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Effect of Carpolobia lutea leaf extract on erectile dysfunction induced by Paroxetine in rats

Phytomedicine Plus 2024年第3期4卷 100609-100609页

作者： Elekofehinti, Olusola Olusola Akintoye, Oluwapelumi Nifesimi Akinjiyan, Moses Orimoloye Oluwamodupe, Cecilia Ojo, Funmilola Mabel Molehin, Olorunfemi Raphael Oluwatuyi, Adedotun Olayemi Ayodeji, Folasade Oluwatobiloba Alonge, Sunday Ayodele Adanlawo, Isaac Gbadura Bioinformatics and Molecular Biology Unit Department of Biochemistry Federal University of Technology Akure Teady Bioscience Research Laboratory Phytomedicine Molecular Biology & Bioinformatics lab Department of Chemical Science (Biochemistry program) Olusegun Agagu University of Science and Technology Department of Biological Sciences (Botany Programme) Olusegun Agagu University of Science and Technology Department of Biochemistry Faculty of Science Ekiti State University

Background: Erectile dysfunction (ERDF) has been implicated as an adverse effect of Paroxetine. Sildenafil (Viagra) is a conventional currently used in the management of this ailment but with side effects. Carpolobia lutea (CL) is a medicinal plant, traditionally explored and established for its aphrodisiac potential. Aim of the Study: The study aims to investigate the effect of Carpolobia lutea Extract (CLE) on ERDF rats induced by paroxetine at the molecular level and its phytocompounds’ interaction with proteins linked to the erection pathway. Methods: Forty-eight adult male Wistar rats were randomly grouped into six experimental groups: normal control, (20 mg/kg PI- ERDF + 1 ml distilled water) and treated groups (10 mg/kg Sildenafil, 50 mg/kg of CLE, 100 mg/kg of CLE and 200 mg/kg of CLE. Rats were administered paroxetine for five days before their sexual behaviour was examined through mounting frequency and latency when introduced to female rats. Antioxidant assays (DPPH, FRAB and TBARS) were performed over the 21-day experiment, and molecular investigations (in vivo and in silico) on ERDF-linked genes were conducted. Results: CLE has good in vitro antioxidant content. Paroxetine upregulated the expressions of phosphodiesterase-5, arginase, acetylcholinesterase, AMP deaminase and interleukin-6 relative to control. However, CLE downregulated these proteins and upregulated Nrf2, Androgen receptor, and Luteinizing hormone compared to PI- ERDF. CLE-phytocompounds like quercetin favourably interacted with phosphodiesterase-5 and nitric oxide synthase. Conclusion: The findings suggest that Carpolobia lutea leaf's phytocompounds show potential as a therapeutic agent in ERDF management. © 2024 The Author(s)

关键词： Carpolobia lutea Erectile dysfunction Gene expression Medicinal plants

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Understanding the binding of the same ligand to GPCRs of different families

Understanding the binding of the same ligand to GPCRs of dif...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Kwabena Owusu Dankwah Jonathon E Mohl Khodeza Begum Ming-Ying Leung Computational Science Program The University of Texas at El Paso El Paso USA Bioinformatics Program Computational Science Program The University of Texas at El Paso El Paso USA Bioinformatics Program Border Biomedical Research Center The University of Texas at El Paso El Paso USA

ISBN: (纸本)9781665429825

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. Such predictions are cost-efficient and can be important aides for planning wet lab experiments to help elucidate signaling pathways and expedite drug discovery. There are existing computational tools for GPCR ligand binding prediction using various sequence and structural derived features. However, these methods have been typically tested on specific families of GPCRs and none has focused on features that characterize binding of a single ligand to multiple GPCR families. In this work, we set out to uncover and understand the binding of the same ligand to multiple GPCRs of different families. As expected only a few GPCRs share a conserved sequence motif. We observed local 3D structural similarities and local sequence similarities. We also observed that the GPCRs within the same family share similar binding pockets for the ligand. Molecular docking revealed that a ligand can bind to GPCRs of different families with different poses but similar conformations. Finally, the ligands bind to similar pockets with similar electrostatic and solvation properties and share similar residues. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

