Über den Resonanz fall bei Systemen von n nichtlinearen gewöhnlichen Differentialgleichungen erster Ordnung

Ohne Zusammenfassung Vorgelegt von L. Collatz II. Teil einer bei der Naturwissenschaftlich-Philosophischen Fakultät der Technischen Hochschule zu Braunschweig eingereichten Dissertation. Referenten: Prof. Dr. R. Iglisch und Prof. Dr. H.-J. Kanold. Auch Herrn Dr. H. Eltermann bin ich für wertvolle Ratschläge zu großem Dank verpflichtet.     

Microscale Thermophoresis and Molecular Modelling to Explore the Chelating Drug Transportation in the Milk to Infant

The microscale thermophoresis (MST) technique was utilized to investigate lactoferrin–drug interaction with the iron chelator, deferiprone, using label-free system. MST depends on the intrinsic fluorescence of one interacting partner. The results indicated a significant interaction between lactoferrin and deferiprone. The estimated binding constant for the lactoferrin–deferiprone interaction was 8.9 × 10⁻⁶ ± 1.6, SD, which is to be reported for the first time. Such significant binding between lactoferrin and deferiprone may indicate the potentiation of the drug secretion into a lactating mother’s milk. The technique showed a fast and simple approach to study protein–drug interaction while avoiding complicated labeling procedures. Moreover, the binding behavior of deferiprone within the binding sites of lactoferrin was investigated through molecular docking which reflected that deferiprone mediates strong hydrogen bonding with ARG121 and ASP297 in pocket 1 and forms H-bond and ionic interaction with ASN640 and ASP395, respectively, in pocket 2 of lactoferrin. Meanwhile, iron ions provide ionic interaction with deferiprone in both of the pockets. The molecular dynamic simulation further confirmed that the binding of deferiprone with lactoferrin brings conformational changes in lactoferrin that is more energetically stable. It also confirmed that deferiprone causes positive correlation motion in the interacting residues of both pockets, with strong negative correlation motion in the loop regions, and thus changes the dynamics of lactoferrin. The MM-GBSA based binding free energy calculation revealed that deferiprone exhibits ∆G TOTAL of −63,163 kcal/mol in pocket 1 and −63,073 kcal/mol in pocket 2 with complex receptor–ligand difference in pocket 1 and pocket 2 of −117.38 kcal/mol and −111.54 kcal/mol, respectively, which in turn suggests that deferiprone binds more strongly in the pocket 1. The free energy landscape of the lactoferrin–deferiprone complex also showed that this complex remains in a high energy state that confirms the strong binding of deferiprone with the lactoferrin. The current research concluded that iron-chelating drugs (deferiprone) can be transported from the mother to the infant in the milk because of the strong attachment with the lactoferrin active pockets.     

Optimized Green Extraction of Polyphenols from Cassia javanica L. Petals for Their Application in Sunflower Oil: Anticancer and Antioxidant Properties

The total phenolic content (TPC) from Cassia javanica L. petals were extracted using ethanolic solvent extraction at concentrations ranging from 0 to 90% and an SCF-CO₂ co-solvent at various pressures. Ultrasound-assisted extraction parameters were optimized using response surface methodology (RSM). Antioxidant and anticancer properties of total phenols were assessed. An SCF-CO₂ co-solvent extract was nano-encapsulated and applied to sunflower oil without the addition of an antioxidant. The results indicated that the best treatment for retaining TPC and total flavonoids content (TFC) was SCF-CO₂ co-solvent followed by the ultrasound and ethanolic extraction procedures. Additionally, the best antioxidant activity by β-carotene/linoleic acid and DPPH free radical-scavenging test systems was observed by SCF-CO₂ co-solvent then ultrasound and ethanolic extraction methods. SCF-CO₂ co-solvent recorded the highest inhibition % for PC3 (76.20%) and MCF7 (98.70%) and the lowest IC₅₀ value for PC3 (145 µ/mL) and MCF7 (96 µ/mL). It was discovered that fortifying sunflower oil with SCF-CO₂ co-solvent nanoparticles had a beneficial effect on free fatty acids and peroxide levels. The SCF-CO₂ method was finally found to be superior and could be used in large-scale processing.     

