PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
Browse
14 results
Search Results
Book Part Citation - WoS: 59Citation - Scopus: 68Stem Cell Therapy for Multiple Sclerosis(Springer, 2019) Genç, Bilgesu; Bozan, Hemdem Rodi; Genç, Şermin; Genç, KürşadMultiple sclerosis (MS) is a chronic inflammatory, autoimmune, and neurodegenerative disease of the central nervous system (CNS). It is characterized by demyelination and neuronal loss that is induced by attack of autoreactive T cells to the myelin sheath and endogenous remyelination failure, eventually leading to functional neurological disability. Although recent evidence suggests that MS relapses are induced by environmental and exogenous triggers such as viral infections in a genetic background, its very complex pathogenesis is not completely understood. Therefore, the efficiency of current immunosuppression-based therapies of MS is too low, and emerging disease-modifying immunomodulatory agents such as fingolimod and dimethyl fumarate cannot stop progressive neurodegenerative process. Thus, the cell replacement therapy approach that aims to overcome neuronal cell loss and remyelination failure and to increase endogenous myelin repair capacity is considered as an alternative treatment option. A wide variety of preclinical studies, using experimental autoimmune encephalomyelitis model of MS, have recently shown that grafted cells with different origins including mesenchymal stem cells (MSCs), neural precursor and stem cells, and induced-pluripotent stem cells have the ability to repair CNS lesions and to recover functional neurological deficits. The results of ongoing autologous hematopoietic stem cell therapy studies, with the advantage of peripheral administration to the patients, have suggested that cell replacement therapy is also a feasible option for immunomodulatory treatment of MS. In this chapter, we overview cell sources and applications of the stem cell therapy for treatment of MS. We also discuss challenges including those associated with administration route, immune responses to grafted cells, integration of these cells to existing neural circuits, and risk of tumor growth. Finally, future prospects of stem cell therapy for MS are addressed.Article Citation - WoS: 19Citation - Scopus: 20Use of Micrornas in Personalized Medicine(Humana Press Inc., 2014) Avci, C.B.; Baran, Y.Personalized medicine comprises the genetic information together with the phenotypic and environmental factors to yield healthcare tailored to an individual and removes the limitations of the "one-size-fits-all" therapy approach. This provides the opportunity to translate therapies from bench to clinic, to diagnose and predict disease, and to improve patient-tailored treatments based on the unique signatures of a patient's disease and further to identify novel treatment schedules. Nowadays, tiny noncoding RNAs, called microRNAs, have captured the spotlight in molecular biology with highlights like their involvement in DNA translational control, their impression on mRNA and protein expression levels, and their ability to reprogram molecular signaling pathways in cancer. Realizing their pivotal roles in drug resistance, they emerged as diagnostic targets orchestrating drug response in individualized therapy examples. It is not premature to think that researchers could have the US Food and Drug Administration (FDA)-approved kit-based assays for miRNA analysis in the near future. We think that miRNAs are ready for prime time. © Springer Science+Business Media New York 2014.Article Citation - Scopus: 45Genes Associated With T Helper 17 Cell Differentiation and Function(Frontiers Media S.A., 2016) Nalbant, Ayten; Eskier, DoğaInterleukin-17 (IL-17)-producing T helper cells (Th17 cells) constitute a lineage of CD4 effector T helper cells that is distinct from the Th1 and Th2 CD4 phenotypes. In humans, Th17 differentiation is induced in the presence of the cytokines IL-1 beta, IL-6 and TGF beta, whereas IL-23 maintains Th17 survival. Effector human Th17 cells express several cytokines and cell surface markers, including IL-17A, IL-17F, IL-22, IL-26, CCR6 and TNFa. Studies on human cells have revealed that the RORC2 transcription factor plays an effective role in Th17 differentiation. Th17 cells contribute to the host immune response by involving various pathologies, including rheumatoid arthritis, multiple sclerosis and Crohn's disease. However, the full extent of their contribution to diseases is being investigated. The differentiation of Th17 cells is controlled by many transcription factors, including ROR gammat, IRF4, RUNX1, BATF, and STAT3. This review covers the general principles of CD4 T helper differentiation and the known transcription factors that play a role in the recently discovered Th17 cells.Article Citation - WoS: 16Citation - Scopus: 18Computational and Bioinformatics Methods for Microrna Gene Prediction(Humana Press, 2014) Allmer, JensMicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.Article