关键词： Drugs Proteins Three-dimensional displays Toxicology Conferences Throughput Planning

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Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

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AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities

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Journal of Molecular Biology 2025年 168967页

作者： Fleming, Jennifer Magana, Paulyna Nair, Sreenath Tsenkov, Maxim Bertoni, Damian Pidruchna, Ivanna Lima Afonso, Marcelo Querino Midlik, Adam Paramval, Urmila Žídek, Augustin Laydon, Agata Kovalevskiy, Oleg Pan, Joshua Cheng, Jun Avsec, Žiga Bycroft, Clare Wong, Lai Hong Last, Meera Mirdita, Milot Steinegger, Martin Kohli, Pushmeet Váradi, Mihály Velankar, Sameer European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Google DeepMind London United Kingdom School of Biological Sciences Seoul National University Seoul 08826 Cuba Interdisciplinary Program in Bioinformatics Seoul National University Seoul 08826 Cuba Institute of Molecular Biology and Genetics Seoul 08826 Cuba Artificial Intelligence Institute Seoul National University Seoul 08826 Cuba

The AlphaFold Protein Structure Database (https://***/) has made significant strides in enhancing its utility and accessibility for the life science research community. The recent integration of AlphaMissense predictions enables access to the pathogenicity of human protein missense variants, with an innovative and interactive heatmap and 3D visualisation that display variant data at the residue level. Users can now toggle between structure model quality (pLDDT) and average pathogenicity scores, providing insights into the implications of specific residue changes. The Foldseek integration offers a rapid and accurate method for protein structure searches and comparisons. Bulk data download options further facilitate comprehensive data analysis and integration with other computational tools. The 3D-Beacons framework (https://***/pdbe/pdbe-kb/3dbeacons/) has also been enhanced with detailed annotation endpoints (such as AlphaMissense data) and integrates LevyLab's dataset of homomeric AlphaFold 2 models. These advancements significantly improve the functionality and accessibility of these resources, enabling discoveries using structure data. © 2025 The Author(s)

关键词： AlphaFold Alphamissense Annotations Foldseek Training

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method 7

Implementation of expert systems in potassium deficiency in ...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Hafizal, Muhammad Titan Putra, Dian Pratama Wirianata, Herry Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta55282 Indonesia Faculty of Forestry STIPER Institute of Agriculture Yogyakarta55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants' condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-To-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants. © 2022 Elsevier B.V.. All rights reserved.

关键词： Expert systems

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A Flow Artist for High-Dimensional Cellular Data

A Flow Artist for High-Dimensional Cellular Data

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IEEE Workshop on Machine Learning for Signal Processing

作者： Kincaid MacDonald Dhananjay Bhaskar Guy Thampakkul Nhi Nguyen Joia Zhang Michael Perlmutter Ian Adelstein Smita Krishnaswamy Department of Mathematics Yale University Department of Genetics Yale School of Medicine Department of Computer Science Yale University Department of Mathematics Pomona College Applied Mathematics Program Yale University Department of Statistics University of Washington Department of Mathematics Boise State University Computational Biology and Bioinformatics Program Yale University

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data.

