Alpha-mangostin ($\alpha$-MG) is a prenylated xanthone extracted from the pericarp of the mangosteen tree (Garcinia mangostana) fruit. The compound exhibits a broad range of therapeutic properties, such as anti-inflammatory, antioxidative, and antimicrobial activity. This review highlights new findings in antibacterial studies involving $\alpha$-MG, demonstrates its potent activity against Gram-positive bacteria, including Staphylococcus and Enterococcus genera, and describes the antibacterial mechanisms involved. Most cited literature comes from 2020 to 2025, highlighting the topic’s relevance despite limited new publications in this period. The primary antibacterial mechanism of $\alpha$-MG consists of the disruption of the bacterial membrane and increased bacterial wall permeability, leading to drug accumulation and cell lysis. Other mechanisms include genomic interference and enzyme activity inhibition, which impair metabolic pathways. $\alpha$-MG can also disrupt biofilm formation, facilitate its removal, and prevent its maturation. Furthermore, $\alpha$-MG presents strong synergistic action with common antibiotics and other phytochemicals, even against drug-resistant strains, facilitating infection treatment and allowing for reduced drug dosage. The main challenge in developing $\alpha$-MG-based drugs is their low aqueous solubility; therefore, nanoformulations have been explored to improve its bioavailability and antibacterial stability. Extended research in this direction may enable the development of effective antibacterial and anti-biofilm therapies based on $\alpha$-MG.

Multidrug-resistant bacteria represent a significant challenge in the treatment of bacterial infections, often leading to therapeutic failures. This issue underlines the need to develop strategies that improve the efficacy of conventional antibiotic therapies. In this study, we aimed to assess whether a plant-derived compound, α-mangostin, and photodynamic therapy (PDT) could enhance the antibacterial activity of ciprofloxacin against uropathogenic strains of Escherichia coli and Staphylococcus aureus. Using nanopore sequencing technology, we confirmed that the clinical strains tested were classified as multidrug-resistant. Digital holotomography (DHT) was used to examine α-mangostin-induced changes in the bacterial cells’ penetration by a photosensitizer. A scanning confocal fluorescence microscope was used to visualize photosensitizer penetration into bacterial cells and validate DHT results. A synergistic effect between α-mangostin and ciprofloxacin was observed exclusively in S. aureus strains, while no enhancement of ciprofloxacin’s antibacterial activity was detected in E. coli strains when combined with α-mangostin. Notably, photodynamic therapy significantly potentiated the antibacterial effects of ciprofloxacin and its combination with α-mangostin compared to untreated controls. In addition, morphological changes were observed in bacterial cells exposed to these antimicrobials. In conclusion, our findings suggest that α-mangostin and PDT may serve as valuable adjuncts to ciprofloxacin, improving the eradication of uropathogens.

The study presents the concepts of rapid, label-free OCT-based multiparametric phenotyping of bacterial colonies using attenuation coefficient (µT). Retrieved 2D-µT maps exhibit significant predictive potential, with 86.3% to 100% of colonies accurately classified by species.

The global increase in antimicrobial resistance (AMR) demands non-destructive diagnostic strategies capable of verifying bacterial identity directly on growth substrates. Here, we report a quantitative optical biosensing approach in which Optical Coherence Tomography (OCT) operates as a depth-resolved attenuation-based sensor rather than a qualitative imaging modality. The sensing principle is based on extraction of the optical attenuation coefficient (µA) from the exponential decay of axial OCT A-scan signals. This physically defined transduction parameter was used to reconstruct depth-resolved two-dimensional attenuation (2D-µA) maps, representing volume-averaged structural and compositional characteristics of bacterial assemblies. Unlike transverse image-texture analysis, the proposed approach derives the analytical signal directly from axial optical signal decay, enabling quantitative characterization of intact microbial structures. The resulting attenuation-based descriptors (including statistical metrics and morphometric parameters) were used for identification of bacterial colonies grown on agar and biofilms formed on stainless steel surfaces. Seven optimized machine learning models were applied to evaluate sensing performance at both species and Gram-type levels. For colonies, the platform achieved classification accuracies of 93.8% (species) and 95.9% (Gram-type). Structurally heterogeneous biofilms were identified with accuracies of 77.9% (species) and 90.8% (Gram-type). An integrated cross-phenotype model maintained a robust Gram-type accuracy of 94.9%, demonstrating consistent sensing capability across different growth architectures. This attenuation-coefficient–based transduction framework establishes OCT as a quantitative optical biosensor for rapid, label-free, in situ bacterial verification while preserving native three-dimensional structure. The method provides a non-destructive analytical gatekeeper prior to confirmatory microbiological assays.