Citation - WoS: 37Citation - Scopus: 46Computational Methods for Microrna Target Prediction(Humana Press, 2014) Hamzeiy, Hamid; Yousef, Malik; Allmer, JensMicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.Article Citation - Scopus: 18Organogenesis From Transformed Tomato Explants(Humana Press, 2005) Frary, Anne; Van Eck, JoyceTomato was one of the first crops for which a genetic transformation system was reported involving regeneration by organogenesis from Agrobacterium-transformed explants. Since the initial reports, various factors have been studied that affect the efficiency of tomato transformation and the technique has been useful for the isolation and identification of many genes involved in plant disease resistance, morphology and development. In this method, cotyledon explants from in vitro-grown seedlings are precultured overnight on a tobacco suspension feeder layer. The explants are then inoculated with Agrobacterium and returned to the feeder layer for a 2-d period of cocultivation. After cocultivation, the explants are transferred to an MS-based selective regeneration medium containing zeatin. Regenerated shoots are then rooted on a separate selective medium. This protocol has been used with several tomato cultivars and routinely yields transformation efficiencies of 10-15%.Article Citation - WoS: 76Citation - Scopus: 79Near-Surface Viscosity Effects on Capillary Rise of Water in Nanotubes(American Physical Society, 2015) Vo, Truong Quoc; Barışık, Murat; Kim, BoHungIn this paper, we present an approach for predicting nanoscale capillary imbibitions using the Lucas-Washburn (LW) theory. Molecular dynamics (MD) simulations were employed to investigate the effects of surface forces on the viscosity of liquid water. This provides an update to the modified LW equation that considered only a nanoscale slip length. An initial water nanodroplet study was performed to properly elucidate the wetting behavior of copper and gold surfaces. Intermolecular interaction strengths between water and corresponding solid surfaces were determined by matching the contact angle values obtained by experimental measurements. The migration of liquid water into copper and gold capillaries was measured by MD simulations and was found to differ from the modified LW equation. We found that the liquid layering in the vicinity of the solid surface induces a higher density and viscosity, leading to a slower MD uptake of fluid into the capillaries than was theoretically predicted. The near-surface viscosity for the nanoscale-confined water was defined and calculated for the thin film of water that was sheared between the two solid surfaces, as the ratio of water shear stress to the applied shear rate. Considering the effects of both the interface viscosity and slip length of the fluid, we successfully predicted the MD-measured fluid rise in the nanotubes.Article Citation - Scopus: 37The Impact of Onco Type Dx® Recurrence Score of Paraffin-Embedded Core Biopsy Tissues in Predicting Response To Neoadjuvant Chemotherapy in Women With Breast Cancer(IOS Press, 2016) Soran, Atilla; Bhargava, Rohit; Johnson, Ronald; Ahrendt, Gretchen; Bonaventura, Marguerite; Diego, Emilia; McAuliffe, Priscilla F.; Serrano, Merida; Menekşe, Ebru; Sezgin, Efe; McGuire, Kandace P.BACKGROUND: Oncotype DX® test is beneficial in predicting recurrence free survival in estrogen receptor positive (ER+) breast cancer. Ability of the assay to predict response to neoadjuvant chemotherapy (NCT) is less well-studied. OBJECTIVE: We hypothesize a positive association between the Oncotype DX® recurrence score (RS) and the percentage tumor response (%TR) after NCT. METHODS: Pre-therapy RS was measured on core biopsies from 60 patients with ER+, HER2.. invasive breast cancer (IBC) who then received NCT. Pre-therapy tumor size was measured using imaging. %TR, partial response (PR; 50%), pathologic complete response (PCR) and breast conserving surgery (BCS) rates were measured. RESULTS: Median RS was 20 (2 69). Median %TR was 42 (0 97)%. PR was observed in 43% of patients. There was no association between %TR and pre-NCT tumor size, age, Nottingham score or nodal status (p 0:05). No statistically significant association with %TR was seen with RS as a categorical or continuous variable (p = 0:21 and 0.7, respectively). Response to NCT improved as ER (p = 0:02) by RT-PCR decreased. Lower ER expression by IHC correlated with response (p = 0:03). CONCLUSIONS: In patients with ER+ IBC receiving NCT, RS did not predict response to NCT using %TR. The benefit of the assay prior to NCT requires further study.Article Citation - Scopus: 19Feature Selection Has a Large Impact on One-Class Classification Accuracy for Micrornas in Plants(Hindawi Publishing Corporation, 2016) Yousef, Malik; Demirci, Müşerref Duygu Saçar; Khalifa, Waleed; Allmer, JensMicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of 95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.