Understanding hyaluronic acid induced variation of water structure by near-infrared spectroscopy

Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Influence of Casting Variables on Release Kinetics of Orally Disintegrating Film

As a new form for supplying vitamin C, orally disintegrating films (ODFs) were developed C based on hyaluronic acid (HA) under varying casting conditions and the properties were analyzed. The films with different thicknesses (2, 3, and 8 mm, for CT2, CT4, and CT8, respectively) were produced by adjustments made to casting height. Two types of 8 mm thick ODFs produced by single or double casting (4 + 4 mm for CTD4+4) methods were also compared. As film thickness increased, water vapor permeability and tensile strength also increased. Even at equal thickness, manufacturing with double casting exhibited a stronger texture and reduced disintegration compared to single casting. All ODFs met the World Health Organization's recommended daily vitamin C intake (45 mg/day) with a single sheet. Films showed over 80% dissolution in various solvents, adhering to the Hixson-Crowell cube root law, indicating vitamin C release occurred via porous penetration of the eluate. For CT2, CT4, and CTD4+4, vitamin C release was primarily governed by diffusion within the gel matrix and HA erosion. However, for CT8, HA erosion-induced release somewhat dominated. Based on the sensory test, it seems desirable to adjust the thickness of the film to 2 or 4 mm, because a thickness greater than that increased the foreign body sensation due to prolonged residence in the oral cavity.

Intradermal Injection of Hybrid Complexes of High- and Low-Molecular-Weight Hyaluronan: Where Do We Stand and Where Are We Headed in Regenerative Medicine?

Hyaluronic acid (HA) is a remarkably multifaceted biomacromolecule, playing a role in regulating myriad biological processes such as wound healing, tissue regeneration, anti-inflammation, and immunomodulation. Crosslinked high- and low-molecular-weight hyaluronic acid hydrogels achieve higher molar concentrations, display slower degradation, and allow optimal tissue product diffusion, while harnessing the synergistic contribution of different-molecular-weight hyaluronans. A recent innovation in the world of hyaluronic acid synthesis is represented by NAHYCO® Hybrid Technology, a thermal process leading to hybrid cooperative hyaluronic acid complexes (HCC). This review summarizes the current literature on the in vitro studies and in vivo applications of HCC, from facial and body rejuvenation to future perspectives in skin wound healing, dermatology, and genitourinary pathologies.

Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics

Hyaluronic acid (HA), a polysaccharide, is widely used for its essential physiological functions. Although the structures of low molecular weight HA produced by both acid and enzyme degradation methods are extremely similar, there are still differences due to the different degradation principles. There is currently no clear way to distinguish between HA prepared by acidolysis and enzymatic hydrolysis. Based on near-infrared (NIR) spectroscopy and aquaphotomics technology, a method for distinguishing HA raw materials and their mixtures from different sources was proposed, and HA with different mixed ratios was accurately quantified. First, NIR spectra of the HA samples were collected. The spectra were then preprocessed to improve the spectral resolution. Spectral information was extracted based on wavelet transform and principal component analysis, resulting in a final selection of 12 characteristic wavelengths containing classification information. The discriminative and quantitative models were then constructed using the 12 wavelengths. The discriminative model achieved a 100% identification rate for HA from different sources. The correlation coefficient of calibration (Rc), validation (Rp), external test (Rt), root mean square error of cross validation (RMSECV), calibration (RMSEC), validation (RMSEP), and external test (RMSET) of the mixed proportion quantitative model were 0.9876, 0.9876, 0.9898, 0.0546, 0.0433, 0.0440, and 0.0347, respectively. In this study, the problem of structural similarity and non-identifiability of HA produced by acidolysis and enzymatic hydrolysis was addressed, and quality monitoring of HA feedstock in HA circulating links was achieved. This is the first time to achieve accurate quantification of solid mixtures using the aquaphotomics method.

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA-A and LMWHA-E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA-A and LMWHA-E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares-discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)-SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA-A and LMWHA-E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

Rosa gallica and its active compound, cyanidin-3,5-O-diglucoside, improve skin hydration via the GLK signaling pathway

Rosa gallica has been previously reported to display anti-inflammatory, anti-oxidative, and anti-skin wrinkle activities. However, the effect of Rosa gallica on skin hydration and its active components are largely unknown. Herein, we aimed to investigate the skin hydration effect of rose petal extract (RPE) in humans and elucidate the underlying molecular mechanism. A double-blinded clinical study was performed to investigate the effect of RPE on skin hydration. Stratum corneum moisture analysis demonstrated that RPE treatment significantly improved hydration levels in human skin. Furthermore, HAS2 and hyaluronic acid levels were notably increased by RPE in keratinocytes and 3D human skin equivalent model. By comparing the modulatory effect on HAS2 expression, cyanidin-3,5-O-diglucoside (CDG) was identified as the most potent compound in RPE likely responsible for skin hydration. The kinase activity of GLK, an upstream regulator of MAPK signaling, was increased by CDG in a dose-dependent manner. Importantly, silencing GLK reversed CDG-mediated HAS2 upregulation, further supporting the involvement of GLK in the CDG-mediated effects. Binding of CDG to GLK was confirmed by pull-down assay and computer modeling. These findings suggest that RPE and its active component CDG increases skin hydration by upregulating HAS2 expression through modulating the GLK-MAP2K-MAPK signaling pathway.

