Zeynep Aydogmus | Pharmaceutical Analysis | Editorial Board Member

Published on by Top Pharmaceutical

Prof. Dr. Zeynep Aydogmus | Pharmaceutical Analysis | Editorial Board Member

Istanbul University | Turkey

Prof. Dr. Zeynep Aydoğmuş is a distinguished scholar in analytical chemistry and pharmaceutical sciences, widely recognized for her pioneering contributions to chromatographic analysis, electroanalytical methods, and pharmaceutical analysis. She began her academic journey at Istanbul University, Faculty of Pharmacy, where she completed her undergraduate, postgraduate, and doctoral studies in Analytical Chemistry. Her academic foundation laid the groundwork for a career devoted to advancing separation sciences, spectroscopical techniques, pharmaceutical analysis, and natural product chemistry. Over the course of her professional development, she enriched her expertise through international training in occupational health, radiation safety, bloodborne pathogen standards, and advanced chromatographic techniques, while also securing specialized certification in European project management.Prof. Dr. Aydoğmuş has built a prominent academic career at the Istanbul University Faculty of Pharmacy, where she has played an integral role within the Department of Basic Pharmaceutical Sciences. She has taught across all academic levels, offering advanced courses in electroanalytical methods, Thin Layer Chromatography (TLC), High-Performance Liquid Chromatography (HPLC), chromatographic method development, analytical chemistry, and research methodologies. Her academic influence extends beyond the classroom through her active participation on doctoral examination juries and her dedicated mentorship of emerging scientists.Her research portfolio spans a wide spectrum of health and natural sciences, including pharmacology and therapeutics, basic pharmaceutics, pharmacognosy, separation and electromagnetic methods, and chromatographic and spectroscopic analysis. Prof. Dr. Aydoğmuş has made significant contributions to studies on antiviral, anticancer, and anti-inflammatory drugs, natural compounds, and bioactive metabolites. Her collaborations have led to impactful publications in internationally indexed journals, reflecting her expertise in voltammetry, UHPLC, drug stability analysis, electrochemical behavior, and analytical method development for pharmaceutical formulations and biological matrices.

Profile: Google Scholar

Featured Publications

Gören, A. C., Topçu, G., Bilsel, G., Bilsel, M., Aydoğmuş, Z., & Pezzuto, J. M.
The chemical constituents and biological activity of essential oil of Lavandula stoechas ssp. stoechas. Zeitschrift für Naturforschung C, 57(9–10), 797–800.

Gönüllü, Ü., Üner, M., Yener, G., Karaman, E. F., & Aydoğmuş, Z.
Formulation and characterization of solid lipid nanoparticles, nanostructured lipid carriers and nanoemulsion of lornoxicam for transdermal delivery. Acta Pharmaceutica, 65(1), 1–13.

Topcu, G., Aydogmus, Z., Imre, S., Gören, A. C., Pezzuto, J. M., Clement, J. A., et al.
Brominated sesquiterpenes from the red alga Laurencia obtusa. Journal of Natural Products, 66(11), 1505–1508.

Zhang, H., Qiu, S., Tamez, P., Tan, G. T., Aydogmus, Z., Hung, N. V., Cuong, N. M., et al.
Antimalarial agents from plants II. Decursivine, a new antimalarial indole alkaloid from Rhaphidophora decursiva. Pharmaceutical Biology, 40(3), 221–224.

Üner, M., Karaman, E. F., & Aydoğmuş, Z.
Solid lipid nanoparticles and nanostructured lipid carriers of loratadine for topical application: Physicochemical stability and drug penetration through rat skin. Tropical Journal of Pharmaceutical Research, 13(5), 653–660.

Zhang, H. J., Tamez, P. A., Aydogmus, Z., Tan, G. T., Saikawa, Y., Hashimoto, K., et al.
Antimalarial agents from plants III. Trichothecenes from Ficus fistulosa and Rhaphidophora decursiva. Planta Medica, 68(12), 1088–1091.

Topcu, G., Altiner, E. N., Gozcu, S., Halfon, B., Aydogmus, Z., Pezzuto, J. M., et al.
Studies on di- and triterpenoids from Salvia staminea with cytotoxic activity. Planta Medica, 69(5), 464–467.

Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

Published on by Top Pharmaceutical

Prof. Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

Isfahan University of Technology | Iran

Prof. Mehdi Khashei is a distinguished scholar in Industrial and Systems Engineering whose extensive academic and research contributions have positioned him as a leading expert in intelligent modeling, hybrid forecasting systems, and data-driven decision-making. He completed his academic journey with a strong mathematical and computational foundation, beginning with a Bachelor’s degree in Applied Mathematics and Computer Science from the University of Isfahan. Building on this analytical background, he pursued advanced studies at the Isfahan University of Technology, earning both his Master’s and Ph.D. in Industrial and Systems Engineering, where he focused on forecasting, analysis of industrial systems, and the integration of artificial intelligence into complex decision environments. His Post-Doctorate research at the same institution further advanced theoretical developments in Modeling Spaces Continuity, reinforcing his expertise in the intersection of systems theory, soft computing, and intelligent analytics.Throughout his academic career, Prof. Khashei has pioneered numerous innovative methodologies in the fields of time series forecasting, intelligent classification, hybrid modeling, and decision science. His research is particularly recognized for introducing discrete learning-based intelligent algorithms, reliability-based hybrid models, and advanced statistical-intelligent approaches for forecasting, classification, and optimization across diverse domains. His work spans energy systems, medicine, chemometrics, financial time series, predictive maintenance, load forecasting, and biomedical decision-making, demonstrating a unique capacity to bridge theory with real-world applications.Prof. Khashei has authored and co-authored an extensive portfolio of high-impact publications across leading international journals. His studies present cutting-edge solutions to challenges in artificial neural networks, fuzzy hybrid models, regression systems, clinical diagnostics, optimization strategies, and intelligent data preprocessing. His contributions have significantly shaped modern approaches to forecasting through hybridization frameworks such as series-parallel models, fuzzy-intelligent seasonal systems, and reliability-based methodologies. With groundbreaking insights into energy classification, breast cancer detection, heart disease diagnosis, and optimal financial turning point detection, his work continues to influence both academic research and applied industry practices.Prof. Mehdi Khashei is widely respected for his scientific rigor, interdisciplinary expertise, and commitment to advancing intelligent systems that enhance decision-making in complex, uncertain environments. His legacy is reflected not only in his prolific scholarly contributions but also in the transformative impact of his methodologies across engineering, computational intelligence, and data-driven industries.

Profile: Google Scholar

Featured Publications

Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for time series forecasting. Expert Systems with Applications, 37(1), 479‑489.

Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664‑2675.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2009). Improvement of auto‑regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing, 72(4‑6), 956‑967.

Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems, 159(7), 769‑786.

Hajirahimi, Z., & Khashei, M. (2019). Hybrid structures in time series modeling and forecasting: A review. Engineering Applications of Artificial Intelligence, 86, 83‑106.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344‑4357.

Khashei, M., Hamadani, A. Z., & Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39(3), 2606‑2620.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63(1), 37‑45.

Khashei, M., & Hajirahimi, Z. (2019). A comparative study of series ARIMA/MLP hybrid models for stock price forecasting. Communications in Statistics – Simulation and Computation, 48(9), 2625‑2640.