Category: Papers

Paper “Interactions of zinc aqua complexes with ovalbumin at the forefront of the Zn²⁺/ZnO-OVO hybrid complex formation mechanism” published in Applied Surface Science. Experimental and theoretical approaches were used for a mechanistic study of the nanocomplexes formation through  Zn²⁺_(aq)-ovalbumin (OVA) binding. The determination of processes occurring at Zn²⁺_(aq)-OVA interfaces and mechanism of hybrid complexes formation involved the modeling of kinetics and isotherm of Zn²⁺_(aq) binding. Scanning electron microscopy, energy-dispersive X-ray analysis and X-ray diffraction were employed to characterize the nano-crystalline structure and morphology of the Zn²⁺/ZnO-OVO hybrid complex. Formation of hybrid complex was monitored using Fourier transform infrared spectroscopy. Specific Zn²⁺_(aq) binding sites were pointed using MD simulations. Large-scale MD analysis was carried out to sample conformational space of the formed Zn²⁺_(aq)-OVA complexes. Density functional theory (DFT) calculations were used to investigate the structures of the interaction complexes between zinc(II) and Asp⁻/Glu⁻ and predict their respective IR spectra, and for mechanistic modeling of oxidation processes involved in the formation and stabilization Zn²⁺/ZnO-OVO hybrid complex. Negatively charged ovalbumin surface was locally neutralized through rapid Zn²⁺_(aq) adsorption and formation of monolayers via 1:1 Asp⁻/Glu⁻/-Zn²⁺_(aq) complexes. The ovalbumin monomer species began to agglomerate, stabilized with Asp⁻/Glu⁻-Zn²⁺_(aq)-Glu⁻/Asp⁻ interface complexes that promoted the formation of nanocomplexes. Finally, antimicrobial assays have shown that Zn²⁺/ZnO-OVO hybrid complex exhibit an inhibition effect against bacteria (A. baumanii, K. pneumonia) and yeast (C. albicans) strains.

Paper “Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships” published in Applied Surface Science. Linear and non-linear quantitative structure–property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods.

Paper: “Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory” published in the International Journal of Molecular Sciences. This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical ‘black box’ approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

Paper: “Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System” published in Industrial & Engineering Chemistry Research. Ensemble learning in an industrial inspection of glass substrates in a TFT-LCD production line. The paper presents solutions in competing objectives, class imbalance, and a limited number of samples.

Paper “Fiber-optic Raman Spectroscopy with Nature-inspired Genetic Algorithms Enhances Real-time In Vivo Detection And Diagnosis of Nasopharyngeal Carcinoma” published in the journal Analytical Chemistry. Raman spectroscopy is an optical vibrational spectroscopic technique capable of probing specific biochemical structures and conformation of tissue and cells in biomedical systems. This work aims to assess the clinical utility of a fiber-optic Raman spectroscopy with nature-inspired genetic algorithms for enhancing in vivo detection and diagnosis of nasopharyngeal carcinoma (NPC) patients. The Raman diagnostic platform is developed based on simultaneous fingerprint (FP) and high-wavenumber (HW) fiber-optic Raman endoscopy associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA). A total of 2126 in vivo FP/HW Raman spectra (598 NPC, 1528 normal) acquired from 113 tissue sites of 14 NPC patients and 48 healthy subjects during nasopharyngeal endoscopic examinations. Distinct Raman peaks have been identified (853 cm^–1 – proteins, 1209 cm^–1 – phenylalanine, 1265 cm^–1 – proteins, 1335 cm^–1 – proteins and nucleic acids, 1554 cm^–1 – tryptophan, porphyrin, 2885 cm^–1 – lipids, 2940 cm^–1 – proteins, 3009 cm^–1 – lipids, and 3250 cm^–1 – water) that are related to the significant biochemical changes (p < 1 × 10^–5) in NPC compared to normal tissue. Raman diagnostic performance is evaluated through the leave-one-object (tissue site)-out cross-validation (LOOCV) method. A statistically significant GA-PLS-LDA model (p < 1 × 10^–5) on FP/HW Raman yields a CV diagnostic accuracy of 98.23% (111/113), sensitivity of 93.33% (28/30), and specificity of 100% (83/83) for NPC classification. This work demonstrates that the fiber-optic FP/HW Raman diagnostic platform developed has great promise for improving real-time in vivo detection and diagnosis of NPC at the molecular level during clinical nasopharyngeal endoscopy.

The review article on: “Column selection and characterization systems in reversed-phase high-performance liquid chromatography” co-authored by myself, Magdalena Skoczylas (PhD candidate), my PhD advisor Prof. Dr. J. Jay Liu (유준), long-time collaborators Prof. Dr. Tomasz Bączek, Prof. Dr. Roman Kaliszan, Prof. Dr. Bogusław Buszewski, and Prof. Dr. Richard Wong, has been published in the prestigious journal Chemical Reviews. If anyone would like to read the full-text, please PM or e-mail me for a copy.