Giovanni Nicola Roviello

Medicinal and edible plants are valuable sources of biologically active compounds whose composition may vary with botanical identity, geographical origin, environmental conditions, and post-harvest processing. In addition to essential mineral constituents, such plants may accumulate non-essential and potentially toxic elements relevant to exogenous chemical exposure and xenobiotic research. This study characterized the phytochemical composition, essential and non-essential elemental profile, and antioxidant activity of rosehip (Rosa spp.) and sea buckthorn (Hippophae rhamnoides L.) fruits collected from different locations in Armenia. Soluble sugars and vitamin C were determined by HPLC, elemental composition by ICP-OES, and radical-scavenging activity by the DPPH assay. In fresh rosehip, vitamin C ranged from 130 to 900 mg/100 g, compared with 11.9–40.0 mg/100 g in sea buckthorn. Potassium was predominant in both fruits. In the analyzed samples, the measured Ca and Mg concentrations were numerically higher in rosehip, whereas higher Fe concentrations were observed in sea buckthorn. Potentially toxic elements, including As, Cd, and Pb, were detected at different concentrations among the analyzed samples. In the selected paired rosehip samples, lower DPPH radical-scavenging activity was measured in the shade-dried materials than in the corresponding fresh materials, while the analyzed sea buckthorn samples showed high DPPH radical-scavenging activity under the applied assay conditions. These findings provide a comparative characterization of the phytochemical and elemental profiles of the investigated fruits and highlight the xenobiotic relevance of the detected non-essential elements.

Background: Thymus tiflisiensis is used in Georgian traditional medicine, but its pharmacological properties remain poorly characterized. This study evaluated the antioxidant, protein denaturation inhibitory, and antibacterial activities of leaf and root extracts and examined the modulatory effects of gallic acid (GA) on these activities. Methods: Methanolic extracts of leaves and roots were analyzed for total phenolic and flavonoid contents, rosmarinic acid (RA), ascorbic acid, and glutathione. Antioxidant activity was evaluated using the DPPH assay, protein denaturation inhibitory activity using a bovine serum albumin denaturation assay, and antibacterial activity against Streptococcus agalactiae and Escherichia coli using broth microdilution. The combined effects of the plant extracts and GA were evaluated across multiple concentration matrices. Molecular docking was used to predict the interactions of RA and GA with the bacterial enzymes MurF and LpxC. Results: The methanolic leaf extract exhibited greater antioxidant activity (IC50 = 34.8 µg/mL) than the root extract (IC50 = 81.9 µg/mL), accompanied by higher total phenolic (113.6 vs. 56.9 mg GAE/g DW) and flavonoid (63.8 vs. 19.0 mg RE/g DW) contents and higher ASC/DHA and GSH/GSSG ratios. Conversely, the root extract exhibited greater protein denaturation inhibitory activity (IC50 = 51.3 vs. 159.7 µg/mL for the leaf extract) and moderate antibacterial activity against S. agalactiae (MIC = 6.0 mg/mL) and contained higher levels of RA. The addition of 5 µM GA enhanced the antioxidant activity of both leaf and root extracts. In protein denaturation assays, 10 µM GA produced a minor enhancement at low extract concentrations, whereas 100 µM GA reduced the inhibitory effects. At 1 mM, GA enhanced the antibacterial activity of both extracts against S. agalactiae and that of the root extract against E. coli. Molecular docking predicted more favorable interactions of RA with MurF and LpxC than those of GA. Conclusions: T. tiflisiensis root extracts exhibited greater protein denaturation inhibitory and antibacterial activities, whereas leaf extracts showed greater antioxidant activity. GA modulated these activities in a concentration- and tissue-dependent manner, with enhancement observed at specific concentrations and attenuation at higher concentrations in the protein denaturation assay. Exploratory molecular docking predicted favorable interactions of RA with the bacterial enzymes MurF and LpxC. These findings support further investigation of the bioactive properties of T. tiflisiensis and the mechanisms underlying their modulation by GA.

