PubMed İndeksli Yayınlar Koleksiyonu
Bu koleksiyon için kalıcı URIhttps://hdl.handle.net/20.500.12573/397
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Makale Atıf - WoS: 13Mont: A Multi-Omics Integrative Tool for Breast Cancer Subtype Stratification(Public Library Science, 2025) Unlu Yazici, Miray; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik; Yazici, Miray UnluBreast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.Makale Atıf - WoS: 13Atıf - Scopus: 154D-QSAR Investigation and Pharmacophore Identification of Pyrrolo[2,1-C][1,4]Benzodiazepines Using Electron Conformational-Genetic Algorithm Method(Taylor & Francis Ltd, 2016-04-02) Ozalp, A.; Yavuz, S. C.; Sabanci, N.; Copur, F.; Kokbudak, Z.; Saripinar, E.In this paper, we present the results of pharmacophore identification and bioactivity prediction for pyrrolo[2,1-c][1,4]benzodiazepine derivatives using the electron conformational-genetic algorithm (EC-GA) method as 4D-QSAR analysis. Using the data obtained from quantum chemical calculations at PM3/HF level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. The ECMC of the lowest energy conformer of the compound with the highest activity was chosen as the template and compared with the ECMCs of the lowest energy conformer of the other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA, i.e. pharmacophore) by ECSP software. A descriptor pool was generated taking into account the obtained pharmacophore. To predict the theoretical activity and select the best subset of variables affecting bioactivities, the nonlinear least square regression method and genetic algorithm were performed. For four types of activity including the GI(50), TGI, LC50 and IC50 of the pyrrolo[2,1-c][1,4] benzodiazepine series, the r(train)(2), r(test)(2) and q(2) values were 0.858, 0.810, 0.771; 0.853, 0.848, 0.787; 0.703, 0.787, 0.600; and 0.776, 0.722, 0.687, respectively.Makale Atıf - WoS: 28Atıf - Scopus: 29A Comparative Study on Normal and Obese Mice Indicates That the Secretome of Mesenchymal Stromal Cells Is Influenced by Tissue Environment and Physiopathological Conditions(BMC, 2020-07-29) Ayaz-Guner, Serife; Alessio, Nicola; Acar, Mustafa B.; Aprile, Domenico; Ozcan, Servet; Di Bernardo, Giovanni; Galderisi, UmbertoBackground: The term mesenchymal stromal cells (MSCs) designates an assorted cell population comprised of stem cells, progenitor cells, fibroblasts, and stromal cells. MSCs contribute to the homeostatic maintenance of many organs through paracrine and long-distance signaling. Tissue environment, in both physiological and pathological conditions, may affect the intercellular communication of MSCs. Methods: We performed a secretome analysis of MSCs isolated from subcutaneous adipose tissue (sWAT) and visceral adipose tissue (vWAT), and from bone marrow (BM), of normal and obese mice. Results: The MSCs isolated from tissues of healthy mice share a common core of released factors: components of cytoskeletal and extracellular structures; regulators of basic cellular functions, such as protein synthesis and degradation; modulators of endoplasmic reticulum stress; and counteracting oxidative stress. It can be hypothesized that MSC secretome beneficially affects target cells by the horizontal transfer of many released factors. Each type of MSC may exert specific signaling functions, which could be determined by looking at the many factors that are exclusively released from every MSC type. The vWAT-MSCs release factors that play a role in detoxification activity in response to toxic substances and drugs. The sWAT-MSC secretome contains proteins involved in in chondrogenesis, osteogenesis, and angiogenesis. Analysis of BM-MSC secretome revealed that these cells exert a signaling function by remodeling extracellular matrix structures, such as those containing glycosaminoglycans. Obesity status profoundly modified the secretome content of MSCs, impairing the above-described activity and promoting the release of inflammatory factors. Conclusion: We demonstrated that the content of MSC secretomes depends on tissue microenvironment and that pathological condition may profoundly alter its composition.Makale Atıf - WoS: 2Atıf - Scopus: 2A Comprehensive MicroRNA-seq Transcriptomic Analysis of Tay-Sachs Disease Mice Revealed Distinct miRNA Profiles in Neuroglial Cells(Springernature, 2025) Kaya, Beyza; Orhan, Mehmet Emin; Yanbul, Selman; Demirci, Muserref Duygu Sacar; Demir, Secil Akyildiz; Seyrantepe, VolkanTay-Sachs disease (TSD) is a rare lysosomal storage disorder marked by the progressive buildup of GM2 in the central nervous system (CNS). This condition arises from mutations in the HEXA gene, which encodes the alpha subunit