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Article ; Online: A new design proposal and thermal analysis of subassemblies inside a satellite

Sorin Drăghici

Scientific Bulletin of Naval Academy, Vol XXII, Iss 1, Pp 297-

2019 Volume 301

Abstract: In this study, the authors aim to validate the use of CFRP composites in a subassembly of a satellite as a replacement for currently used aluminum. Considering the high costs of sending a mass unit of payload into orbit, the mass should be minimized. ... ...

Abstract	In this study, the authors aim to validate the use of CFRP composites in a subassembly of a satellite as a replacement for currently used aluminum. Considering the high costs of sending a mass unit of payload into orbit, the mass should be minimized. Therefore, the application of composite materials in electronics housing structures was proposed. For this, a new design is proposed and a comparative thermal analysis of the two boxes is made. The present study focusses on a subassembly’s surfaces and materials of a satellite structure subject to thermal variations in order to numerically validate the new design. A satellite electronic housing was subject to a three-dimensional finite element analysis. The satellite’s thermal loading in an orbit of sun-tracking mode is partially transferred to the electronic housing inside. A model is developed and the temperature distribution in the satellite subassembly predicted using empirical available data. Based on the numerical results conclusion and discussions are drawn.
Keywords	transient heat transfer ; carbon fiber-reinforced plastics ; satellites ; Naval Science ; V
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Naval Academy Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A novel approach for predicting upstream regulators (PURE) that affect gene expression

Tuan-Minh Nguyen / Douglas B. Craig / Duc Tran / Tin Nguyen / Sorin Draghici

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 12

Abstract: Abstract External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely ...

Abstract	Abstract External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Autoantibodies against cytoskeletons and lysosomal trafficking discriminate sarcoidosis from healthy controls, tuberculosis and lung cancers

Samer Najeeb Hanoudi / Harvinder Talwar / Sorin Draghici / Lobelia Samavati

Molecular Biomedicine, Vol 3, Iss 1, Pp 1-

2022 Volume 14

Abstract: Abstract Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from ...

Abstract	Abstract Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from tissues of sarcoidosis patients. Using a high throughput method, we constructed a microarray platform from this cDNA library containing large numbers of sarcoidosis clones. After selective biopanning, 1070 sarcoidosis-specifc clones were arrayed and immunoscreend with 152 sera from patients with sarcoidosis and other pulmonary diseases. To identify the sarcoidosis classifiers two statistical approaches were conducted: First, we identified significant biomarkers between sarcoidosis and healthy controls, and second identified markers comparing sarcoidosis to all other groups. At the threshold of an False Discovery Rate (FDR) < 0.01, we identified 14 clones in the first approach and 12 clones in the second approach discriminating sarcoidosis from other groups. We used the classifiers to build a naïve Bayes model on the training-set and validated it on an independent test-set. The first approach yielded an AUC of 0.947 using 14 significant clones with a sensitivity of 0.93 and specificity of 0.88, whereas the AUC of the second option was 0.92 with a sensitivity of 0.96 and specificity of 0.83. These results suggest robust classifier performance. Furthermore, we characterized the informative phage clones by sequencing and homology searches. Large numbers of classifier-clones were peptides involved in cellular trafficking and cytoskeletons. These results show that sarcoidosis is associated with a specific pattern of immunoreactivity that can discriminate it from other diseases.
Keywords	T7phage library ; Sarcoidosis ; Tuberculosis ; Microarray ; Immunoscreening ; Medicine ; R
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Springer
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MAGPEL

Nafiseh Saberian / Adib Shafi / Azam Peyvandipour / Sorin Draghici

Scientific Reports, Vol 10, Iss 1, Pp 1-

an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature

2020 Volume 11

Abstract: Abstract In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this ... ...

Abstract	Abstract In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to: (i) the complexity of natural language processing, (ii) inconsistent use of standard recommendations for variant description, and (iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients’ clinical outcome on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identification of cell types from single cell data using stable clustering

Azam Peyvandipour / Adib Shafi / Nafiseh Saberian / Sorin Draghici

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 12

Abstract: Abstract Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells ... ...