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

Design,Synthesis, Docking,DFT, MD Simulation Studies of a New Nicotinamide-Based Derivative: In Vitro Anticancer and VEGFR-2 Inhibitory Effects

A nicotinamide-based derivative was designed as an antiproliferative VEGFR-2 inhibitor with the key pharmacophoric features needed to interact with the VEGFR-2 catalytic pocket. The ability of the designed congener ((E)-N-(4-(1-(2-(4-benzamidobenzoyl)hydrazono)ethyl)phenyl)nicotinamide), compound 10, to bind with the VEGFR-2 enzyme was demonstrated by molecular docking studies. Furthermore, six various MD simulations studies established the excellent binding of compound 10 with VEGFR-2 over 100 ns, exhibiting optimum dynamics. MM-GBSA confirmed the proper binding with a total exact binding energy of −38.36 Kcal/Mol. MM-GBSA studies also revealed the crucial amino acids in the binding through the free binding energy decomposition and declared the interactions variation of compound 10 inside VEGFR-2 via the Protein–Ligand Interaction Profiler (PLIP). Being new, its molecular structure was optimized by DFT. The DFT studies also confirmed the binding mode of compound 10 with the VEGFR-2. ADMET (in silico) profiling indicated the examined compound’s acceptable range of drug-likeness. The designed compound was synthesized through the condensation of N-(4-(hydrazinecarbonyl)phenyl)benzamide with N-(4-acetylphenyl)nicotinamide, where the carbonyl group has been replaced by an imine group. The in-vitro studies were consonant with the obtained in silico results as compound 10 prohibited VEGFR-2 with an IC₅₀ value of 51 nM. Compound 10 also showed antiproliferative effects against MCF-7 and HCT 116 cancer cell lines with IC₅₀ values of 8.25 and 6.48 μM, revealing magnificent selectivity indexes of 12.89 and 16.41, respectively.     

Ameliorative Effect of Ocimum forskolei Benth on Diabetic,Apoptotic, and Adipogenic Biomarkers of Diabetic Rats and 3T3-L1 Fibroblasts Assisted by In Silico Approach

Diabetes mellitus (DM) is a complicated condition that is accompanied by a plethora of metabolic symptoms, including disturbed serum glucose and lipid profiles. Several herbs are reputed as traditional medicine to improve DM. The current study was designed to explore the chemical composition and possible ameliorative effects of Ocimum forskolei on blood glucose and lipid profile in high-fat diet/streptozotocin-induced diabetic rats and in 3T3-L1 cell lines as a first report of its bioactivity. Histopathological study of pancreatic and adipose tissues was performed in control and treatment groups, along with quantification of glucose and lipid profiles and the assessment of NF-κB, cleaved caspase-3, BAX, and BCL2 markers in rat pancreatic tissue. Glucose uptake, adipogenic markers, DGAT1, CEBP/α, and PPARγ levels were evaluated in the 3T3-L1 cell line. Hesperidin was isolated from total methanol extract (TME). TME and hesperidin significantly controlled the glucose and lipid profile in DM rats. Glibenclamide was used as a positive control. Histopathological assessment showed that TME and hesperidin averted necrosis and infiltration in pancreatic tissues, and led to a substantial improvement in the cellular structure of adipose tissue. TME and hesperidin distinctly diminished the mRNA and protein expression of NF-κB, cleaved caspase-3, and BAX, and increased BCL2 expression (reflecting its protective and antiapoptotic actions). Interestingly, TME and hesperidin reduced glucose uptake and oxidative lipid accumulation in the 3T3-L1 cell line. TME and hesperidin reduced DGAT1, CEBP/α, and PPARγ mRNA and protein expression in 3T3-L1 cells. Moreover, docking studies supported the results via deep interaction of hesperidin with the tested biomarkers. Taken together, the current study demonstrates Ocimum forskolei and hesperidin as possible candidates for treating diabetes mellitus.     

Investigation of proton-proton short-range correlations via the 12C(e,e'pp) reaction

We investigated simultaneously the 12C(e,e'p) and 12C(e,e'pp) reactions at Q2=2 (GeV/c)2, xB=1.2, and in an (e, e'p) missing-momentum range from 300 to 600 MeV/c. At these kinematics, with a missing momentum greater than the Fermi momentum of nucleons in a nucleus and far from the delta excitation, short-range nucleon-nucleon correlations are predicted to dominate the reaction. For (9.5+/-2)% of the 12C(e,e'p) events, a recoiling partner proton was observed back-to-back to the 12C(e,e'p) missing-momentum vector, an experimental signature of correlations.     

Influence of Intrinsic Decoherence in the Presence of Stark Shift on Nonclassical Properties of the Two-Mode JCM

We study the influence of intrinsic decoherence in the presence of Stark shift for the two-mode Jaynes—Cummings model (JCM). An analytic solution of the Milburn equation for the multiquant two-mode JCM Hamiltonian is obtained. We use this solution to investigate the influence of intrinsic decoherence and Stark shift on nonclassical properties of the system, for the resonant and the off-resonant cases. We compare the behavior of the system in the case of having a coherent superposition state and a statistical mixture of coherent states as an initial field.     

A Bootstrap Framework for Aggregating within and between Feature Selection Methods

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.     

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.