Accurate prediction of complex formulation viscosity is crucial for the lubricant industry and engineering processes. However, developing generalizable models remains challenging due to the complexity of industrial mixtures and the scarcity of public experimental data, often resulting from corporate proprietary restrictions. This study presents a machine-learning workflow for viscosity estimation from a proprietary pseudonymized dataset, showing that robust models can be trained under data-protection limitations. We evaluated multiple machine learning architectures, including Decision Tree, Random Forest, Support Vector Regression, and Gradient Boosting, employing feature selection strategies adapted to pseudonymized data. Additionally, the Walther formula was used as a physics-based reference. While this formula required deanonymized multi-temperature viscosity data, it proved valuable for identifying experimental outliers. Among the evaluated models, Gradient Boosting demonstrated superior performance, achieving a median absolute percentage error of 16.2% on the held-out test set. These results indicate that machine learning can support viscosity screening for complex industrial fluids without requiring full compositional transparency.
Glycated hemoglobin (HbA1c) is a well-established biomarker reflecting chronic glycemic control in diabetes. It accumulates through non-enzymatic glycation of hemoglobin under sustained hyperglycemia and serves as a surrogate of metabolic memory. Emerging in parallel, glycosylated RNA (glycoRNA), small noncoding RNAs bearing covalently attached N-linked glycans, has revealed unexpected roles in immune signaling and glycoimmunomodulation. While glycation and glycosylation represent distinct biochemical processes, both are modulated by glucose availability and cellular stress. This opinion paper aimed to explore the conceptual parallels between HbA1c and glycoRNA, proposing that hyperglycemia-induced metabolic changes may simultaneously influence both processes. In light of these, glycoRNA represents an emerging biomarker as a functional effector in metabolic disease, mirroring the hyperglycemia-driven immunological dimensions of HbA1c with a further advantage of a defined metabolic sequence, which can deepen diagnostics towards disease progression patterns. Though direct experimental evidence is currently limited, we outline plausible mechanistic intersections and suggest methodological frameworks for future research. Therefore, this perspective aims to stimulate interdisciplinary investigation into glycoRNA biology within the broader context of glycemic dysregulation and immune modulation in diabetes.
Metal-organic frameworks (MOFs) are a class of soft porous crystals that possess extensive capabilities for regulating and inducing morphological transitions between different crystalline phases under external influences. According to the contemporary perspective, transitions between metastable structural phases occur cooperatively throughout the material, thereby preserving its ideal crystalline structure. A phase of the metal-organic framework DUT-8(Ni), which is poorly investigated and is a transient metastable phase between phases with completely open and closed pores, was studied by using Raman spectroscopy. In this Letter, we present experimental evidence for the coexistence of two structural phases with different pore sizes within a single microcrystal using the hyperspectral Raman mapping technique. The focused light of the laser beam triggered the structural phase change of the microcrystals. The treatment spot was tiny compared to that of the transition region. The long-term stability of the microcrystal phase after the transition is demonstrated. Changes in the reflectance spectra, which characterize the crystal's color, also confirm the observed changes. The coexistence of different phases within one crystal, on the one hand, changes the existing understanding of the phase transition mechanism between open and closed pore phases. And on the other hand, it is a very illustrative example of Raman mapping capabilities as not only the isolated Raman spectrum matters but also the whole data set obtained from the microcrystal surface.
The method based on umbilics that expose line-like organization of complex director fields is used to introduce umbilic surfaces as a numerically robust probe of three-dimensional (3D) topological solitons in frustrated cholesteric liquid crystals. We present a coordinate-free analytical formulation of the umbilic-line approach that ensures reliable detection of umbilics on discrete simulation grids and thus avoids the problems caused by instabilities and sensitivity to coordinate choices. By using our method we introduce the laboratory-referenced phase field giving a natural tool for intuitive surface colorization. In addition, we employ this field to define the two fundamental integer invariants of umbilic loops: the transverse index (the strength) and the longitudinal winding (the profile twist). These invariants directly link the umbilic geometry to the topological characteristics of textures, thus enabling soliton identification and a comparison of solitons by topological content. We apply the technique to the three canonical solitons obtained by the free-energy minimization: the toron and the looped cholesteric fingers of the first and second types with the Hopf indices equal to zero and unity, respectively. It is found that the umbilic-surface representation clearly exposes defect structures, discriminates between visually similar but topologically distinct textures and provides a tool for quantifying and visualizing 3D solitons from director field data.
Curcumin and usnic acid are biologically active substances (BAS) with significant therapeutic potential, however, their clinical use is hindered by their limited water solubility and poor bioavailability. We have developed the dual-drug delivery system incorporating curcumin and usnic acid into hyaluronic acid (HA)-based polymer matrix. The drug release kinetics of both compounds from a polymeric matrix based on native hyaluronic acid with various molecular weights were investigated. The drug release of the active molecules was evaluated at 37 degrees C in a mixture of phosphate buffer solution and ethanol (70:30, v/v), the concentrations of released agents were determined using a spectrophotometric method. Our results demonstrate that the release rate and mechanism are strongly influenced by the polymer molecular weight and the presence of both active compounds. It was shown for the first time that usnic acid changes the release profile of curcumin from diffusive to anomalous type. These findings provide a mechanistic basis for the regulation of dual-drug release and highlight the potential of HA-based matrices for the development of prolonged-release formulations of biologically active agents.
