Physical characterization of succinylated type I collagen by Raman spectra and MALDI-TOF/MS and in vitro evaluation for biomedical applications

A general deep learning model for predicting and classifying pea protein content via visible and near-infrared spectroscopy

Rapid and accurate detection of pea protein content is crucial for breeding and ensuring food quality. This study introduces the PeaNet model, which employs an improved convolutional neural network architecture to predict and classify pea protein content. The model was developed using 156 visible and near-infrared spectral datasets from 52 varieties cultivated under varied conditions. The data were preprocessed with Savitzky-Golay smoothing and multiplicative scatter correction to improve quality. The results revealed that the model achieved an R2 of 0.84 for predicting protein content and a classification accuracy of 85.33 % on the test set. On an independent validation set comprising different pea varieties, the model maintained an R2 above 0.80 and a classification accuracy of 83.33 %. It significantly outperformed traditional machine learning models and conventional deep learning architectures. This study introduces a universal, accurate, and efficient method for detecting pea protein content, thereby advancing food nutrition assessment and quality control.

Identification and Characterization of Fibronectin-Binding Peptides in Gelatin

Collagen and fibronectin (FN) are important components in the extracellular matrix (ECM). Collagen-FN binding belongs to protein-protein interaction and plays a key role in regulating cell behaviors. In this study, FN-binding peptides were isolated from gelatin (degraded collagen) using affinity chromatography, and the amino acid sequences were determined using HPLC-MS. The results indicated that all FN-binding peptides contained GPAG or GPPG. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and dual-polarization interferometry (DPI) were used to analyze the effects of hydroxylation polypeptide on FN binding activity. DPI analysis indicated that peptides with molecular weight (MW) between 2 kDa and 30 kDa showed higher FN-binding activity, indicating MW range played an important role in the interaction between FN and peptides. Finally, two peptides with similar sequences except for hydroxylation of prolines were synthesized. The FN-binding properties of the synthesized peptides were determined by MALDI-TOF MS. For peptide, GAPGADGP*AGAPGTP*GPQGIAGQR, hydroxylation of P8 and P15 is necessary for FN-binding. For peptide, GPPGPMGPPGLAGPPGESGR, the FN-binding process is independent of proline hydroxylation. Thus, FN-binding properties are proline-hydroxylation dependent.

Rheological Method for Determining the Molecular Weight of Collagen Gels by Using a Machine Learning Technique

This article presents, for the first time, the results of applying the rheological technique to measure the molecular weights (Mw) and their distributions (MwD) of highly hierarchical biomolecules, such as non-hydrolyzed collagen gels. Due to the high viscosity of the studied gels, the effect of the concentrations on the rheological tests was investigated. In addition, because these materials are highly sensitive to denaturation and degradation under mechanical stress and temperatures close to 40 °C, when frequency sweeps were applied, a mathematical adjustment of the data by machine learning techniques (artificial intelligence tools) was designed and implemented. Using the proposed method, collagen fibers of Mw close to 600 kDa were identified. To validate the proposed method, lower Mw species were obtained and characterized by both the proposed rheological method and traditional measurement techniques, such as chromatography and electrophoresis. The results of the tests confirmed the validity of the proposed method. It is a simple technique for obtaining more microstructural information on these biomolecules and, in turn, facilitating the design of new structural biomaterials with greater added value.

Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

The goal of sample-size planning (SSP) is to determine the number of measurements needed for statistical analysis. This SSP is necessary to achieve robust and significant results with a minimal number of measurements that need to be collected. SSP is a common procedure for univariate measurements, whereas for multivariate measurements, like spectra or time traces, no general sample-size-planning method exists. Sample-size planning becomes more important for biospectroscopic data because the data generation is time-consuming and costly. Additionally, ethical reasons do not allow the use of unnecessary samples and the measurement of unnecessary spectra. In this paper, a general sample-size-planning algorithm is presented that is based on learning curves. The learning curve quantifies the improvement of a classifier for an increasing training-set size. These curves are fitted by the inverse-power law, and the parameters of this fit can be utilized to predict the necessary training-set size. Sample-size planning is demonstrated for a biospectroscopic task of differentiating six different bacterial species, including Escherichia coli, Klebsiella terrigena, Pseudomonas stutzeri, Listeria innocua, Staphylococcus warneri, and Staphylococcus cohnii, on the basis of their Raman spectra. Thereby, we estimate the required number of Raman spectra and biological replicates to train a classification model, which consists of principal-component analysis (PCA) combined with linear-discriminant analysis (LDA). The presented algorithm revealed that 142 Raman spectra per species and seven biological replicates are needed for the above-mentioned biospectroscopic-classification task. Even though it was not demonstrated, the learning-curve-based sample-size-planning algorithm can be applied to any multivariate data and in particular to biospectroscopic-classification tasks.

Detection of organic compounds in impact glasses formed by the collision of an extraterrestrial material with the Libyan Desert (Africa) and Tasmania (Australia)

Impact glasses are rich silica melted formed at high temperature and pressure by the impact of an extraterrestrial body on Earth. Here, Libyan Desert glasses (LDGs) and Darwin glasses (DGs) were studied. Two non-destructive analytical techniques were used to detect and characterize organic compounds present in their inclusions: Raman spectroscopy and scanning electron microscopy coupled to energy-dispersive X-ray spectroscopy (SEM-EDS). Phytoliths, humboldtine, palmitic acid, myristic acid, oleic acid, 4-methyl phthalic acid, and S-H stretching vibrations of amino acids were identified. The presence of these particular organic compounds in such materials has not been reported so far, providing information about (a) the ancient matter of the area where the impact glasses were formed, (b) organic matter belonging to the extraterrestrial body which impacted on the Earth, or (c) even to current plant or bacterial life, which could indicate an active interaction of the LDG and DG with the surrounding environment. Moreover, the identification of fullerene allowed us to know a pressure (15 GPa) and temperatures (670 K or 1800-1900 K) at which samples could be subjected.

Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples

Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis.

Glycoproteins functionalized natural and synthetic polymers for prospective biomedical applications: A review

Glycoproteins have multidimensional properties such as biodegradability, biocompatibility, non-toxicity, antimicrobial and adsorption properties; therefore, they have wide range of applications. They are blended with different polymers such as chitosan, carboxymethyl cellulose (CMC), polyvinyl pyrrolidone (PVP), polycaprolactone (PCL), heparin, polystyrene fluorescent nanoparticles (PS-NPs) and carboxyl pullulan (PC) to improve their properties like thermal stability, mechanical properties, resistance to pH, chemical stability and toughness. Considering the versatile charateristics of glycoprotein based polymers, this review sheds light on synthesis and characterization of blends and composites of glycoproteins, with natural and synthetic polymers and their potential applications in biomedical field such as drug delivery system, insulin delivery, antimicrobial wound dressing uses, targeting of cancer cells, development of anticancer vaccines, development of new biopolymers, glycoproteome research, food product and detection of dengue glycoproteins. All the technical scientific issues have been addressed; highlighting the recent advancement.

Liquid phase collagen modified graphene that induces apoptosis

The differential interference contrast (DIC) and fluorescence confocal micrographs show collagen microfibrils attacking graphite from all sides to form a stable dispersion of collagen modified graphene, but only collagen picks up a stain.

Vibrational spectroscopic investigation on interaction of sago starch capped silver nanoparticles with collagen: a comparative physicochemical study using FT-IR and FT-Raman techniques

Vibrational spectroscopies as analytical tools to investigate the interaction of sago starch-capped silver nanoparticles with collagen scaffolds for biomedical applications.