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In this study, molecular dynamics simulations were employed to analyze interactions between phospholipid membranes and human serum albumin (HSA) in the presence of mono- and divalent cations. Two types of membranes, composed of dipalmitoyl phosphatidylcholine (DPPC) and dipalmitoyl phosphatidylethanolamine (DPPE), were utilized. The results revealed that both systems exhibited high stability. The DPPE complexes displayed a greater affinity for albumin compared to DPPC. The high stability of the complexes was attributed to a high number of ionic contacts and hydrogen bonds. The presence of mono- and divalent metal cations significantly influenced the membrane’s capacity to bind proteins. However, these effects varied depending on the phospholipid composition of the bilayer. The studies confirmed the relatively low ability of DPPC to bind potassium ions, as previously observed by others. Consequently, the DPPC/HSA/K+ complex was found to be the least stable among the systems studied. While DPPC interactions were limited to HSA domains I and II, DPPE was able to interact with all domains of the protein. Both lipid bilayers exhibited substantial structural changes and characteristic curvature induced by interactions with HSA, which confirms the formation of relatively strong interactions capable of influencing the arrangement of the phospholipids.

This article presents a method for identifying the cause of damage to a rotary air preheater on one of the fluidized bed boilers operating in a power plant. The bearing in question operates under harsh conditions with the exhaust gas temperature reaching 287 °C and causing its casing to heat up intensively. It is therefore important to ensure that the bearing is constantly cooled by water, which lowers the operating temperature and thus extends its service life. Unfortunately, after a short period of operation, the upper double-row spherical roller bearing was damaged, and the tests presented in the assessment method helped to determine the cause of damage to its casing.

The aim of this study was to determine the effect of a cow’s breed on the content of minerals in milk depending on the stage of lactation. The study material was the milk of cows of two breeds, Montbéliarde (mb; n = 15) and black-and-white Polish Holstein-Friesian (phf; n = 15), kept at the same farm. The phf cows were matched with the mb cows by analogs considering the stage and number of successive lactations. Milk for analysis was collected six times during lactation when the official test milking was performed, and a total of 180 milk samples were collected. The fresh milk was analyzed for its proximate chemical composition and mineral content (Ca, Mg, K, Na and Zn), which were identified in the mineralization by flame atomic absorption spectrophotometry. Milk of the Montbéliarde cows contained significantly more sodium, calcium, magnesium and zinc compared to milk of the Polish Holstein-Friesian cows. This enables the conclusion that the contents of these elements are determined by breed. This study demonstrates the variability of the contents of the analyzed minerals with time after calving. During lactation, the contents of potassium, calcium, magnesium and sodium were the highest at the late stage of lactation, especially in the tenth month of lactation.

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles.

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C28 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems.

Fibroin–chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca2+) and hydrogen phosphate (HPO₄2−) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions, including hydrogen bonds, ionic contacts, and hydrophobic contacts, along with structural and energetical features, were examined. The calculated binding energy values for the fibroin–chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between the considered biopolymers. These interactions were found to primarily be hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of a significant number of hydrogen bonds and ionic contacts with chitosan.

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications.

IntroductionWithin FAR SEAS, a multi-component evidence-based community intervention was implemented and evaluated in Mazovia (Poland), with the aim of preventing alcohol-exposed pregnancies, and therefore preventing FASD.MethodsMulti-disciplinary professionals from different services (social, addiction, and psychology), recruited women of child-bearing age (pregnant and not pregnant) in local communities, screened them for alcohol risk, and allocated participants (n = 441) to groups for low- (70%), moderate- (23%), or high-risk (7%) of alcohol exposed pregnancy, to provide interventions tailored to their needs. The non-parametric sign test, testing differences between pairs of observations before and after intervention was used to evaluate the outcomes.ResultsFollow-up data (collected from 93% of participants) indicated positive changes in the key outcome variables: risky alcohol consumption dropped by 81%, contraception use increased by 15% and visiting a gynecologist increased by 39%; as well as in associated psychosocial risk factors (decrease in cigarette and drug use, domestic violence and depressive symptoms). No changes were noted in frequency of other service use (medical, psychological, or social). The most prominent changes were observed in the moderate-risk group.DiscussionChanging risky behaviors (alcohol consumption and sex without contraception) to prevent alcohol exposed pregnancies is feasible at the local level, even without engagement of medical professionals. Key challenges, related to engaging professionals and local authorities, must be addressed; and procedures should be adapted to local contexts and needs.

