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Article ; Online: Network-based approaches for modeling disease regulation and progression

Gihanna Galindez / Sepideh Sadegh / Jan Baumbach / Tim Kacprowski / Markus List

Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 780-

2023 Volume 795

Abstract: Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering ... ...

Abstract	Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
Keywords	Network enrichment ; Network inference ; Disease modeling ; Network medidince ; Systems medicine ; Biotechnology ; TP248.13-248.65
Subject code	006
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Network-based approaches for modeling disease regulation and progression.

Galindez, Gihanna / Sadegh, Sepideh / Baumbach, Jan / Kacprowski, Tim / List, Markus

Computational and structural biotechnology journal

2022 Volume 21, Page(s) 780–795

Abstract	Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
Language	English
Publishing date	2022-12-16
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.12.022
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Head Turn During Visual Field Testing to Minimize the Influence of Prominent Facial Anatomy.

Sadegh Mousavi, Seyedmostafa / Jamali Dogahe, Sepideh / Lyons, Lance J / Khanna, Cheryl L

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society

2023

Abstract: Background: Facial contour naturally decreases the visual field. Peripheral visual field defects caused by facial anatomy and ocular pathology can be missed in a routine standard of care. Mathematically calculating the true angle for turning the head to ...

Abstract	Background: Facial contour naturally decreases the visual field. Peripheral visual field defects caused by facial anatomy and ocular pathology can be missed in a routine standard of care. Mathematically calculating the true angle for turning the head to optimize the peripheral visual field has not been studied to date. The purpose of this study was to explore the utility of turning the head during perimetry to maximize the testable visual field. Methods: Six healthy study participants aged 18-52 were enrolled, prospectively; the dominant eye of each participant was tested. In total, 60-4 visual fields were obtained from each participant's dominant eye with the head in primary position. Then, the 60-4 tests were repeated with the head turned prescribed degrees toward and away from the tested eye ("manual method"). Based on a photograph of the participant's face, a convolutional neural network (CNN) was used to predict the optimal head turn angle for maximizing the field, and the test was repeated in this position ("automated method"). Results: Maximal visual field exposure was found at a head turn of 15° away from the tested eye using the manual method and was found at an average head turn of 12.6° using the automated method; maximum threshold values were similar between manual and automated methods. The mean of threshold in these subjects at the standard direction and the predicted optimum direction was 1,302, SD = 69.35, and 1,404, SD = 67.37, respectively (P = 0.02). Conclusions: Turning the head during perimetry maximizes the testable field area by minimizing the influence of prominent facial anatomy. In addition, our CNN can accurately predict each individual's optimal angle of head turn for maximizing the visual field.
Language	English
Publishing date	2023-12-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	1189901-3
ISSN	1536-5166 ; 1070-8022
ISSN (online)	1536-5166
ISSN	1070-8022
DOI	10.1097/WNO.0000000000002019
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Article ; Online: Predicting 60-4 visual field tests using 3D facial reconstruction.

Jamali Dogahe, Sepideh / Garmany, Armin / Sadegh Mousavi, Seyedmostafa / Khanna, Cheryl L

The British journal of ophthalmology

2023 Volume 108, Issue 1, Page(s) 112–116

Abstract: Background: Despite, the potential clinical utility of 60-4 visual fields, they are not frequently used in clinical practice partly, due to the purported impact of facial contour on field defects. The purpose of this study was to design and test an ... ...