Fabrication and characterization of phycocyanin-alginate-pregelatinized corn starch composite gel beads: Effects of carriers on kinetic stability of phycocyanin

Composite gel beads using calcium alginate and different concentrations of pregelatinized corn starch (PCS) were produced to encapsulate phycocyanin (PC). Rheological properties of different sodium alginate/PCS/PC mixtures, structural and morphological properties of beads, and kinetic stability of encapsulated PC (upon heating at various time-temperature combinations) were then assessed. Rheological properties of the mixtures exhibited shear thinning behaviors. Aquagram revealed that the PC-containing beads had more water structure with weak‑hydrogen bonds. Morphological images represented less subsidence in the structures of composite gel beads, unlike PCS-free beads. Kinetic study showed that degradation rate constant values of PC encapsulated in composite gel beads (1.08-3.45 × 10^-4, 3.38-4.43 × 10^-4, and 5.57-15.32 × 10^-4 s^-1) were lower than those in PCS-free alginate gel beads (4.45 × 10^-4, 9.20 × 10^-4, and 18.04 × 10^-4 s^-1) at 40, 50, and 60 °C, respectively. This study suggests that the composite gel beads can improve PC stability.

Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy

Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.

Regulation of aquaporin-3 water permeability by hyaluronan

Hyaluronan (HA) is a major component in the extracellular matrix and is responsible for maintaining the water content of the skin. However, the function and moisturizing mechanism at the atomic level of HA remain only partially understood. Investigating the interactions of HA and other skin components can help us understand how the former moisturizes the skin. Considering that aquaporin-3 (AQP3) is a protein responsible for transmembrane water transport in the human skin, we have, therefore, investigated the interactions of AQP3 and HA with different molecular weights using molecular dynamics simulations in the present work. Our results indicate that HA can adsorb onto AQP3 and decrease water mobility around the latter. In addition, the permeation rate of water through AQP3 can also be decreased by HA, and this phenomenon is particularly obvious for small molecular HA. Moreover, we found that large molecular HA can link two adjacent membranes in the extracellular matrix, increasing the adhesion between the membranes in the periplasm. The results of the present study indicate that HA is a natural regulator of AQP3, revealing the synergetic function of HA and AQP3 in the extracellular matrix of the skin.

Research on the Structure of Peanut Allergen Protein Ara h1 Based on Aquaphotomics

Peanut allergy is becoming a life-threatening disease that could induce severe allergic reactions in modern society, especially for children. The most promising method applied for deallergization is heating pretreatment. However, the mechanism from the view of spectroscopy has not been illustrated. In this study, near-infrared spectroscopy (NIRS) combined with aquaphotomics was introduced to help us understand the detailed structural changes information during the heating process. First, near-infrared (NIR) spectra of Ara h1 were acquired from 25 to 80°C. Then, aquaphotomics processing tools including principal component analysis (PCA), continuous wavelet transform (CWT), and two-dimensional correlation spectroscopy (2D-COS) were utilized for better understanding the thermodynamic changes, secondary structure, and the hydrogen bond network of Ara h1. The results indicated that about 55°C could be a key temperature, which was the structural change point. During the heating process, the hydrogen bond network was destroyed, free water was increased, and the content of protein secondary structure was changed. Moreover, it could reveal the interaction between the water structure and Ara h1 from the perspective of water molecules, and explain the effect of temperature on the Ara h1 structure and hydrogen-bonding system. Thus, this study described a new way to explore the thermodynamic properties of Ara h1 from the perspective of spectroscopy and laid a theoretical foundation for the application of temperature-desensitized protein products.

Water as a probe to understand the traditional Chinese medicine extraction process with near infrared spectroscopy: A case of Danshen (Salvia miltiorrhiza Bge) extraction process

Extraction process is not only a critical manufacturing unit but also the initial process of various extracts and preparations. Taking the most extensive Chinese herbal medicine Danshen (Salvia miltziorrhiza Bge) as an example, salvianolic acid B (Sal B) is its main active pharmaceutical ingredient but lacks accurate characterization of the extraction process. As one of process analytical technologies, near-infrared spectroscopy (NIRS) technology has been widely applied for monitoring pharmaceutical extraction process. In most past studies, water spectral information is often eliminated due to its high absorption. However, this study proposed a method of using water spectrum to understand the whole extraction process and to quickly determine the content of Sal B. Principal component analysis (PCA) was first utilized to investigate the whole extraction process, then the reconstructed spectrum based on PCA was established and analyzed by Aquaphotomics, and finally the partial least squares regression (PLSR) quantitative model of Sal B was established. PCA and Aquaphotomics results showed the whole extraction process could be considered as a dynamic change from structure breaker to structure maker, and the dominance of highly H-bonded water structures increases with the extraction time. Also, the Sal B quantitative model with water spectrum showed higher accuracy and stability than other methods, which parameters (RMSEC, RMSECV, RMSEP, R²c, R²cv, R²p, RPD) were 0.2408 mg/mL, 0.2939 mg/mL, 0.2584 mg/mL, 0.9536, 0.9300, 0.9494, 4.6298, respectively, and the paired t-test showed that Sal B content measured by NIR and HPLC methods had no significant differences (p > 0.05). In conclusion, all result indicated that water can be used as a probe to understand the traditional Chinese medicine extraction process with NIRS.