The present work investigated the properties of rubber vulcanizates containing different nanoparticles (Cloisite 20A and Cloisite Na+) and prepared using different sonication amplitudes. The results showed that a maximum improvement in tensile strength of more than 60% over the reference sample was obtained by the nanocomposites containing 2 wt.% Cloisite 20A and 1 wt.% Cloisite Na+ and mixed with a maximum amplitude of 270 µm. The modulus at 300% elongation increased by approximately 18% and 25% with the addition of 2 wt.% Cloisite 20A and 3 wt.% Cloisite Na+, respectively. The shape retention coefficient of rubber samples was not significantly affected by the mixing amplitude, while the values of the softness measured at the highest amplitude (270 µm) were higher compared to those of mixtures homogenized with lower amplitudes. The loading-unloading and loading-reloading processes showed similar trends for all tested nanocomposites. However, they increased with increasing levels of sample stretching but were not significantly affected by filler content at a given elongation. More energy was dissipated during the loading-unloading process than during the loading-reloading. SEM micrographs of rubber samples before and after cycling loading showed rough, stratified, and elongated morphologies. XRD results showed that elastomeric chains were intercalated in the MMT nanosheets, confirming the improvement of mechanical properties. The difference between the hydrophilic pristine nanoclay (Cloisite Na+) and organomodified MMT (Cloisite 20A) was also highlighted, while the peaks of the stretched rubber samples were smaller, regardless of the rubber composition, due most probably to the decrease of interlayer spacing.

The present study aimed to investigate the effect of mineral (sulphate) or organic (glycine chelate) forms of Zn used in quantities covering 100% or 50% of the Zn requirement on the fatty acid profile of the thigh muscles of Ross 308 broiler chickens. We also analysed the dietary value of this meat based on its fatty acid profile. The experimental factors did not influence the content of basic chemical components and the meat’s pH. Although, in terms of statistics, the presented study supports the statement that the form (sulphate vs. glycine chelate) and/or amount of Zn used (100% or 50% of the requirement) affects the fatty acid profile and dietary value of thigh meat, the results imply that the requirement of Ross 308 broiler chickens for Zn was also covered in full when in it was used in amounts covering 50% of the requirement, irrespective of the form in which Zn was used. However, it cannot be explicitly confirmed that the form of Zn affects the analysed parameters of thigh meat.

Solubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and antimicrobial agent. However, its low solubility hampers its efficient applications. In this project, deep eutectic solvents (DESs) were used as solubilizing agents for dapsone as an alternative to traditional solvents. DESs were composed of choline chloride and one of six polyols. Additionally, water–DES mixtures were studied as a type of ternary solvents. The solubility of dapsone in these systems was determined spectrophotometrically. This study also analyzed the intermolecular interactions, not only in the studied eutectic systems, but also in a wide range of systems found in the literature, determined using the COSMO-RS framework. The intermolecular interactions were quantified as affinity values, which correspond to the Gibbs free energy of pair formation of dapsone molecules with constituents of regular solvents and choline chloride-based deep eutectic solvents. The patterns of solute–solute, solute–solvent, and solvent–solvent interactions that affect solubility were recognized using Orange data mining software (version 3.36.2). Finally, the computed affinity values were used to provide useful descriptors for machine learning purposes. The impact of intermolecular interactions on dapsone solubility in neat solvents, binary organic solvent mixtures, and deep eutectic solvents was analyzed and highlighted, underscoring the crucial role of dapsone self-association and providing valuable insights into complex solubility phenomena. Also the importance of solvent–solvent diversity was highlighted as a factor determining dapsone solubility. The Non-Linear Support Vector Regression (NuSVR) model, in conjunction with unique molecular descriptors, revealed exceptional predictive accuracy. Overall, this study underscores the potency of computed molecular characteristics and machine learning models in unraveling complex molecular interactions, thereby advancing our understanding of solubility phenomena within the scientific community.

In this study, both practical and theoretical aspects of the solubility of edaravone (EDA) in Deep Eutectic Solvents (DESs) were considered. The solubility of edaravone in some media, including water, can be limited, which creates the need for new efficient and environmentally safe solvents. The solubility of EDA was measured spectrophotometrically and the complex intermolecular interactions within the systems were studied with the COSMO-RS framework. Of the four studied DES systems, three outperformed the most efficient classical organic solvent, namely dichloromethane, with the DES comprising choline chloride and triethylene glycol, acting as hydrogen bond donor (HBD), in a 1:2 molar proportion yielding the highest solubility of EDA. Interestingly, the addition of a specific amount of water further increased EDA solubility. Theoretical analysis revealed that in pure water or solutions with high water content, EDA stacking is responsible for self-aggregation and lower solubility. On the other hand, the presence of HBDs leads to the formation of intermolecular clusters with EDA, reducing self-aggregation. However, in the presence of a stoichiometric amount of water, a three-molecular EDA–HBD–water complex is formed, which explains why water can also act as a co-solvent. The high probability of formation of this type of complexes is related to the high affinity of the components, which exceeds all other possible complexes.