of the enzyme beta-hexosaminidase A. A newly developed mouse model for early-onset TSD (Hexa-/-Neu3-/-) exhibited signs of neurodegeneration and neuroinflammation, evidenced by elevated levels of pro-inflammatory cytokines and chemokines, as well as significant astrogliosis and microgliosis. Identifying disease-specific MicroRNAs (miRNAs) may aid the development of targeted therapies. Although previous small-scale studies have investigated miRNA expression in some regions of GM2 gangliosidosis mouse models, thorough profiling of miRNAs in this innovative TSD model remains to be done. In this study, we employed next-generation sequencing to analyze the complete miRNA profile of neuroglial cells from Hexa-/-Neu3-/- mice. By comparing KEGG and Reactome pathways associated with neurodegeneration, neuroinflammation, and sphingolipid metabolism in Hexa-/-Neu3-/- neuroglial cells, we discovered new MicroRNAs and their targets related to the pathophysiology of GM2 gangliosidosis. For the first time, our findings showed that miR-708-5p, miR-672-5p, miR-204-5p, miR-335-5p, and miR-296-3p were upregulated, while miR-10 b-5p, miR-615-3p, miR-196a-5p, miR-214-5p, and miR-199a-5p were downregulated in Hexa-/-Neu3-/- neuroglial cells in comparison to age-matched wild-type (WT). These specific changes in miRNA expression deepen our understanding of the disease's neuropathological characteristics in Hexa-/-Neu3-/- mice. Our study suggests that miRNA-based therapeutic strategies may improve clinical outcomes for TSD patients.Makale Atıf - WoS: 14Atıf - Scopus: 14A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization(Amer Medical Assoc, 2021-10-11) Yan, Yao; Schaffter, Thomas; Bergquist, Timothy; Yu, Thomas; Prosser, Justin; Aydin, Zafer; Mooney, SeanIMPORTANCE Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. OBJECTIVES To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. DESIGN, SETTING, AND PARTICIPANTS This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. MAIN OUTCOMES AND MEASURES Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. RESULTS In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. CONCLUSIONS AND RELEVANCE In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.Makale Atıf - WoS: 15Atıf - Scopus: 15A Crowdsourced Analysis to Identify Ab Initio Molecular Signatures Predictive of Susceptibility to Viral Infection(Nature Portfolio, 2018-10-24) Fourati, Slim; Taa, Aarthi; Mahmoudian, Mehrad; Burkhart, Joshua G.; Klen, Riku; Henao, Ricardo; Sieberts, Solveig K.; Talla, AarthiThe response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.Makale A Decision Support System for the Prediction of Mortality in Patients With Acute Kidney Injury Admitted in Intensive Care Unit(Univ South Bohemia, 2020-03-01) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir HakanIntensive care unit (ICU) is a very special unit of a hospital, where healthcare professionals provide treatment and, later, close followup to the patients. It is crucial to estimate mortality in ICU patients from many viewpoints. The purpose of this study is to classify the status of patients with acute kidney injury (AKI) in ICU as early mortality, late mortality, and survival by the application of Classification and Regression Trees (CART) algorithm to the patients' attributes such as blood urea nitrogen, creatinine, serum and urine neutrophil gelatinase-associated lipocalin (NGAL), alkaline phosphatase, lactate dehydrogenase (LDH), gamma-glutamyl transferase, laboratory electrolytes, blood gas, mean arterial pressure, central venous pressure and demographic details of patients. This study was conducted 50 patients with AKI who were followed up in the ICU. The study also aims to determine the significance of relationship between the attributes used in the prediction of mortality in CART and patients' status by employing the Kruskal-Wallis H test. The classification accuracy, sensitivity, and specificity of CART for the tested attributes for the prediction of early mortality, late mortality, and survival of patients were 90.00%, 83.33%, and 91.67%, respectively. The values of both urine NGAL and LDH on day 7 showed a considerable difference according to the patients' status after being examined by the Kruskal-Wallis H test.Makale Atıf - WoS: 10Atıf - Scopus: 16A Fractional-Order Mathematical Model Based on Vaccinated and Infected Compartments of Sars-Cov With a Real Case Study During the Last Stages of the Epidemiological Event(Elsevier, 2023-06) Bilgil, Halis; Yousef, Ali; Erciyes, Ayhan; Erdinc, Ummugulsum; Ozturk, ZaferIn 2020 the world faced with a pandemic spread that affected almost everything of humans' social and health life. Regulations to decrease