Zirve University

About: Zirve University is a education organization based out in Gaziantep, Turkey. It is known for research contribution in the topics: Cellular automaton & Inverse problem. The organization has 177 authors who have published 475 publications receiving 5914 citations.

Systematic Exploration of Synergistic Drug Pairs

Abstract: Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, ‘promiscuous synergy’ can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs inS. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

Abstract: Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal. For this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed. QDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM. This study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.

The effects of Nigella sativa (Ns), Anthemis hyalina (Ah) and Citrus sinensis (Cs) extracts on the replication of coronavirus and the expression of TRP genes family

Abstract: Extracts of Anthemis hyalina (Ah), Nigella sativa (Ns) and peels of Citrus sinensis (Cs) have been used as folk medicine to fight antimicrobial diseases. To evaluate the effect of extracts of Ah, Ns and Cs on the replication of coronavirus (CoV) and on the expression of TRP genes during coronavirus infection, HeLa-CEACAM1a (HeLa-epithelial carcinoembryonic antigen-related cell adhesion molecule 1a) cells were inoculated with MHV-A59 (mouse hepatitis virus–A59) at moi of 30. 1/50 dilution of the extracts was found to be the safe active dose. ELISA kits were used to detect the human IL-8 levels. Total RNA was isolated from the infected cells and cDNA was synthesized. Fluidigm Dynamic Array nanofluidic chip 96.96 was used to analyze the mRNA expression of 21 TRP genes and two control genes. Data was analyzed using the BioMark digital array software. Determinations of relative gene expression values were carried out by using the 2−∆∆Ct method (normalized threshold cycle (Ct) value of sample minus normalized Ct value of control). TCID50/ml (tissue culture infectious dose that will produce cytopathic effect in 50 % of the inoculated tissue culture cells) was found for treatments to determine the viral loads. The inflammatory cytokine IL-8 level was found to increase for both 24 and 48 h time points following Ns extract treatment. TRPA1, TRPC4, TRPM6, TRPM7, TRPM8 and TRPV4 were the genes which expression levels changed significantly after Ah, Ns or Cs extract treatments. The virus load decreased when any of the Ah, Ns or Cs extracts was added to the CoV infected cells with Ah extract treatment leading to undetectable virus load for both 6 and 8hpi. Although all the extract treatments had an effect on IL-8 secretion, TRP gene expression and virus load after CoV infection, it was the Ah extract treatment that showed the biggest difference in virus load. Therefore Ah extract is the best candidate in our hands that contains potential treatment molecule(s).

DC-to-DC Converter With Low Input Current Ripple for Maximum Photovoltaic Power Extraction

Abstract: This paper presents a dc-to-dc converter, which offers continuous input and output energy flow and low input current ripple, applicable and mandatory for photovoltaic (PV) arrays and maximum power tracking applications. The PV array yields exponential curves for current and voltage where maximum power occurs at the curve's mutual knee. Conventional dc-to-dc converters have a relatively high input current ripple which causes high power losses when connected to nonlinear sources like PV arrays. The proposed converter maximizes the power that can be sourced from the PV array, without the need of any electrolytic filtering capacitance. The effect of current ripple can be significant and decreases PV system efficiency. Converter simulations and experimental results support and extol the system concept.