Measurements of the Hansen solubility parameters of mites and cockroaches to improve pest control applications

Bacterial contamination of cockroaches in different wards of hospital, restaurant and home

Cockroaches are very capable of mechanically transmitting harmful microorganisms, which is seen to be a severe hazard to the general public's health. The purpose of this study was the evaluation of cockroach bacterial contamination in various locations throughout Babylon. 300 cockroaches were caught from different wards of the hospital, restaurants, and houses. Using PBS buffer, the external surface of the cockroaches was washed to collect bacteria. Standard phenotypic methods were used to identify and classify bacteria. Afterward, the bacterial resistance to different antibiotics was investigated using the Kirby-Bauer disk diffusion susceptibility test. The 200 (66.6 %) American cockroaches including 56 (18.7 %) Blattella germanica and 44 (14.6 %) Blatta orientalis were identified. Noteworthy, 96.6 % of cockroaches were infected with different bacteria. Bacillus strains, coagulase-negative Staphylococci (CoNs), and Escherichia coli were the most frequent among the isolated bacteria. On average, the highest antibiotic resistance was detected to cefotaxime, ampicillin, cephalothin, and kanamycin. On the other hand, the isolated bacteria showed high sensitivity to gentamicin, nitrofurantoin, tetracycline, trimethoprim/sulfamethoxazole (SXT), and chloramphenicol. high antibiotic resistance in bacteria isolated from different wards of the hospital and the high potential of transmission of these bacteria by cockroaches is a serious warning for the health of society.

Hansen Solubility Parameters Applied to the Extraction of Phytochemicals

In many analytical chemical procedures, organic solvents are required to favour a better global yield upon the separation, extraction, or isolation of the target phytochemical analyte. The selection of extraction solvents is generally based on the solubility difference between target analytes and the undesired matrix components, as well as the overall extraction procedure cost and safety. Hansen Solubility Parameters are typically used for this purpose. They are based on the product of three coordinated forces (hydrogen bonds, dispersion, and dipolar forces) calculated for any substance to predict the miscibility of a compound in a pure solvent, in a mixture of solvents, or in non-solvent compounds, saving time and costs on method development based on a scientific understanding of chemical composition and intermolecular interactions. This review summarises how Hansen Solubility Parameters have been incorporated into the classical and emerging (or greener) extraction techniques of phytochemicals as an alternative to trial-and-error approaches, avoiding impractical experimental conditions and resulting in, for example, saving resources and avoiding unnecessary solvent wasting.

Influence of active pharmaceutical ingredient structures on Hansen solubility parameters

In drug development, preformulation is the key step, where compatibility between active pharmaceutical ingredient (API) and excipients is the crucial parameter. To simplify this process, reliable and suitable prediction models are needed. In this case, Hansen solubility parameters (HSPs) can be used. Moreover, HSPs can also describe and characterize the surface properties of the measured substances. Precisely, the surface properties of APIs and excipients affect the compatibility of the resulting dosage form. In this work, HSPs of six selected APIs of different chemical nature were determined (tadalafil, vardenafil-hydrochloride trihydrate, mefenamic acid, bisoprolol hemi-fumarate, meloxicam and indomethacin) using inverse gas chromatography (IGC) according to Snyder and Karger adsorption model. This study aimed to investigate the influence of APIs structure on HSPs and to prove the sensitivity of this method to different chemical nature of measured substances. Our results showed the influence of selected APIs chemical nature on HSPs. These results can provide a better understanding of API behaviour during the drug development process.

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".

Evaluation of the Antifoaming Effect Using Hansen Solubility Parameters

The appearance of foam in various industrial processes can cause challenges. Antifoaming agents are widely added to suppress foam. To exert a defoaming effect, affinity between the main foam-generating component and the antifoaming agent is an important criterion for selection of an antifoaming agent. The Hansen solubility parameter (HSP) can be used as an index to show the affinity between substances more quantitatively, simply, and accurately. The Hansen solubility sphere method was used to measure the HSPs of antifoaming agents and a foam-forming surfactant. Various antifoaming agents were added to a surfactant solution, and the defoaming effect was evaluated. Correlations of 0.953-0.860 confirmed a relationship between affinity of the antifoaming agents for the surfactant based on HSP theory and the defoaming effect. It is suggested that use of HSP as an indicator can facilitate selection of the most suitable antifoaming agent for the process.