Abstract	Abstract Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Discovery of primary prostate cancer biomarkers using cross cancer learning

Kaiyue Zhou / Suzan Arslanturk / Douglas B. Craig / Elisabeth Heath / Sorin Draghici

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 13

Abstract: Abstract Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over- ... ...

Abstract	Abstract Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of life effects. Further, there is ever increasing evidence of metastasis and higher mortality when hormone-sensitive or castration-resistant PCa tumors are treated indistinctively. Hence, the critical need is to discover clinically-relevant and actionable PCa biomarkers by better understanding the biology of PCa. In this paper, we have discovered novel biomarkers of PCa tumors through cross-cancer learning by leveraging the pathological and molecular similarities in the DNA repair pathways of ovarian, prostate, and breast cancer tumors. Cross-cancer disease learning enriches the study population and identifies genetic/phenotypic commonalities that are important across diseases with pathological and molecular similarities. Our results show that ADIRF, SLC2A5, C3orf86, HSPA1B are among the most significant PCa biomarkers, while MTRNR2L1, EEPD1, TEPP and VN1R2 are jointly important biomarkers across prostate, breast and ovarian cancers. Our validation results have further shown that the discovered biomarkers can predict the disease state better than any randomly selected subset of differentially expressed prostate cancer genes.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identifying significantly impacted pathways

Tuan-Minh Nguyen / Adib Shafi / Tin Nguyen / Sorin Draghici

Genome Biology, Vol 20, Iss 1, Pp 1-

a comprehensive review and assessment

2019 Volume 15

Abstract: Abstract Background Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of ... ...

Abstract	Abstract Background Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true. Results This article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested. Conclusion Overall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.
Keywords	Pathway analysis ; Signaling pathways ; Network topology ; Metabolic pathways ; Statistical significance ; Bias ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Subject code	310
Language	English
Publishing date	2019-10-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction to

Tuan-Minh Nguyen / Adib Shafi / Tin Nguyen / Sorin Draghici

Genome Biology, Vol 20, Iss 1, Pp 1-

Identifying significantly impacted pathways: a comprehensive review and assessment

2019 Volume 1

Abstract: Following publication of the original paper [1], the authors reported the following update to the competing interests declaration. ...

Abstract	Following publication of the original paper [1], the authors reported the following update to the competing interests declaration.
Keywords	Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Language	English
Publishing date	2019-11-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identifying biologically relevant putative mechanisms in a given phenotype comparison.

Samer Hanoudi / Michele Donato / Sorin Draghici

PLoS ONE, Vol 12, Iss 5, p e

2017 Volume 0176950

Abstract: A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of ... ...

Abstract	A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights.
Keywords	Medicine ; R ; Science ; Q
Subject code	612
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function

Cristiana Iosef / Michael J. Knauer / Michael Nicholson / Logan R. Van Nynatten / Gediminas Cepinskas / Sorin Draghici / Victor K. M. Han / Douglas D. Fraser

Journal of Translational Medicine, Vol 21, Iss 1, Pp 1-

2023 Volume 21

Abstract: Abstract Aims Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment. Methods Plasma proteome from Long-COVID outpatients was ... ...

Abstract	Abstract Aims Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment. Methods Plasma proteome from Long-COVID outpatients was analyzed in comparison to matched acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of 3072 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. Results Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cell redistribution with a dominant resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This potential resetting of cell phenotypes was reflected in prospective vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Several markers (ANGPT1, VEGFA, CCR7, CD56, citrullinated histone 3, elastase) were validated by serological methods in additional patient cohorts. Signaling of transforming growth factor-β1 with probable connections to elevated EP/p300 suggested vascular inflammation and tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway suggested progression from acute COVID-19 to Long-COVID. The vasculo-proliferative process predicted in Long-COVID might contribute to changes in the organ-specific proteome reflective of neurologic and cardiometabolic dysfunction. Conclusions Taken together, our findings point to a vasculo-proliferative process in Long-COVID that is likely initiated either prior hypoxia (localized or systemic) and/or stimulatory factors (i.e., cytokines, chemokines, growth factors, angiotensin, etc). Analyses of the plasma proteome, used as a surrogate for cellular signaling, unveiled potential ...
Keywords	Medicine ; R
Subject code	610
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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