In this review, we aim to provide a summary of recent research advancements and applications of algin (i.e., alginic acid) and alginate-hybrid materials (AHMs) in medical fields. Algin/alginate are abundant natural products that are chemically inert and biocompatible, and they have superior gelation properties, good mechanical strengths, and biodegradability. The AHMs have been widely applied in wound dressing, cell culture, tissue engineering, and drug delivery. However, medical applications in different fields require different properties in the AHMs. The drug delivery application requires AHMs to provide optimal drug loading, controlled and targeted drug-releasing, and/or visually guided drug delivery. AHMs for wound dressing application need to have improved mechanical properties, hydrophilicity, cell adhesion, and antibacterial properties. AHMs for tissue engineering need improved mechanical properties that match the target organs, superior cell affinity, and cell loading capacity. Various methods to produce AHMs that meet different needs were summarized. Formulations to form AHMs with improved stability, drug/cell-loading capacity, cell adhesion, and mechanical properties are active research areas. This review serves as a road map to provide insights into the strategies to develop AHMs in medical applications.

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellular vesicles (EVs). EVs were isolated from patient plasma using size-exclusion chromatography and labeled with polyyne-based Raman tags targeting three ovarian cancer biomarkers: CA-125, HE4, and CA-19-9. Labeled and unlabeled EVs were deposited onto SERS-active substrates, and spectra were collected using a custom confocal Raman microscope. Incorporating the tag-derived signal into SERS analysis enhanced interpretability and added molecular specificity. We evaluated classification performance using various ML models applied to spectral datasets from a cohort of ovarian cancer patients and healthy controls. Combined use of the Raman tag and label-free regions improved classification accuracy compared to either modality alone. Notably, support vector machine (SVM) achieved over 95 % accuracy, sensitivity, and specificity. Compared to ELISA, our SERS platform demonstrated improved sensitivity in detecting EV-associated biomarkers from small sample volumes. This approach addresses a key limitation of SERS-based diagnostics by linking spectral features to known biomarkers, offering improved transparency and performance in ML-enabled liquid biopsy.

The primary plant cell wall is a complex matrix composed of interconnected polysaccharides including cellulose, hemicellulose, and pectin. Changes of this dynamic polysaccharide system play a critical role during plant cell development and differentiation. A better understanding of cell wall architectures can provide insight into the plant cell development. In this study, a Raman spectroscopic imaging approach was developed to visualize the distribution of plant cell wall polysaccharides. In this approach, Surface-enhanced Raman scattering (SERS through self-assembled silver nanoparticles) was combined with Raman labels (4-Aminothiophenol. 4ATP) and targeted enzymatic hydrolysis to improve the sensitivity, specificity, and throughput of the Raman imaging technique, and to reveal the distribution of pectin and its co-localization with xyloglucan inside onion epidermal cell (OEC) wall. This technique significantly decreased the required spectral acquisition time. The resulted Raman spectra showed a high Raman signal. The resulted Raman images successfully revealed and characterized the pectin distribution and its co-localization pattern with xyloglucan in OEC wall.