Protective Effect of Carvacrol against Paclitaxel-Induced Ototoxicity in Rat Model

Objective: This study aimed to explore whether carvacrol (CV) had a protective effect on paclitaxel-induced ototoxicity from biochemical, functional, and histopathological perspectives.Methods: Forty Wistar albino male rats were randomly separated into five groups of eight rats. Group 1 was the control group, so Paclitaxel or CV was not administered. Group 2 was administered i.p. CV at 25 mg/kg once a week; Group 3, was administered i.p. paclitaxel at 5 mg/kg once a week; Group 4 was administered i.p. paclitaxel at 5 mg/kg followed (30 min later) by CV at 25 mg/kg once a week; and Group 5 was administered i.p. CV at 25 mg/kg followed (1 day later) by paclitaxel at 5 mg/kg. once a week. The drugs were administered intraperitoneally once a week for four consecutive weeks, and distortion product otoacoustic emissions (DPOAE) tests were performed at the beginning of the study before the first drug administration and at the end of the study after the last drug administration. All rats were sacrificed, and cochleae were removed for biochemical and histopathological analysis.Results: Biochemical data indicated that paclitaxel caused oxidative stress in the cochlea. Histopathological findings revealed the loss of outer hair cells in the organ of Corti (CO) and moderate degenerative changes in the stria vascularis (SV). It was observed that DPOAE measurements were significantly reduced at high frequencies. In groups which CV was administered together with paclitaxel, these biochemical, histopathological, and functional changes were favorably reversed.Conclusion: CV may have a protective effect against paclitaxel-induced ototoxicity when given

The effect of additional protein support ın hospitalized unconscious elderly malnourished patients receiving enteral nutrition.

TEZ9022Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2008.Kaynakça (s. 93-104) var.xi, 118 s. : res. ; 29 cm.Malnutrition in unconscious patients has a risk of increased morbidity, mortality and extended hospital stays. The objective of this study is examine the influence of additional protein support in hospitalized unconscious elderly malnourished patients receiving enteral nutrition. A total of 20 patients were recruited. All patients were feed by a standart enteral nutrition formula during the 3 days adaptation period. The study group(n=10), received additional protein support (2g/kg/day protein) and the control group (n=10) received only standart enteral nutrition (1g/kg/day protein) during the 12 days study period. Nutrition status was assessed 3 times with anthropometric measurements, biochemical and ürine parameters. These results were compared between these two groups of patients and changes in these parameters during the adaptation and sudy period were evaluated. The subjects' mean age was 77.45=9.45(60-93) year in all 20 patients; the mean age was 76.70=8.78 year in the study group, 78.20±10.49 year in the control group (p>0.05). There was no statistically significant difference between the study and control groups with regard to the anthropometric measurements and serum proteins average values (p>0.05). The changes in serum protein levels during the adaptation and study period were no significant at the study group (p>0.05). Although there was no statistical difference, when the changes in the average values of anthropometric measurements, serum IGF-1, RBP and prealbumin values were examined graphically the average values were increased in the study group at the end of the study compared to day 0 (p0.05). Ek protein ve kontrol grubu hastaların antropometrik ölçümler ve serum proteinlerinin ortalama değerleri arasında önemli bir fark bulunmamıştır (p>0.05). Ek protein desteği alan hastaların serum protein değerlerinde günlere göre önemli bir farklılık görülmemiştir (p>0.05). Ancak, istatistiksel olarak bir fark bulunmamasına rağmen ek protein alan hastaların antropometrik ölçüm ve serum IGF-1, RBP ve prealbümin ortalama değerlerinde başlangıca göre çalışma bitiminde görülen değişimler grafiksel olarak incelendiğinde bir artış olduğu görülmektedir. Kontrol grubundaki hastaların günlere göre serum albümin düzeyinde, önemli bir azalma görülmektedir (p<0.05). Bu araştırmada, araştırmaya alınan hastaların, hastalık durumları ve malnütrisyon varlığının sağlık durumu üzerine etkili olduğu, beslenme destek tedavisinde ek proteinin, beslenme parametrelerini olumlu yönde etkileyebileceği görülmüştür.Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir. Proje No: ZF2004D37

