana Habuza, Rasha Awawdeh, Hossam Elshekhali, Martin Lee, Nassim Salamin, Ruhina Sajid, Dhanya Kiran, Sanjay Nihalani, Darya Smetanina, Tatsiana Talako, Klaus Neidl-Van Gorkom, Nazar Zaki, Tom Loney, Yauhen Statsenko
Summary: This study conducted a retrospective chart review of COVID-19 patients in the multi-national society of the UAE and found variations in disease severity among different ethnic groups. The researchers also built a classification model based on clinical and laboratory findings to predict ethnic specificity of the disease.
FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Mubashir Hassan, Muhammad Yasir, Saba Shahzadi, Andrzej Kloczkowski
Summary: In this study, computational drug repositioning approaches were used to identify potential therapeutic drugs for Ewing sarcoma. Through molecular docking and pharmacogenomics analysis, astemizole, sulfinpyrazone, and pranlukast were found to exhibit comparable effects to pazopanib and may be used as potential therapeutic agents for Ewing sarcoma treatment.
Article
Multidisciplinary Sciences
Wasif Khan, Nazar Zaki, Mohammad M. Masud, Amir Ahmad, Luqman Ali, Nasloon Ali, Luai A. Ahmed
Summary: Accurate prediction of newborn birth weight is crucial for evaluating their health and safety. This study provides a detailed approach for weight estimation and low birth weight classification. Multiple subsets of features and feature selection techniques were used, along with synthetic minority oversampling, to improve classification performance. The results demonstrate that the Random Forest algorithm performs best for weight estimation, while Logistic Regression with SMOTE oversampling achieves the best performance in low birth weight classification.
SCIENTIFIC REPORTS
(2022)
Article
Health Care Sciences & Services
Mariam Almeqbaali, Sofia Ouhbi, Mohamed Adel Serhani, Leena Amiri, Reem K. Jan, Nazar Zaki, Ayman Sharaf, Abdulla Al Helali, Eisa Almheiri
Summary: This study aimed to develop and evaluate a biofeedback-based app for young adults with anxiety in the UAE. The app includes serious games, breathing exercises, positive messaging, and other features. Through surveys, analysis, and interviews, the app's requirements were determined and it was co-designed with mental health professionals. The results showed that the app is efficient and easy to use.
JMIR SERIOUS GAMES
(2022)
Article
Medicine, General & Internal
Yauhen Statsenko, Tetiana Habuza, Tatsiana Talako, Mikalai Pazniak, Elena Likhorad, Aleh Pazniak, Pavel Beliakouski, Juri G. Gelovani, Klaus Neidl-Van Gorkom, Taleb M. Almansoori, Fatmah Al Zahmi, Dana Sharif Qandil, Nazar Zaki, Sanaa Elyassami, Anna Ponomareva, Tom Loney, Nerissa Naidoo, Guido Hein Huib Mannaerts, Jamal Al Koteesh, Milos R. Ljubisavljevic, Karuna M. Das
Summary: The study developed an automatic assessment method for lung impairment in COVID-19 associated pneumonia using machine learning algorithms. The models based on multi-plane 2D images showed slightly better performance than single-projection images, and the models trained on 3D images were more accurate than those on 2D, although the differences were not statistically significant.
FRONTIERS IN MEDICINE
(2022)
Correction
Education & Educational Research
Balqis Albreiki, Tetiana Habuza, Nazar Zaki
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
(2022)
Article
Environmental Sciences
Wasif Khan, Nazar Zaki, Amir Ahmad, Jiang Bian, Luqman Ali, Mohammad Mehedy Masud, Nadirah Ghenimi, Luai A. Ahmed
Summary: Low birth weight (LBW) infants are a serious global public health concern. Predicting infant weight before birth can help identify risk factors and reduce morbidity and mortality. Existing machine learning algorithms for LBW prediction have shown good performance but need improvement for use in real-world clinical settings. To address this, we propose transforming tabular data into a knowledge graph to capture structural information and improve classification performance. Our method achieved the best performance on a real-life dataset, with a 6% improvement in the area under the curve compared to using the original risk factors. We also highlight the clinical relevance of our proposed model for its adaptation in clinical settings.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2023)
Article
Computer Science, Information Systems
Songhui Diao, Weiren Luo, Jiaxin Hou, Ricardo Lambo, Hamas A. AL-kuhali, Hanqing Zhao, Yinli Tian, Yaoqin Xie, Nazar Zaki, Wenjian Qin
Summary: In this paper, a novel deep multi-magnification similarity learning (DSML) approach is proposed, which can help interpret the multi-magnification learning framework and visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), overcoming the difficulty of understanding cross-magnification information propagation.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Green & Sustainable Science & Technology
Jaloliddin Rustamov, Zahiriddin Rustamov, Nazar Zaki
Summary: Green space is a green infrastructure consisting of vegetation and is associated with various benefits. Past studies have either neglected the human perception or have been challenging to apply. This research proposes a machine learning methodology using transfer learning on pre-trained models to assess green space quality based on human perception.
Article
Education & Educational Research
Balqis Albreiki, Tetiana Habuza, Nazar Zaki
Summary: Technological advances have led to the emergence of online learning platforms, but none of them accurately predict students' academic performance and commitment. This paper proposes a machine learning method that converts tabulated data into graphs and uses graph topological features to enhance data analysis. The results show that incorporating graph embedding features improves the prediction accuracy for identifying at-risk students.
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
(2023)
Article
Education & Educational Research
Nazar Zaki, Sherzod Turaev, Khaled Shuaib, Anusuya Krishnan, Elfadil Mohamed
Summary: This paper presents a system based on artificial intelligence that automates and validates the mapping process between course learning outcomes (CLOs) and program learning outcomes (PLO). The system uses natural language processing to automate the mapping process and has shown promising results in testing. A web-based tool has also been created to assist teachers and administrators in performing automatic mappings.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Review
Computer Science, Information Systems
Sherzod Turaev, Saja Al-Dabet, Aiswarya Babu, Zahiriddin Rustamov, Jaloliddin Rustamov, Nazar Zaki, Mohd Saberi Mohamad, Chu Kiong Loo
Summary: Body language is a nonverbal form of communication that includes movements, postures, gestures, and expressions of the body. It expresses human feelings, thoughts, and intentions, and also reveals physical and psychological health conditions. The importance of studying the body language of people with health conditions can be seen through various reports in literature.
Article
Multidisciplinary Sciences
Wasif Khan, Nazar Zaki, Amir Ahmad, Mohammad M. Masud, Romana Govender, Natalia Rojas-Perilla, Luqman Ali, Nadirah Ghenimi, Luai A. Ahmed
Summary: This study aims to address two important limitations encountered by traditional machine learning models in predicting adverse pregnancy outcomes, and improve prediction performance by using node embedding and graph outlier detection algorithms.
SCIENTIFIC REPORTS
(2023)