the epidemiological spread and studies to produce the vaccine of SARS-CoV-2 were on one side a hope to return back to the regular life, but on the other side there were also notable criticism about the vaccines itself. In this study, we established a fractional order differential equations system incorporating the vaccinated and re-infected compartments to a SIR frame to consider the expanded and detailed form as an SVIIvR model. We considered in the model some essential parameters, such as the protection rate of the vaccines, the vaccination rate, and the vaccine's lost efficacy after a certain period. We obtained the local stability of the disease-free and co-existing equilibrium points under specific conditions using the Routh-Hurwitz Criterion and the global stability in using a suitable Lyapunov function. For the numerical solutions we applied the Euler's method. The data for the simulations were taken from the World Health Organization (WHO) to illustrate numerically some scenarios that happened.(c) 2022 Elsevier B.V. All rights reserved.Makale Atıf - WoS: 8Atıf - Scopus: 8A Genome-Scale Metabolic Model of Parasitic Whipworm(Nature Portfolio, 2023-10-31) Bay, Omer F.; Hayes, Kelly S.; Schwartz, Jean-Marc; Grencis, Richard K.; Roberts, Ian S.Genome-scale metabolic models are widely used to enhance our understanding of metabolic features of organisms, host-pathogen interactions and to identify therapeutics for diseases. Here we present iTMU798, the genome-scale metabolic model of the mouse whipworm Trichuris muris. The model demonstrates the metabolic features of T. muris and allows the prediction of metabolic steps essential for its survival. Specifically, that Thioredoxin Reductase (TrxR) enzyme is essential, a prediction we validate in vitro with the drug auranofin. Furthermore, our observation that the T. muris genome lacks gsr-1 encoding Glutathione Reductase (GR) but has GR activity that can be inhibited by auranofin indicates a mechanism for the reduction of glutathione by the TrxR enzyme in T. muris. In addition, iTMU798 predicts seven essential amino acids that cannot be synthesised by T. muris, a prediction we validate for the amino acid tryptophan. Overall, iTMU798 is as a powerful tool to study not only the T. muris metabolism but also other Trichuris spp. in understanding host parasite interactions and the rationale design of new intervention strategies. In this work, Bay et al describe the construction of the first genome-scale metabolic model for the parasitic whipworm, Trichuris muris and use it to identify novel metabolic pathways and predict critical enzymes and essential metabolites for worm survival.Makale Atıf - WoS: 4Atıf - Scopus: 4A Homozygous Frameshift Variant in the CILK1 Gene Causes Cranioectodermal Dysplasia(Springernature, 2025) Sezer, Abdullah; Oner, Sukru S.; Saat, Hanife; Turan, Merve G.; Gungor, Tulin; Cevik, Sebiha; Kaplan, Oktay I.Cranioectodermal dysplasia (CED) is a ciliopathy characterized by skeletal and ectodermal abnormalities, renal failure, and liver fibrosis. Pathogenic variants in genes that encode the intraflagellar transport (IFT) complex components, particularly IFT-A, are responsible for approximately two-thirds of the CED cases. However, the cause of the remaining cases remains unknown. Ciliogenesis-associated kinase 1 (CILK1) is a highly conserved ciliary serine/threonine kinase with an N-terminal catalytic domain responsible for kinase activity and a C-terminal non-catalytic domain that interacts with the IFT-B complex. Biallelic variants in the catalytic domain are associated with lethal skeletal dysplasia, endocrine cerebroosteodysplasia, and short-rib polydactyly syndrome. No human disease has been linked to biallelic variants in the non-catalytic domain. We present a homozygous frameshift variant in the CILK1 gene that affects the distal part of the non-catalytic domain, causing CED in five patients from two pedigrees. All the patients survived into childhood and had disproportionately short stature, skeletal abnormalities, ectodermal dysplasia, renal issues, and liver complications. Functional data from patient-derived cells and the C. elegans model indicate that the variant reduces cilia number, increases cilia length, and disrupts the localization of IFT components. In contrast, the ciliary localization of CILK1 bearing the variant itself remains unaffected. Notably, we rescued the majority of these abnormalities by reintroducing CILK1 into patient-derived cells. Finally, our study describes CILK1 as a novel causal gene and the first non-IFT protein-encoding gene in the etiology of CED, thus expanding the known genotypic, mechanistic, and phenotypic spectrum of CED.