Plant proteins represent both important nutritional resources and reservoirs of encrypted bioactive peptide sequences. However, protein nutritional quality and peptide bioactivity are distinct properties: a favorable indispensable amino acid profile or high digestibility does not necessarily predict the generation of biologically active peptides. This review examines plant-derived proteins and peptides with particular emphasis on the relationship between parent-protein composition, peptide release, sequence-dependent activity, analytical identification, and biological validation. The relevant peer-reviewed literature was critically evaluated, with detailed search and selection criteria. Major plant protein sources, peptide-generation strategies, purification and mass-spectrometric characterization approaches, and reported antioxidant, antimicrobial, antihypertensive, antidiabetic, anti-inflammatory, and other regulatory activities are discussed. Particular attention is given to experimentally validated peptide sequences and to the hierarchy of evidence from computational prediction and biochemical assays to cellular, animal, and human studies. Structure–activity relationships are interpreted cautiously, recognizing that peptide length, charge, hydrophobicity, and individual amino acid residues are contributory rather than independently predictive features. The review further integrates computational prediction with experimental validation and highlights gastrointestinal stability, intestinal transport, bioavailability, safety, scalability, and regulatory considerations as major translational challenges. This evidence-based framework distinguishes nutritional adequacy from peptide function and supports more rigorous development of plant-derived bioactive peptides for functional-food, nutraceutical, and related applications.

The increasing incidence of fungal infections caused by Candida albicans and the multidrug-resistant emerging pathogen Candidozyma auris (formerly Candida auris) represents a major global healthcare challenge. Current antifungal therapies are limited by toxicity, drug–drug interactions, rapid resistance emergence, and poor efficacy against fungal biofilms, highlighting the urgent need for novel antifungal agents with new mechanisms of action. In this context, marine ecosystems have emerged as an exceptional source of structurally diverse natural products with promising pharmacological properties. This review summarizes recent advances in marine-derived compounds exhibiting antifungal activity against C. albicans and C. auris. The available evidence is organized according to major chemical classes, including polyketides, alkaloids, terpenoids and meroterpenoids, and miscellaneous marine metabolites. Particular emphasis is placed on antifungal potency, mechanisms of action, anti-biofilm and anti-virulence activities, synergistic interactions with conventional antifungal drugs, and reported structure–activity relationships. Several compounds demonstrated potent direct antifungal activity, while others showed mainly antibiofilm, anti-virulence, synergistic, or resistance-modifying effects despite having limited direct growth-inhibitory activity. Although many promising candidates remain at the preclinical stage, current evidence suggests that marine natural products have significant potential as scaffolds for discovering and developing next-generation antifungal therapeutics. Continued interdisciplinary research integrating natural product chemistry, medicinal chemistry, pharmacology, and translational studies will be essential for transforming these compounds into clinically useful antifungal drugs.

Hantaviruses are zoonotic RNA viruses responsible for two severe human diseases: hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS). Worldwide case fatality rates vary from 1% to 40%. Although single-layer genomics, transcriptomics, proteomics, and metabolomics studies have advanced our understanding of hantavirus biology, true multi-omics integration remains scarce, leaving systems-level mechanisms of disease severity and host–pathogen interactions unresolved. High-throughput omics technologies have greatly advanced the study of hantavirus–host interactions. This review summarizes genomic, transcriptomic, proteomic, metabolomic, and AI-enabled approaches in hantavirus research. Genomic studies have clarified viral diversity, evolution, and reassortment, while transcriptomics has identified regulatory networks governing endothelial and innate immune responses. Proteomics has revealed host proteins involved in immune regulation, endothelial dysfunction, and potential therapeutic targeting, whereas metabolomics indicates substantial metabolic reprogramming during infection, although dedicated studies remain limited. AI-based approaches are increasingly applied to outbreak prediction, surveillance, and risk modeling. Drawing on successful multi-omics frameworks developed for other viral infections, we discuss opportunities to improve biomarker discovery, surveillance, therapeutic development, and future precision medicine strategies for hantavirus infections.

Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we outline future research directions, including hybrid AI–biological models, AI-supported viral surveillance in healthcare environments, and the integration of explainable AI to enhance clinical trust.

Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We will discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we will highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we will outline future research directions, including hybrid AI-biological models, IoT-supported viral surveillance in smart environments, and the integration of explainable AI to enhance clinical trust.