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Identification of molecular signatures and pathways common to blood cells and brain tissue based RNA-Seq datasets of bipolar disorder: Insights from comprehensive bioinformatics approach

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informatics in Medicine Unlocked 2022年 29卷

作者： Islam, A.M. Humyra Rahman, Md Habibur Bristy, Sadia Afrin Andalib, K.M. Salim Khan, Umama Awal, Md Abdul Hossain, Md Shahadat Moni, Mohammad Ali Bioinformatics and Biomedical Research Network of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Electronics and Communication Engineering Discipline Khulna University Khulna 9208 Bangladesh Department of Quantitative Sciences International University of Business Agriculture and Technology Dhaka 1230 Bangladesh Dept. of Computer Science and Engineering Pabna University of Science and Technology Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Bipolar disorder (BD), also referred to as manic depression, is a mental illness marked by significant mood fluctuations as well as changes in sleep, energy, thinking, and behavior. About 1% of the world's population is affected by BD. In the United States, roughly 3% of people are thought to be impacted at some point in their lives;female and male percentages appear to be similar. The most prevalent age for the onset of symptoms is 20 years old;early-onset is connected with a worse prognosis. The primary molecular markers for identification and therapeutic target screening have now become a worldwide concern. We looked at gene expression data from brain tissue (RNA-seq) in this work. Transcriptomic analysis of GSE53239 and GSE81396 was implemented to figure out the DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to perform functional annotation and pathway enrichment analysis of DEGs, respectively. In addition, a protein-protein interaction network was built to find hub proteins (HSPA1A, GNL1, FOSL1, DDX39B, BAG3, ARHGAP11A, MDC1, PRRC2A, VPS52, and DHX16). Subsequently, TFs-DEGs interaction, miRNAs-DEGs interaction, and correlated diseases were also identified. In the brain tissues of BD patients, we discovered possible new linkages between pathogenic processes. Beryllium sulfate exhibited the most negative correlation and ability to reverse BD. As a result, this research provides molecular biomarkers at the RNA and protein levels that may aid in the better understanding of molecular processes, as well as potential pharmacological targets and therapies for the treatment of BD patients. © 2022 The Authors

关键词： Bioinformatics Bipolar disorder Gene ontology Genes Hub protein Pathways

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Sharp bounds on the spectral radius of nonnegative matrices and comparison to the frobenius’ bounds

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International Journal of Circuits, Systems and Signal Processing 2020年 14卷 423-434页

作者： Adam, Maria Assimakis, Nicholas Babouklis, Fotis Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece General Department National and Kapodistrian University of Athens Athens Greece

In this paper, a new upper bound and a new lower bound for the spectral radius of a nοnnegative matrix are proved by using similarity transformations. These bounds depend only on the elements of the nonnegative matrix and its row sums and are compared to the well-established upper and lower Frobenius’ bounds. The proposed bounds are always sharper or equal to the Frobenius’ bounds. The conditions under which the new bounds are sharper than the Frobenius' ones are determined. Illustrative examples are also provided in order to highlight the sharpness of the proposed bounds in comparison with the Frobenius’ bounds. An application to linear invariant discrete-time nonnegative systems is given and the stability of the systems is investigated. The proposed bounds are computed with complexity O(n2). © 2020, North Atlantic University Union. All rights reserved.

关键词： Matrix algebra

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Performance Analysis for Molecular Communication Under Feedback Channel Using Multipath and Single Path Technique

SSRN

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SSRN 2022年

作者： Ningthoujam, Sanjit Chingkheinganba, Tekcham Chakraborty, Swarnendu Kumar Elngar, Ahmed A. Chakrabarti, Prasun Chakrabarti, Tulika Praveen, S. Phani Gupta, Amit Margala, Martin School of Computer Science and Engineering VIT University Andhra Pradesh 522237 India BSNL Dimapur797112 India Dept. of Computer Science and Engineering National Institute of Technology Arunachal Pradesh 791112 India Faculty of Computers and Information Techno India NJR Institute of Technology India Department of Chemistry Sir Padampat Singhania University Rajasthan Udaipur313601 India Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh 520007 India Department of ECE Narasaraopeta Engineering College Andhra Pradesh Narasaraopeta522601 India Computer Science School of Computing and Informatics University of Louisiana United States

Nanomachines that can run tasks and molecular communication (MC) for interconnecting nanomachines have been made possible by advances in nanotechnology. Different techniques of Automatic Repeat Request (ARQ) protocols have been devised in MC for acquiring the necessary molecules, although the retransmission of duplicate copies requires special attention. This work presents a diffusion-based MC with a feedback loop to improve MC performance. Based on the received ACK(s) or NACK(s), the Channel state determines if the state is good (G) or bad (B), and then single path or multipath procedures are used to the ‘G' and ‘B' states. The results of the MATLAB simulation illustrate how the systems compare in terms of communication rate, the possibility of complete transmission of the desired molecules, and energy consumption. When the channel state is bad, the multipath approach performs better, but when the channel state is perfect in MC, the single path strategy performs better. © 2022, The Authors. All rights reserved.

