A Comparison of Ramipril and Bevacizumab to Mitigate Radiation-Induced Brain Necrosis: An Experimental Study

Background: Bevacizumab, an anti-vascular endothelial growth factor (VEGF) antibody, is a new treatment approach for radionecrosis. In our study, we compared the prophylactic and therapeutic usage of a promising agent, ramipril (an angiotensin-converting enzyme inhibitor), with that of bevacizumab for reducing radiation-induced brain injury after high-dose stereotactic radiosurgery (SRS). Methods: A total of 60 Wistar rats were used. The rats were irradiated with a single dose of 50 Gy using a Leksell Gamma Knife device. Bevacizumab and ramipril were administered in the prophylactic protocol (starting the first day of SRS) and in the therapeutic protocol (starting the fourth week of SRS). Their usage was continued until 12 weeks, and the right frontal lobes of the rats were examined histologically (hematoxylin and eosin stain) and immunohistochemically (hypoxia-inducible factor [HIF]-1α, VEGF, and CD31 antibody expression). Results: The expression of VEGF, HIF-1α, and CD31 had significantly increased at 12 weeks after SRS compared with the control group. The addition of bevacizumab or ramipril to SRS significantly mitigated the histological severity of radiation injury and the expression of VEGF, HIF-1α, and CD31. However, the prophylactic use of bevacizumab and ramipril seemed to be more effective than therapeutic administration. Our results also revealed that the greatest benefit was achieved with the use of prophylactic administration of bevacizumab compared with other treatment protocols. Conclusions: Ramipril might be a promising agent for patients with radionecrosis. Clinical studies are required to investigate the effective and safe doses of ramipril, which is an inexpensive, well-tolerated drug that can cross the blood–brain barrier. © 2020 Elsevier Inc

The possible mechanisms of the human microbiome in allergic diseases

WOS: 000394351800003PubMed: 27115907In the present paper, we discuss the importance of the microbiome in allergic disease. In this review paper, the data from the Medline (PubMed) and search engine of Kirikkale University were systematically searched for all relevant articles in June 15th, 2015 for the past 30 years. The keywords of "microbiome'', "dysbiosis'', "allergy'', "allergic rhinitis'', "allergic disease'', "mechanisms'' and "treatment'' were used alone or together. In this paper, microbiomes were presented in terms of "Definition'', "Influence of \the human microbiome on health'', "The microbiome and allergic diseases'', and "Modulation of the gut microbiota in terms of treatment and prevention''. Microbiological dysbiosis is also reviewed. The microbiome is the genetic material of all microbes (bacteria, fungi, protozoa, and viruses) that live on or in the human body. Microbes outnumber human cells in a 10: 1 ratio. Most microbes live in the gut, particularly the large intestine. Changes in the immune function of the respiratory tract are (at least in theory) linked to the immunomodulatory activity of the gut microbiota via the concept of a "common mucosal response''. The gut microbiota shapes systemic immunity, thus affecting the lung mucosa. Alternatively, changes in the gut microbiota may reflect alterations in the oropharyngeal microbiota, which may in turn directly affect the lung microbiota and host immune responses via microaspiration. Dysbiosis is defined as qualitative and quantitative changes in the intestinal flora; and modern diet and lifestyle, antibiotics, psychological and physical stress result in alterations in bacterial metabolism, as well as the overgrowth of potentially pathogenic microorganisms. All immune system components are directly or indirectly regulated by the microbiota. The nature of microbial exposure early in life appears to be important for the development of robust immune regulation; disruption of either the microbiota or the host response can trigger chronic inflammation. Dysbiosis is also an important clinical entity. Antibiotics, psychological and physical stress, and dietary factors contribute to intestinal dysbiosis

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software