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Estimating HIV Cross-sectional Incidence Using Recency Tests from a Non-representative Sample

arXiv

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arXiv 2024年

作者： Pan, Jianan Bannick, Marlena Gao, Fei Department of Biostatistics University of Washington SeattleWA United States Biostatistics Bioinformatics and Epidemiology Program Fred Hutchinson Cancer Center SeattleWA United States Public Health Sciences Division Fred Hutchinson Cancer Research Center SeattleWA United States

Cross-sectional incidence estimation based on recency testing has become a widely used tool in HIV research. Recently, this method has gained prominence in HIV prevention trials to estimate the "placebo" incidence that participants might experience without preventive treatment. The application of this approach faces challenges due to non-representative sampling, as individuals aware of their HIV-positive status may be less likely to participate in screening for an HIV prevention trial. To address this, a recent phase 3 trial excluded individuals based on whether they have had a recent HIV test. To the best of our knowledge, the validity of this approah has yet to be studied. In our work, we investigate the performance of cross-sectional HIV incidence estimation when excluding individuals based on prior HIV tests in realistic trial settings. We develop a statistical framework that incorporates an testing-based criterion and possible non-representative sampling. We introduce a metric we call the effective mean duration of recent infection (MDRI) that mathematically quantifies bias in incidence estimation. We conduct an extensive simulation study to evaluate incidence estimator performance under various scenarios. Our findings reveal that when screening attendance is affected by knowledge of HIV status, incidence estimators become unreliable unless all individuals with recent HIV tests are excluded. Additionally, we identified a trade-off between bias and variability: excluding more individuals reduces bias from non-representative sampling but in many cases increases the variability of incidence estimates. These findings highlight the need for caution when applying testing-based criteria and emphasize the importance of refining incidence estimation methods to improve the design and evaluation of future HIV prevention trials. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

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miRNA networks regulating gene expression in response to tension or compression forces in the cells of the periodontal ligament

miRNA networks regulating gene expression in response to ten...

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BioInspired Processing (BIP), IEEE International Conference on

作者： Mariana Bonilla Valverde Rodrigo Mora Rodríguez Master Program on Bioinformatics and Systems Biology Postgraduate Program SEP University of Costa Rica 11501-2060 San José Costa Rica Faculty of Microbiology University of Costa Rica 11501-2060 San José Costa Rica

ISBN: (纸本)9781665470490

In response to force application, biological reactions occur in the periodontal ligament that causes the orthodontic tooth movement. Differential expression of genes such as ALPL, FGF7, PDRM1, and NR4A2 are involved in the exposure to tension or compression forces by contributing to bone remodeling. In this study, a systems biology approach is used in order to identify a modulator of gene expression when mechanical loadings are applied in periodontal ligament cells. We developed a mathematical model of the interaction network of ALPL and FGF7 with several miRNAs and the transcription factor modulators in BioNetUCR, Cytoscape and COPASI software. We found that miR129-2 has a negative control in ALPL, FGF7, NR4A2 and PRDM1 genes, while miRNA192 has a negative control in ALPL and FGF7 genes in compression and tension side. Both miRNAs are proposed for future experimental validation.

关键词： Systems biology Loading Force Modulation Cells (biology) Bones Software

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Cancer Prediction Using Clinical and Genomic Data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services Predictive models Data models bioinformatics Cancer Systematic literature review

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InDRI: Intelligent Diagnosis Radiology Images 6

InDRI: Intelligent Diagnosis Radiology Images

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Ritonga, Imelda Liana Gultom, Roby Pahala Januario Lubis, Elida Afni Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Clinical Pathology Unit Rumah Sakit Imelda Pekerja Indonesia Medan20239 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

The need for an early screening and Computer-Aided Diagnosis (CAD) system based on Artificial Intelligence (AI) for the field of radiology is essential to realize considering the large impact of lung diseases globally. However, developing AI requires a lot of data, which is a challenge because data in the health sector tends to be limited in quantity and has disparities between diagnostic categories (data imbalance). To overcome this issue, DINO ViT models was trained with the publicly available COVID-19 Radiography Database in our previous study. The model was subsequently implemented into a web application, with this study focusing on the development of a prototype for the application. The study found this application can be useful for medical personnel and doctors in carrying out early screening and CAD. Meanwhile, it also highlighted the general unfamiliarity with AI applications among medical staff, emphasizing the need for increased education and training on AI's role in healthcare. © 2024 IEEE.

关键词： Lung cancer

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