关键词： Molecules

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Deep Learning Predicts Prevalent and Incident Parkinson’s Disease From UK Biobank Fundus Imaging

arXiv

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arXiv 2023年

作者： Tran, Charlie Shen, Kai Liu, Kang Ashok, Akshay Ramirez-Zamora, Adolfo Chen, Jinghua Li, Yulin Fang, Ruogu Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Physics University of Florida GainesvilleFL32611 United States Dept. of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Neurology University of Florida GainesvilleFL32611 United States Department of Ophthalmology University of Florida GainesvilleFL32611 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States J. Crayton Pruitt Family Department. of Biomedical Engineering University of Florida GainesvilleFL32611 United States Center for Cognitive Aging and Memory University of Florida GainesvilleFL32611 United States

Parkinson’s disease is the world’s fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson’s disease and automate diagnostics would greatly improve the treatment of patients with Parkinson’s disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson’s disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson’s disease from UK Biobank fundus imaging. Our results show that Parkinson’s disease individuals can be differentiated from age and gender-matched healthy subjects with an Area Under the Curve (AUC) of 0.77. This accuracy is maintained when predicting either prevalent or incident Parkinson’s disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations. © 2023, CC BY.

关键词： Patient treatment

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Low Dose Myocardial Perfusion SPECT Denoising Using an Attention-Based Generative Adversarial Network

Low Dose Myocardial Perfusion SPECT Denoising Using an Atten...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Jingzhang Sun Chien-Ying Li Yu Du Tung-Hsin Wu Bang-Hung Yang Yi-Hwa Liu Greta S. P. Mok Biomedical Imaging Laboratory (BIG) Dept. of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Department of Nuclear Medicine Taipei Veterans General Hospital Taipei Taiwan Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Taipei Taiwan Department of Internal Medicine Yale University School of Medicine New Haven CT USA

Deep learning-based denoising is promising for low dose (LD) myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods used fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. Attention scheme is able to leverage voxels with stronger correlations beyond the kernel receptive field. In this study, we propose a 3D attention-based conditional generative adversarial network (AttcGAN) for denoising LD MP-SPECT images on both projection- and reconstruction-domain. Forty-six patients underwent ~1184 MBq 99m Tc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired from right anterior oblique to left posterior oblique positions and the acquisition time for each projection was 10 s for the full dose (FD) mode. LD MP-SPECT images (1/10 to 7/10 of FD) were generated from the FD list mode data. cGAN and AttcGAN were implemented using Tensorflow with the Adam optimizer running up to 200 epochs. FD and LD MP-SPECT pairs of 30 patients were used for training the cGAN and AttcGAN separately for each dose level, while 6, and 10 patients were applied for validation and testing. Normalized mean square error (NMSE), normalized standard deviation (NSD), and perfusion defect size (PDS) were analyzed. The results indicate that denoising on the projection-domain outperforms reconstruction-domain for both networks, and the AttcGAN is superior to cGAN on both projection- and reconstruction-domain on all dose levels. We conclude that AttcGAN denoising on projection-domain can further improve the denoising performance for LD MP-SPECT with better defect quantification accuracy.

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Machine Learning Models Predict the Need of Amputation and/or Peripheral Artery Revascularization in Hypertensive Patients Within 7-Years Follow-Up

Machine Learning Models Predict the Need of Amputation and/o...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Konstantina Tsarapatsani Antonis I. Sakellarios Vassilis D. Tsakanikas Hans J. Trampisch Henrik Rudolf Nikolaos Tachos Marcus E. Kleber Winfried März Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Medical Clinic V Mannheim Medical Faculty University of Heidelberg Mannheim Germany Mannheim Institute of Public Health Social and Preventive Medicine Medical Faculty Mannheim University of Heidelberg Mannheim Germany

