The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiota’s role in human health and disease, particularly focusing on CIDs and probiotic interventions.

Healthcare-associated infections (HAIs) represent one of the most persistent challenges in modern healthcare delivery, affecting millions of patients worldwide and imposing substantial clinical and economic burdens on healthcare systems. The emergence of antimicrobial resistance (AMR) has further complicated infection management, creating an urgent need for innovative therapeutic and preventive strategies. Current strategies for combating AMR in hospital settings encompass comprehensive infection prevention and control measures, antimicrobial stewardship programs, enhanced environmental cleaning protocols and innovative surface modification technologies. Nanotechnology has emerged as a valuable approach to address the limitations of conventional antimicrobial strategies. Various nanomaterial categories offer innovative platforms for developing novel treatment strategies and for providing advantages including reduced toxicity through lower dosage requirements, diminished resistance development potential, and enhanced antibacterial effects through combined action mechanisms. Particularly, metal-based nanoparticles and their oxides demonstrate exceptional antimicrobial properties through multiple mechanisms including membrane damage, protein binding and reactive oxygen species generation. This comprehensive review examines the current landscape of hospital-acquired infections, the growing threat of antimicrobial resistance, and the promising role of nanotechnology-based solutions, with particular emphasis on silver nanoparticles as innovative tool for HAI control in clinical settings. Recent advances in nanotechnology-enabled antimicrobial coatings are assessed along with their clinical translation in hospital settings, identifying key barriers concerning material durability, safety profiles, and regulatory pathways.

Sericin, a natural glycoprotein constituting 20–30% of the silk cocoon, has emerged as a promising biomaterial due to its excellent biological properties, including biocompatibility, antioxidant properties and potential applications in regenerative medicine. The quality and the features of sericin are strongly dependent on the extraction and purification methods, which can employ mild conditions to preserve the molecular integrity of the protein or recovery techniques from waste streams produced during the industrial degumming processes. The silk industry prioritizes fiber yield over protein preservation, so often harsh alkaline conditions at high temperatures are adopted. These divergent approaches result in fundamentally different products with distinct molecular characteristics and functional capabilities. This review comprehensively examines the current technological approaches for sericin extraction techniques and for its recovery from textile industry waste, focusing on how these aspects affect the biological properties of the protein and the potential applications.

Laboratory medicine is crucial for clinical decision-making, yet result interpretation often remains challenging for patients. This study evaluates the effectiveness of an Artificial Intelligence (AI)-powered conversational system in interpreting laboratory test results, utilizing a closed-box training approach for a Claude-based virtual chatbot focused exclusively on laboratory data interpretation without clinical diagnosis. The system was tested using 100 laboratory reports from three Italian laboratories, encompassing diverse biochemical parameters and measurement standards. The laboratories employed different analytical platforms and methodologies, enabling evaluation of the chatbot’s ability to interpret results across varied instrumental settings. The interpretation accuracy was rigorously assessed through peer review by three independent medical experts with extensive laboratory medicine experience. The Claude model demonstrated complete accuracy with zero hallucinations, attributed to the controlled training environment, domain-specific prompts, and pure generation mechanisms without external data access. Patient feedback from 70 participants showed high satisfaction rates, with 90% providing positive ratings. This study demonstrates that carefully designed AI models can effectively bridge the gap between raw laboratory data and patient understanding, potentially transforming laboratory reporting systems while maintaining high accuracy and avoiding diagnostic territory. These findings have significant implications for patient empowerment and healthcare communication efficiency.

The escalating global crisis of antimicrobial resistance demands innovative therapeutic strategies that transcend conventional approaches. This comprehensive review examines the groundbreaking synergistic integration of silver nanoparticles (AgNPs) with silk proteins (fibroin and sericin from Bombyx mori) to create advanced nanocomposite materials for biomedical applications. While extensive literature exists for AgNPs and silk proteins individually, a limited number of studies have explored their synergistic combination. This review consolidates this fragmented knowledge to establish the foundational framework for an emerging field. The unique properties of silk proteins as natural reducing, stabilizing, and capping agents enable environmentally friendly AgNPs synthesis while creating intelligent therapeutic platforms with emergent properties. These hybrid materials demonstrate superior performance in terms of antimicrobial efficacy, biocompatibility, and accelerated wound healing compared to the individual components. The nanocomposites exhibit broad-spectrum activity against multidrug-resistant pathogens while maintaining exceptional biocompatibility and promoting tissue regeneration. This integration represents a promising evolution toward biomimetic therapeutic platforms that work in harmony with biological systems, offering sustainable solutions to contemporary healthcare challenges.

Objective: This study aimed to validate the clinical utility of a salivary molecular platform (Oral Predict^®) for periodontal pathogen detection across preventive, therapeutic, and maintenance settings. Methods: A longitudinal randomized study was conducted involving 78 adults who provided saliva samples at baseline, one month, and three months after professional dental hygiene. Participants were randomized into two groups: control group (n = 39) and probiotic group with Oral Predict^® probiotic supplementation (n = 39). Crude saliva was processed directly without nucleic acid extraction and analyzed by multiplex real-time PCR using either the compact Real-time PCR system or standard thermocyclers. Results: At baseline, Fusobacterium nucleatum was the most prevalent pathogen (84.6%), followed by Tannerella forsythia (53.8%) and Porphyromonas gingivalis (46.2%). The Total Pathogen Burden Score (TPBS) showed progressive increases with age, smoking, and poor oral hygiene, and was significantly higher in participants with gingival bleeding. Among individual pathogens, no significant associations were observed with periodontitis staging or grading. Professional hygiene induced mean reductions of 1–2 logs across all pathogens, with TPBS decreasing from 8.7 ± 3.2 to 4.1 ± 2.8 (p < 0.001). At three months, 69.2% of the control group experienced bacterial rebound, whereas 85% of probiotic users sustained or improved bacterial reductions. Conclusions: Salivary molecular testing provides a robust, non-invasive approach for periodontal pathogen detection, treatment monitoring, and long-term maintenance assessment. The flexibility of the Oral Predict^® platform across point-of-care and laboratory settings, combined with automated interpretation, supports integration into preventive protocols and personalized periodontal care. These findings demonstrate the potential of saliva-based molecular diagnostics to shift periodontal management from reactive to predictive and precision-based strategies.