This article presents a method for identifying the cause of damage to a rotary air preheater on one of the fluidized bed boilers operating in a power plant. The bearing in question operates under harsh conditions with the exhaust gas temperature reaching 287 °C and causing its casing to heat up intensively. It is therefore important to ensure that the bearing is constantly cooled by water, which lowers the operating temperature and thus extends its service life. Unfortunately, after a short period of operation, the upper double-row spherical roller bearing was damaged, and the tests presented in the assessment method helped to determine the cause of damage to its casing. [ABSTRACT FROM AUTHOR]

In this study, molecular dynamics simulations were employed to analyze interactions between phospholipid membranes and human serum albumin (HSA) in the presence of mono- and divalent cations. Two types of membranes, composed of dipalmitoyl phosphatidylcholine (DPPC) and dipalmitoyl phosphatidylethanolamine (DPPE), were utilized. The results revealed that both systems exhibited high stability. The DPPE complexes displayed a greater affinity for albumin compared to DPPC. The high stability of the complexes was attributed to a high number of ionic contacts and hydrogen bonds. The presence of mono- and divalent metal cations significantly influenced the membrane's capacity to bind proteins. However, these effects varied depending on the phospholipid composition of the bilayer. The studies confirmed the relatively low ability of DPPC to bind potassium ions, as previously observed by others. Consequently, the DPPC/HSA/K+ complex was found to be the least stable among the systems studied. While DPPC interactions were limited to HSA domains I and II, DPPE was able to interact with all domains of the protein. Both lipid bilayers exhibited substantial structural changes and characteristic curvature induced by interactions with HSA, which confirms the formation of relatively strong interactions capable of influencing the arrangement of the phospholipids. [ABSTRACT FROM AUTHOR]

The present study investigated the effects of thermal aging, ultraviolet radiation (UV), and stress softening on the performance properties of rubber modified with Cloisite Na+ or Cloisite 20A. Tensile strength (TS), strain at break (SB), modulus, and the retention coefficient were measured before and after aging. Results showed that TS and SB decreased by about 50% after 7 days of aging for all tested samples due to the breakage of the chemical bonds between rubber and nanoparticles. The modulus at 300% elongation increased by 20%, 15%, and 7% after thermal aging for the unmodified sample, nanocomposites with Cloisite Na+, and Cloisite 20A, respectively. The shape retention coefficient of all samples was not affected by heat, except for the virgin rubber sample, which exhibited a decrease of about 15% under thermal aging. The virgin matrix and nanocomposites showed different values of aging coefficient during thermal aging and UV radiation. The dissipated energy of samples that were aged after stretching was slightly higher than that of samples that were aged after stretching due to the breakdown of the bonds within the nanocomposites. Loading-reloading energy results showed that the level of stress softening was lower when Mullins was applied after the aging of the samples. Differential scanning calorimetry results indicated a slight decrease in Tg1 in the aged and stretched samples and an increase in the temperature of the first endothermic peak due to the addition of nanofillers in the stretched and aged samples. Thermogravimetric analysis revealed that all tested samples exhibited similar thermograms, regardless of their state of stretching or aging. Scanning electron microscopy analysis showed that the fracture surface of the virgin unaged sample was rough with some holes, while it was flatter and less rough after aging. [ABSTRACT FROM AUTHOR]

A significant need within the design of materials for vehicles or other engineering structures is to determine their potential to mitigate impact loads. The material acting as a shield during an impact absorbs energy, dissipating the excess in a process of irreversible deformation. In order to prevent this, or to limit the areas of damage as much as possible, have begun to be used materials that absorb impact energy without drastically compromising their strength. Energy absorbing composite structures (EACS) have the ability to convert impact energy into some form of energy absorbed through deformation. Compared to homogeneous materials, a number of factors also point to the increasing advantage of using composite sandwich structures, which, in addition to their high strength ratings, have a lower weight and a much more effective ability to absorb shock or impact load energy. This paper presents the results of damage tolerance testing of epoxy-glass sandwich composites with chemical modified matrix. The damage tolerance of the composites was determined using an Instron CEAST 9340 testing machine with an impact energy ranging from 5 J to 35 J and indicated the value at which visible damage to the composite occurs while it retains some of its strength properties. It was the most important test to determine the damage tolerance, but additional tests to characterize the strength of the composite more comprehensively were also performed. Experimental studies were used to present a methodology for the preliminary characterization of the material strength and to analyse the relation between structure and mechanical response of the composite. [ABSTRACT FROM AUTHOR]

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This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of edaravone solubility data. Hence, new measurements were performed to collect edaravone solubility values in polar non-protic and diprotic media. Such an extended set of data was used in the machine learning process for tuning the parameters of regressor models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring high predictive power and accurate solubility computations for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria, were included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are better suited for edaravone dissolution than those described in this manuscript. The theoretical framework presented in this study provides a precise guideline for conducting experiments, as well as saving time and resources in the pursuit of new findings.