Lower extremity amputation and requirement of peripheral artery revascularization are common outcomes of undiagnosed peripheral artery disease patients. In the current work, prediction models for the need of amputation or peripheral revascularization focused on hypertensive patients within seven years follow up are employed. We applied machine learning (ML) models using classifiers such as Extreme Gradient Boost (XGBoost), Random Forest (RF) and Adaptive Boost (AdaBoost), that will allow clinicians to identify the patients at risk of these two endpoints using simple clinical data. We used the non-interventional cohort of the getABI study in the primary care setting, selecting 4,191 hypertensive patients out of 6,474 patients with age over 65 years old and followed up for vascular events or death up to 7 years. During this follow up period, 150 patients underwent either amputation or peripheral revascularization or both. Accuracy, Specificity, Sensitivity and Area under the receiver operating characteristic curve (AUC) were estimated for each machine learning model. The results demonstrate Random Forest as the most accurate model for the prediction of the composite endpoint in hypertensive patients within 7 years follow-up, achieving 73.27 % *** Relevance—This study assists clinicians to better predict and treat these serious outcomes, amputation and peripheral revascularization in hypertensive patients.

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Predicting upper limb disability progression in primary progressive multiple sclerosis using machine learning and statistical methods

Predicting upper limb disability progression in primary prog...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Mostafa, Sally Song, Isabel Hyo Jung Metwally, Ahmed A. Strauli, Nicolas Sewde, Nehemiah Friesenhahn, Michel Usdin, Maxime Jia, Xiaoming San Francisco State University Department of Computer Science San FranciscoCA United States Cairo University Biomedical Engineering Dept Giza Egypt OMNI Biomarker Development Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States PHC Biostatistics Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States

ISBN: (纸本)9781665401265

Multiple sclerosis (MS) is a heterogeneous immune-mediated disorder of the central nervous system, and primary progressive MS (PPMS) is characterized by insidious injury independent of clinical relapses from disease onset. Predicting disease progression remains an unmet need across the MS disease spectrum. We evaluated statistical and machine learning (ML) models for predicting progression in 596 patients with PPMS from ORATORIO, a randomized, placebo (PBO)-controlled, phase 3 clinical study of ocrelizumab (OCR). Using baseline clinical, magnetic resonance imaging, and biomarker (neurofilament light chain [NfL]) data, we trained 6 ML models to predict a progression event (logistic regression, support vector machines, random forest, neural network, gradient boosting, and extreme gradient boosting) and the time to progression (Cox proportional hazards [PH]) on upper limb function, as measured by 20% confirmed worsening on the 9-hole peg test (9HPT). Predictive performance was similar across the six ML models (area under the curve = 0.63 to 0.67) and was modest using the Cox PH model (C-index = 0.76, an improvement over previous studies). In the Cox PH model, treatment arm, baseline 9HPT, neurologic functional system scores, and serum NfL levels were identified as factors associated with progression;model performance was similar across the OCR, PBO, and combined OCR + PBO treatment arms. Accounting for potential interactions with treatment arm resulted in similar predictive performance. To our knowledge, this is the first study to systematically evaluate ML models and statistical methods for predicting progression in PPMS using a well-characterized clinical trial cohort. © 2021 IEEE.

关键词： Support vector machines

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The Rendezvous Between Extreme Value Theory and Next-generation Networks

arXiv

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arXiv 2024年

作者： Sagar, Srinivas Subhash, Athira Liu, Chen-Feng Elzanaty, Ahmed Al-Badarneh, Yazan H. Kalyani, Sheetal Alouini, Mohamed-Slim Bennis, Mehdi Hanzo, Lajos Dept. of Electrical Engg. IIT Madras India Department of Informatics New Jersey Institute of Technology NewarkNJ07102 United States University of Surrey GuildfordGU2 7XH United Kingdom Department of Electrical Engineering The University of Jordan Amman11942 Jordan Makkah Province Thuwal Saudi Arabia Centre for Wireless Communications University of Oulu Oulu90014 Finland School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable low-latency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional communication systems meeting specific average performance requirements, NG systems are expected to meet quality-of-service requirements in extreme scenarios as well. In this regard, extreme value theory (EVT) provides a powerful framework for the design of communication systems. In this paper, we provide a comprehensive survey of advances in communication that utilize EVT to characterize the extreme order statistics of interest. We first give an overview of the history of EVT and then elaborate on the fundamental theorems. Subsequently, we discuss different problems of interest in NG communication systems and how EVT can be utilized for their analysis. We finally point out the open challenges and future directions of EVT in NG communication systems. Copyright © 2024, The Authors. All rights reserved.

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

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2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

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