WOS

Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Author: search.filters.author.Kayhan, Cavit Kerem

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    Gamma-irradiated SARS-CoV-2 vaccine candidate, OZG-38.61.3, confers protection from SARS-CoV-2 challenge in human ACEII-transgenic mice
    (NATURE PORTFOLIO, 2021-01-01) Turan, Raife Dilek; Tastan, Cihan; Kancagi, Derya Dilek; Yurtsever, Bulut; Karakus, Gozde Sir; Ozer, Samed; Abanuz, Selen; Cakirsoy, Didem; Tumentemur, Gamze; Demir, Sevda; Seyis, Utku; Kuzay, Recai; Elek, Muhammer; Kocaoglu, Miyase Ezgi; Ertop, Gurcan; Arbak, Serap; Elmas, Merve Acikel; Hemsinlioglu, Cansu; Ng, Ozden Hatirnaz; Akyoney, Sezer; Sahin, Ilayda; Kayhan, Cavit Kerem; Tokat, Fatma; Akpinar, Gurler; Kasap, Murat; Kocagoz, Ayse Sesin; Ozbek, Ugur; Telci, Dilek; Sahin, Fikrettin; Yalcin, Koray; Ratip, Siret; Ince, Umit; Ovali, Ercument
    The SARS-CoV-2 virus caused the most severe pandemic around the world, and vaccine development for urgent use became a crucial issue. Inactivated virus formulated vaccines such as Hepatitis A and smallpox proved to be reliable approaches for immunization for prolonged periods. In this study, a gamma-irradiated inactivated virus vaccine does not require an extra purification process, unlike the chemically inactivated vaccines. Hence, the novelty of our vaccine candidate (OZG-38.61.3) is that it is a non-adjuvant added, gamma-irradiated, and intradermally applied inactive viral vaccine. Efficiency and safety dose (either 10(13) or 10(14) viral RNA copy per dose) of OZG-38.61.3 was initially determined in BALB/c mice. This was followed by testing the immunogenicity and protective efficacy of the vaccine. Human ACE2-encoding transgenic mice were immunized and then infected with the SARS-CoV-2 virus for the challenge test. This study shows that vaccinated mice have lowered SARS-CoV-2 viral RNA copy numbers both in oropharyngeal specimens and in the histological analysis of the lung tissues along with humoral and cellular immune responses, including the neutralizing antibodies similar to those shown in BALB/c mice without substantial toxicity. Subsequently, plans are being made for the commencement of Phase 1 clinical trial of the OZG-38.61.3 vaccine for the COVID-19 pandemic.
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    Intramural Component of Venous, Lymphatic, and Perineural Invasion in Colon Cancer: A Threat or an Illusion?
    (GALENOS PUBL HOUSE, 2022-01-01) Ozer, Leyla; Tasci, Elif Senocak; Mutlu, Arda Ulas; Piyade, Betul; Ramoglu, Nur; Ajredini, Mirac; Gurleyik, Damla; Cecen, Recep; Dincer, Sena Nur; Musevitoglu, Turan; Goksel, Suha; Ince, Umit; Kayhan, Cavit Kerem; Erdamar, Sibel; Yildiz, Ibrahim; Aytac, Erman
    Background: Extramural venous invasion is an independent predictor of poor outcome in colorectal cancer, whereas the significance of the intramural component of venous and lymphatic and perineural invasion is unclear. Aims: To evaluate the prognostic impact of intramural components for venous, lymphatic, and perineural invasions and the relation of these invasion patterns with clinicopathological features in patients with colon cancer. Study Design: A retrospective cross-sectional study. Methods: The analysis included 626 patients with colon cancer in stages II and III. All patients were divided into four categories (no invasion, intramural invasion only, extramural invasion only, or both intramural and extramural invasions) for vascular invasion, lymphatic invasion and perineural invasion. The primary outcomes were 5-year disease-free and overall survival. Results: Right-sided (for vascular invasion, 24.7\% vs. 33.9\%, p = 0.007
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    Partial healing effects of St. John's wort oil on the rat excisional wound model
    (MARMARA UNIV, FAC MEDICINE, 2022-01-01) Atsu, Ayse Nilhan; Bilgic, Tayfun; Kayhan, Cavit Kerem; Saglam, Zumrut Mine Isik; Caf, Nazli
    Objective: St. John's wort (SJW) oil (Hypericum perforatum) has been used for its immunomodulatory and anti-inflammatory effects. Several studies have shown the efficacy of SJW on wound healing. The aim of this study is to assess the effectiveness of SJW using a combination of biochemical, histopathological and laser Doppler evaluations. Materials and Methods: Sixteen young Wistar albino rats were used as case and control groups (having 8 in each group). After anesthesia protocol, 6 mm punch biopsy was taken from six separate sites on the rats' dorsal skin. Three wounds were stitched (closed wounds)
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    Preclinical Assessment of Efficacy and Safety Analysis of CAR-T Cells (ISIKOK-19) Targeting CD19-Expressing B-Cells for the First Turkish Academic Clinical Trial with Relapsed/Refractory ALL and NHL Patients
    (GALENOS YAYINCILIK, 2020-01-01) Tastan, Cihan; Kancagi, Derya Dilek; Turan, Raife Dilek; Yurtsever, Bulut; Cakirsoy, Didem; Abanuz, Selen; Yilanci, Muhammet; Seyis, Utku; Ozer, Samed; Mert, Selin; Kayhan, Cavit Kerem; Tokat, Fatma; Elmas, Merve Acikel; Birdogan, Selcuk; Arbak, Serap; Yalcin, Koray; Sezgin, Aslihan; Kizilkilic, Ebru; Hemsinlioglu, Cansu; Ince, Umit; Ratip, Siret; Ovali, Ercument
    Objective: Relapsed and refractory CD19-positive B-cell acute lymphoblastic leukemia (ALL) and non-Hodgkin lymphoma (NHL) are the focus of studies on hematological cancers. Treatment of these malignancies has undergone recent transformation with the development of new gene therapy and molecular biology techniques, which are safer and well-tolerated therapeutic approaches. The CD19 antigen is the most studied therapeutic target in these hematological cancers. This study reports the results of clinical-grade production, quality control, and in vivo efficacy processes of ISIKOK-19 cells as the first academic clinical trial of CAR-T cells targeting CD19-expressing B cells in relapsed/refractory ALL and NHL patients in Turkey. Materials and Methods: We used a lentiviral vector encoding the CD19 antigen-specific antibody head (FMC63) conjugated with the CD8-CD28-CD3 zeta
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    MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue
    (SPRINGER LONDON LTD, 2022-01-01) Cayir, Sercan; Solmaz, Gizem; Kusetogullari, Huseyin; Tokat, Fatma; Bozaba, Engin; Karakaya, Sencer; Iheme, Leonardo Obinna; Tekin, Eren; Yazici, Cisem; Ozsoy, Gulsah; Ayalti, Samet; Kayhan, Cavit Kerem; Ince, Umit; Uzel, Burak; Kilic, Onur
    Mitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a 68.7\% F1-score and 49.0\% F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists' decision.