Prediction of Intramolecular Reorganization Energy Using Machine Learning

Facile charge transport is desired for many applications of organic semiconductors (OSCs). To take advantage of high-throughput screening methodologies for the discovery of novel OSCs, parameters relevant to charge transport are of high interest. The intramolecular reorganization energy (RE) is one of the important charge transport parameters suitable for molecular-level screening. Because the calculation of the RE with quantum-chemical methods is expensive for large-scale screening, we investigated the possibility of predicting the RE from the molecular structure by means of machine learning methods. We combinatorially generated a molecular library of 5631 molecules with extended conjugated backbones using benzene, thiophene, furan, pyrrole, pyridine, pyridazine, and cyclopentadiene as building blocks and obtained the target electronic data at the B3LYP level of theory with the 6-31G* basis set. We compared ridge, kernel ridge, and deep neural net (DNN) regression models based on graph- and geometry-based descriptors. We found that DNNs outperform the other methods and can predict the RE with a coefficient of determination of 0.92 and root-mean-square error of similar to 12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy.We thank Secil Usta and Simla B. Harma for help with DNN scripting and Isiksu Eksioglu for useful discussions regarding the use of the Keras python deep learning library. S.A.-E. acknowledges financial support from The Scientific and Technological Research Council of Turkey (Ardeb 3001 Programme, Grant 216Z096), software support from Chem Axon Ltd., and support from NVIDLA Corporation through the donation of the Titan Xp GPU used for this research

Prediction of Intramolecular Reorganization Energy Using Machine Learning

Facile charge transport is desired for many applications of organic semiconductors (OSCs). To take advantage of high-throughput screening methodologies for the discovery of novel OSCs, parameters relevant to charge transport are of high interest. The intramolecular reorganization energy (RE) is one of the important charge transport parameters suitable for molecular-level screening. Because the calculation of the RE with quantum-chemical methods is expensive for large-scale screening, we investigated the possibility of predicting the RE from the molecular structure by means of machine learning methods. We combinatorially generated a molecular library of 5631 molecules with extended conjugated backbones using benzene, thiophene, furan, pyrrole, pyridine, pyridazine, and cyclopentadiene as building blocks and obtained the target electronic data at the B3LYP level of theory with the 6-31G* basis set. We compared ridge, kernel ridge, and deep neural net (DNN) regression models based on graph- and geometry-based descriptors. We found that DNNs outperform the other methods and can predict the RE with a coefficient of determination of 0.92 and root-mean-square error of similar to 12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy.We thank Secil Usta and Simla B. Harma for help with DNN scripting and Isiksu Eksioglu for useful discussions regarding the use of the Keras python deep learning library. S.A.-E. acknowledges financial support from The Scientific and Technological Research Council of Turkey (Ardeb 3001 Programme, Grant 216Z096), software support from Chem Axon Ltd., and support from NVIDLA Corporation through the donation of the Titan Xp GPU used for this research

Bounds on the cost of compatible refinement of simplex decomposition trees in arbitrary dimensions

A hierarchical simplicial mesh is a recursive decomposition of space into cells that are simplices. Such a mesh is compatible if pairs of neighboring cells meet along a single common face. Compatibility condition is important in many applications where the mesh serves as a discretization of a function. Enforcing compatibility involves refining the simplices further if they share split faces with their neighbors, thus generates a larger mesh. We prove a tight upper bound on the expansion factor for 2-dimensional meshes, and show that the size of a simplicial subdivision grows by no more than a constant factor when compatibly refined. We also prove upper bounds for d-dimensional meshes. (C) 2019 Elsevier B.V. All rights reserved

Convex Hull for Probabilistic Points

29th SIBGRAPI Conference on Graphics, Patterns and Images (2016 : Sao Paulo; Brazil)We analyze the correctness of an O(n log n) time divide-and-conquer algorithm for the convex hull problem when each input point is a location determined by a normal distribution. We show that the algorithm finds the convex hull of such probabilistic points to precision within some expected correctness determined by a user-given confidence value phi In order to precisely explain how correct the resulting structure is, we introduce a new certificate error model for calculating and understanding approximate geometric error based on the fundamental properties of a geometric structure. We show that this new error model implies correctness under a robust statistical error model, in which each point lies within the hull with probability at least phi, for the convex hull problem.IBM,NVIDI