Abstract	Background: Despite, the potential clinical utility of 60-4 visual fields, they are not frequently used in clinical practice partly, due to the purported impact of facial contour on field defects. The purpose of this study was to design and test an artificial intelligence-driven platform to predict facial structure-dependent visual field defects on 60-4 visual field tests. Methods: Subjects with no ocular pathology were included. Participants were subject to optical coherence tomography, 60-4 Swedish interactive thresholding algorithm visual field tests and photography. The predicted visual field was compared with observed 60-4 visual field results in subjects. Average and point-specific sensitivity, specificity, precision, negative predictive value, accuracy, and F1-scores were primary outcome measures. Results: 30 healthy were enrolled. Three-dimensional facial reconstruction using a convolution neural network (CNN) was able to predict facial contour-dependent 60-4 visual field defects in 30 subjects without ocular pathology. Overall model accuracy was 97%±3% and 96%±3% and the F1-score, dependent on precision and sensitivity, was 58%±19% and 55%±15% for the right eye and left eye, respectively. Spatial-dependent model performance was observed with increased sensitivity and precision within the far inferior nasal field reflected by an average F1-score of 76%±20% and 70%±29% for the right eye and left eye, respectively. Conclusions: This pilot study reports the development of a CNN-enhanced platform capable of predicting 60-4 visual field defects in healthy controls based on facial contour. Further study with this platform may enhance understanding of the influence of facial contour on 60-4 visual field testing.
MeSH term(s)	Humans ; Visual Field Tests/methods ; Artificial Intelligence ; Pilot Projects ; Visual Fields ; Predictive Value of Tests ; Vision Disorders/diagnosis ; Tomography, Optical Coherence ; Sensitivity and Specificity ; Intraocular Pressure
Language	English
Publishing date	2023-12-18
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80078-8
ISSN	1468-2079 ; 0007-1161
ISSN (online)	1468-2079
ISSN	0007-1161
DOI	10.1136/bjo-2022-321651
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Article ; Online: Docosahexaenoic acid (DHA) impairs hypoxia-induced cellular and exosomal overexpression of immune-checkpoints and immunomodulatory molecules in different subtypes of breast cancer cells.

Maralbashi, Sepideh / Aslan, Cynthia / Kahroba, Houman / Asadi, Milad / Soltani-Zangbar, Mohammad Sadegh / Haghnavaz, Navideh / Jadidi, Farhad / Salari, Farhad / Kazemi, Tohid

BMC nutrition

2024 Volume 10, Issue 1, Page(s) 41

Abstract: Background: Tumor cells express immune-checkpoint molecules to suppress anti-tumor immune responses. In part, immune evasion takes place by secreting exosomes bearing immune-checkpoint and immunomodulatory molecules and their inducing and/or regulating ... ...

Abstract	Background: Tumor cells express immune-checkpoint molecules to suppress anti-tumor immune responses. In part, immune evasion takes place by secreting exosomes bearing immune-checkpoint and immunomodulatory molecules and their inducing and/or regulating agents e.g., microRNAs (miRs). This study aimed to evaluate the effects of omega-3 fatty acid, docosahexaenoic acid (DHA), on the expression of some selected immune-checkpoint and immunomodulatory molecules and their regulating miRs under both normoxic and hypoxic conditions in triple negative (TNBC) invasive and triple positive non-invasive breast cancer cell lines. Methods: MDA-MB-231 and BT-474 cells were treated with 100 µM DHA under hypoxic and normoxic conditions for 24 h. Exosomes were isolated by ultracentrifuge and confirmed by electron microscope and anti-CD9, -CD63, -CD81 immunoblotting. Total RNA from cells and exosomes were extracted and expression of CD39, CD73, CD47, CD80, PD-L1, B7-H3, B7-H4 genes and their related miRs were evaluated by quantitative Real-time PCR. Results: This study showed significant over-expression of immune-checkpoint and immunomodulatory molecules under hypoxic condition. Treatment with DHA resulted in a significant decrease in immune-checkpoint and immunomodulatory molecule expression as well as an upregulation of their regulatory miRNA expression. Conclusion: DHA supplementation may be utilized in breast cancer therapy for down-regulation of cellular and exosomal immune escape-related molecules.
Language	English
Publishing date	2024-03-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2809847-X
ISSN	2055-0928 ; 2055-0928
ISSN (online)	2055-0928
ISSN	2055-0928
DOI	10.1186/s40795-024-00844-y
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Article ; Online: Network-based approaches for modeling disease regulation and progression

Galindez, Gihanna / Sadegh, Sepideh / Baumbach, Jan / Kacprowski, Tim / List, Markus

Computational and Structural Biotechnology Journal. 2023, v. 21 p.780-795

2023

Abstract	Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
Keywords	biomedical research ; biotechnology ; data collection ; drug development ; precision medicine ; Network enrichment ; Network inference ; Disease modeling ; Network medidince ; Systems medicine
Language	English
Size	p. 780-795.
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.12.022
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Article ; Online: Resveratrol as an antitumor agent for glioblastoma multiforme: Targeting resistance and promoting apoptotic cell deaths.

