Integratomics of Human Dermal Fibroblasts Treated with Low Molecular Weight Hyaluronic Acid

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.

New Functions of Low-Molecular-Weight Hyaluronic Acid on Epidermis Filaggrin Production and Degradation

Hyaluronic acid (HA) is a high-molecular-weight polysaccharide with high moisturizing power. It is composed of repeating disaccharides of N-acetyl-D-glucosamine and D-glucuronic acid. Low-molecular-weight hyaluronan (LMHA) is obtained by changing the molecular weight or modifying the functional groups of HA and is commonly used together with HA in cosmetics. The objective of this study was to determine whether LMHA promotes the synthesis of filaggrin (FLG). We also investigated whether LMHA activates FLG-degrading enzymes. Three-dimensional (3D) models of the human epidermis were cultured with LMHA. Real-time PCR was used to quantify the mRNA levels of profilaggrin (proFLG), involucrin (IVL), and FLG-degrading enzymes. FLG protein levels were measured by fluorescent antibody staining and Western blotting. The mRNA was quantified using a 3D epidermis model, and it was observed that the mRNA levels of proFLG, IVL, caspase-14 (CASP14), and bleomycin hydrolase were increased by the application of LMHA. Immunofluorescence results showed an increase in FLG proteins, and results from experiments using 3D epidermis models showed that LMHA increased the activity of CASP14. This suggests that the topical application of LMHA would result in an increase in natural moisturizing factor and promote moisturization of the stratum corneum.