Acıbadem Üniversitesi Açık Erişim Sistemi
Not a member yet
2603 research outputs found
Sort by
International Association for the Study of Lung Cancer Study of the Impact of Coronavirus Disease 2019 on International Lung Cancer Clinical Trials
No full textIntroduction: To evaluate the effects of the global coronavirus disease 2019 (COVID-19) pandemic on lung cancer trials, we surveyed investigators and collected aggregate enrollment data for lung cancer trials across the world before and during the pandemic. Methods: A Data Collection Survey collected aggregate monthly enrollment numbers from 294 global lung cancer trials for 2019 to 2020. A 64-question Action Survey evaluated the impact of COVID-19 on clinical trials and identified mitigation strategies implemented. Results: Clinical trial enrollment declined from 2019 to 2020 by 14\% globally. Most reductions in enrollment occurred in April to June where we found significant decreases in individual site enrollment (p = 0.0309). Enrollment was not significantly different in October 2019 to December of 2019 versus 2020 (p = 0.25). The most frequent challenges identified by the Action Survey (N = 172) were fewer eligible patients (63\%), decrease in protocol compliance (56\%), and suspension of trials (54\%). Patient-specific challenges included access to trial site (49\%), ability to travel (54\%), and willingness to visit the site (59\%). The most frequent mitigation strategies included modified monitoring requirements (47\%), telehealth visits (45\%), modified required visits (25\%), mailorder medications (25\%), and laboratory (27\%) and radiology (21\%) tests at nonstudy facilities. Sites that felt the most effective mitigation strategies were telehealth visits (85\%), remote patient-reported symptom collection (85\%), off-site procedures (85\%), and remote consenting (89\%).Conclusions: The COVID-19 pandemic created many challenges for lung cancer clinical trials conduct and enrollment. Mitigation strategies were used and, although the pandemic worsened, trial enrollment improved. A more flexible approach may improve enrollment and access to clinical trials, even beyond the pandemic.(c) 2022 Published by Elsevier Inc. on behalf of International Association for the Study of Lung Cancer.5MAY651-6601
Limited field adaptive radiotherapy for glioblastoma: changes in target volume and organ at risk doses
No full textObjective: This study aimed to investigate the tumor volume changes occurring during limited-field radiotherapy (RT) for glioblastoma patients and whether a volume-adapted boost planning approach provided any benefit on tumor coverage and normal tissue sparing. Materials and Methods: Twenty-four patients underwent simulation with magnetic resonance (MR) and computed tomography (CT) scans prior to RT (MR\_initial, CT\_initial) and boost treatment (MR\_adapt, CT\_adapt). For the boost phase, MR\_initial and MR\_adapt images were used to delineate GTV(2) and GTV(2\_adapt), respectively. An initial boost plan (Plan\_initial) created on CT\_initial for PTV2 was then reoptimized on CT\_adapt by keeping the same optimization and normalization values. Plan\_adapt was generated on CT\_adapt for PTV(2\_adapt )volume. Dose volume histogram parameters for target volumes and organs-at-risk were compared using these boost plans generated on CT\_adapt. Plan\_initial and Plan\_adaptive boost plans were summed with the first phase plan and the effect on the total dose was investigated. Results: Target volume expansion was noted in 21 0 /c, of patients while 791b had shrinkage. The average difference for the initial and adaptive gross tumor volume (G1V), clinical target volume (CTV), and planning target volume (PTV) volumes were statistically significant. Maximum dose differences for brainstem and optic chiasm were significant. Healthy brain tissue V-10 and ipsilateral optic nerve maximum doses were found to decrease significantly in Plan\_adaptive. Conclusion: Results of this study confirm occurrence of target volume changes during RT for glioblastoma patients. An adaptive plan can provide better normal tissue sparing for patients with lesion shrinkage and avoid undercoverage of treatment volumes in case of target volume expansion especially when limited-fields are used.1MAR9-194
Long-term within- and between-subject biological variation of 29 routine laboratory measurands in athletes
No full textObjectives Within- and between-subject biological variation (BV) estimates have many applications in laboratory medicine. However, robust high-quality BV estimates are lacking for many populations, such as athletes. This study aimed to deliver BV estimates of 29 routine laboratory measurands derived from a Biological Variation Data Critical Appraisal Checklist compliant design in a population of high-endurance athletes. Methods Eleven samples per subject were drawn from 30 triathletes monthly, during a whole sport season. Serum samples were measured in duplicate for proteins, liver enzymes, lipids and kidney-related measurands on an Advia2400 (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CVI) and between-subject (CVG) biological variation estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and health related variables. Results Most CVI estimates were similar or only slightly higher in athletes compared to those reported for the general population, whereas two- to three-fold increases were observed for amylase, ALT, AST and ALP. No effect of exercise and health related variables were observed on the CVI estimates. For seven measurands, data were not homogeneously distributed and BV estimates were therefore not reported. Conclusions The observation of higher CVI estimates in athletes than what has been reported for the general population may be related to physiological stress over time caused by the continuous practice of exercise. The BV estimates derived from this study could be applied to athlete populations from disciplines in which they exercise under similar conditions of intensity and duration.4MAR 28618-6286
Gut immune homeostasis: the immunomodulatory role of Bacillus clausii, from basic to clinical evidence
No full textIntroduction The gut microbiota affects the development of the gut immune system in early life. Perturbations to microbiota structure and composition during this period can have long-term consequences on the health of the individual, through its effects on the immune system. Research in the last few decades has shown that probiotic administration can reverse these effects in strain- and environment-specific ways. Bacillus clausii (B. clausii) has been in use for many decades as a safe and efficacious probiotic, but its mode of action has not yet been completely elucidated. Areas covered In this review, we discuss how the gut immune system works, the factors that affect its functioning, and the plethora of research highlighting its role in various diseases. We also discuss the known modes of action of Bacillus probiotics, and highlight the preclinical and clinical evidence that reveal how B. clausii acts to bolster gut defense. Expert opinion We anticipate that the treatment and/or prevention of dysbiosis will be central to managing human health and disease in the future. Discovering the pathophysiology of autoimmune diseases, infections, allergies, and some cancers will aid our understanding of the key role played by microbial communities in these diseases.7JUL 3717-7291
Genetic landscape of pancreatic cancer: a narrative review
No full textBackground and Objective: Pancreatic cancer is an aggressive disease with an impaired survival despite improvements in clinical management. Thus, understanding disease biology is of vital importance in order to overcome therapeutic challenges and achieve better prognosis. The purpose of this review is to outline the genetic landscape of pancreatic cancer along with its clinical implications. Methods: We reviewed existing literature using electronic databases to outline the genetic landscape in pancreatic cancer. Key Content and Findings: This review mainly contains information on the genetic background of pancreatic cancer, mainly KRAS, CDKN2A, TP53 and SMAD4, with emphasis on the importance of understanding disease biology. Conclusions: The genetic aspects of pancreatic cancer have been well described especially with the introduction of next generation sequencing techniques. Future studies focusing on translation of these alterations in clinical application might pave the way for personalized surveillance and therapy.1FEB1
İndometazin ile indüklenen gastrik ülser modelinde ferulik asitin inflamasyon ve oksidatif stres üzerine etkisinin araştırılması
No full textYeni geliştirilen terapötik monoklonal antikorların fizikokimyasal özelliklerinin ve stres koşullarındaki değişikliklerinin değerlendirilmesi ve validasyonu
No full textMITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue
No full textMitosis 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.20OCT17837-178513