Karkon-Shayan, Sepideh / Aliashrafzadeh, Hasan / Dianat-Moghadam, Hassan / Rastegar-Pouyani, Nima / Majidi, Mohammadreza / Zarei, Mahdi / Moradi-Vastegani, Sadegh / Bahramvand, Yaser / Babaniamansour, Sepideh / Jafarzadeh, Emad

Acta histochemica

2023 Volume 125, Issue 6, Page(s) 152058

Abstract: Glioblastoma multiforme (GBM) is one of the most aggressive brain and spinal cord tumors. Despite the significant development in application of antitumor drugs, no significant increases have been observed in the survival rates of patients with GBM, as ... ...

Abstract	Glioblastoma multiforme (GBM) is one of the most aggressive brain and spinal cord tumors. Despite the significant development in application of antitumor drugs, no significant increases have been observed in the survival rates of patients with GBM, as GBM cells acquire resistance to conventional anticancer therapeutic agents. Multiple studies have revealed that PI3K/Akt, MAPK, Nanog, STAT 3, and Wnt signaling pathways are involved in GBM progression and invasion. Besides, biological processes such as anti-apoptosis, autophagy, angiogenesis, and stemness promote GBM malignancy. Resveratrol (RESV) is a non-flavonoid polyphenol with high antitumor activity, the potential of which, regulating signaling pathways involved in cancer malignancy, have been demonstrated by many studies. Herein, we present the potential of RESV in both single and combination therapy- targeting various signaling pathways- which induce apoptotic cell death, re-sensitize cancer cells to radiotherapy, and induce chemo-sensitizing effects to eventually inhibit GBM progression.
MeSH term(s)	Humans ; Glioblastoma/metabolism ; Resveratrol/pharmacology ; Phosphatidylinositol 3-Kinases/metabolism ; Brain Neoplasms/drug therapy ; Brain Neoplasms/metabolism ; Brain Neoplasms/pathology ; Antineoplastic Agents/pharmacology ; Apoptosis ; Cell Line, Tumor
Chemical Substances	Resveratrol (Q369O8926L) ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-) ; Antineoplastic Agents
Language	English
Publishing date	2023-06-17
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	77-2
ISSN	1618-0372 ; 0065-1281
ISSN (online)	1618-0372
ISSN	0065-1281
DOI	10.1016/j.acthis.2023.152058
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Article ; Online: Retraction Note: Effect of L-citrulline supplementation on blood pressure: a systematic review and meta-analysis of randomized controlled trials.

Mahboobi, Sepideh / Tsang, Catherine / Rezaei, Shahla / Jafarnejad, Sadegh

Journal of human hypertension

2019 Volume 35, Issue 4, Page(s) 381

Language	English
Publishing date	2019-10-24
Publishing country	England
Document type	Retraction of Publication
ZDB-ID	639472-3
ISSN	1476-5527 ; 0950-9240
ISSN (online)	1476-5527
ISSN	0950-9240
DOI	10.1038/s41371-019-0280-1
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Article ; Online: Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond.

Sadegh, Sepideh / Skelton, James / Anastasi, Elisa / Maier, Andreas / Adamowicz, Klaudia / Möller, Anna / Kriege, Nils M / Kronberg, Jaanika / Haller, Toomas / Kacprowski, Tim / Wipat, Anil / Baumbach, Jan / Blumenthal, David B

Nature communications

2023 Volume 14, Issue 1, Page(s) 1662

Abstract: A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and ... ...

Abstract	A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
Language	English
Publishing date	2023-03-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-37349-4
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Article ; Online: Robust disease module mining via enumeration of diverse prize-collecting Steiner trees.

Bernett, Judith / Krupke, Dominik / Sadegh, Sepideh / Baumbach, Jan / Fekete, Sándor P / Kacprowski, Tim / List, Markus / Blumenthal, David B

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 6, Page(s) 1600–1606

Abstract: Motivation: Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically ...

Abstract	Motivation: Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i.e. the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the widespread adoption of DMMMs in the biomedical sciences. Results: To overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG (Kyoto Encyclopedia of Genes and Genomes) gene set enrichment scores and overlap with DisGeNET disease genes. Availability and implementation: A Python 3 implementation and scripts to reproduce the results reported in this article are available on GitHub: https://github.com/bionetslab/robust, https://github.com/bionetslab/robust-eval. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Trees ; Computational Biology/methods ; Protein Interaction Maps
Language	English
Publishing date	2022-01-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab876
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