Federal Ministry of Food and Agriculture

Wound healing is a complex physiological process involving coordinated phases of inflammation, tissue repair, and scar formation to restore tissue integrity. Recently, silver nanoparticles (AgNPs) have been investigated for their potential to promote wound healing; however, their application is limited by concerns over cytotoxicity and the development of bacterial resistance. To address these limitations, this study presents the development of a silver-alginate hydrogel (AgSACip) incorporating ciprofloxacin, synthesized using green tea extract act as a biogenic reducing and stabilizing agent. Sodium alginate, a naturally derived macromolecule, serves as a biocompatible matrix to stabilize AgNPs, reduce cytotoxicity, and enhance therapeutic efficacy. The wound healing potential of AgSACip was evaluated in diabetic and burn wound models, and its antibacterial activity, cytotoxic effects, and pharmacokinetics were analyzed using in silico methods. The results demonstrate that AgSACip significantly accelerates wound healing compared to AgNPs and ciprofloxacin alone, particularly in diabetic and burn wounds. Furthermore, AgSACip exhibited enhanced antibacterial activity against bacterial strains isolated from wound pus samples and showed no cytotoxicity toward NIH-3T3 fibroblast cells. These findings highlight the potential of AgSACip, a macromolecule-based hydrogel, as a safe and effective alternative to conventional wound dressings, offering improved wound healing outcomes.

Pain and welfare monitoring is essential for ethical animal testing, but current cage-side assessments are qualitative and subjective. Here we present the GrimACE, a fully standardized and automated cage-side monitoring tool for mice, the most widely used animals in research. The GrimACE uses computer vision to provide automated mouse grimace scale (MGS) assessment together with pose estimation in a safe, dark environment. We validated the system by analyzing pain after brain surgeries (craniotomies) with head implants under two analgesia regimes. Human-expert and automated MGS scores showed very high correlation (Pearson's r = 0.87). Both expert and automated scores revealed that a moderate increase in pain can be detected for up to 48 h after surgeries, but that both a single dose of meloxicam (5 mg/kg subcutaneuously) or three doses of buprenorphine (0.1 mg/kg) + meloxicam (5 mg/kg subcutaneuously) provide adequate and comparable pain management. Simultaneous pose estimation demonstrated that mice receiving buprenorphine + meloxicam showed increased movement 4 h after surgery, indicative of hyperactivity, a well-known side effect of opioid treatment. Significant weight loss was also detected in the buprenorphine + meloxicam treatment group compared with the meloxicam-only group. In addition, detailed BehaviorFlow analysis and automated MGS scoring of control animals suggests that habituation to GrimACE is unnecessary, and that measurements can be repeated multiple times, ensuring standardized postoperative recovery monitoring.

Bacteriophages are considered to have great potential as naturally occurring, antimicrobial agents for use in food production. Phages are ubiquitous in nature and can be isolated from almost all habitats. This review outlines the possibilities, as well as the limitations of their use in food production. Applications of phages in the food sector are described and the limitations of their use, as well as potential risks, are discussed. Approaches for a possible classification as either processing aid or food additive are considered, and the current status of their use in and outside the EU is presented. Finally, the need for research to close identified knowledge gaps is highlighted.

Several scientific initiatives are underway to modernise and advance chemical environmental risk assessment (ERA), supporting the shift from conventional practices toward more mechanistic, integrated, and systems-based approaches. These developments aim to enhance ecological realism, improve predictive capacity, reduce reliance on animal testing, and foster greater alignment across sectors, ultimately contributing to better protection of biodiversity and the environment. Yet, translating these innovative approaches into regulatory practice remains challenging due to fragmented efforts and limited alignment between research and policy. To address this gap, the European Partnership for the Assessment of Risks from Chemicals (PARC) is fostering structured dialogue and stakeholder engagement to align research and innovation with regulatory needs. Within PARC, a proposed solution is the development of the MIND platform—Map, Integrate, Network, Drive—envisioned as a strategic and collaborative space that connects people, practices, and resources across disciplines and regulatory domains. MIND aims to encourage collaboration and knowledge sharing and integration, gradually connecting fragmented initiatives to build a more integrated and interoperable ERA knowledge ecosystem. By encouraging collaboration, improving data sharing, and enhancing policy relevance, MIND seeks to accelerate the uptake of advanced ERA approaches in regulatory practice, with a particular focus on safeguarding biodiversity. Over time, this may contribute to Europe’s sustainability objectives and the ambitions of the European Green Deal.

Non-targeted methods (NTMs) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) show promise in bacterial resistance detection, yet temporal variations in spectral features pose significant challenges. These proteomic patterns, which characterize bacterial phenotypes and pathological functions, may vary over time due to bacterial adaptation, virulence, or resistance mechanisms, resulting in large prediction uncertainties and potentially degrading NTM performance. We present a comprehensive screening method to detect temporal changes in MALDI-TOF spectral patterns, demonstrated using methicillin-resistant and -susceptible Staphylococcus aureus (MRSA/MSSA) isolates collected over several years. Our approach combines convolutional neural networks (CNNs) with statistical methods, including significance testing, kernel density estimation, and receiver operating characteristics for dataset shift detection. We employ Gradient-weighted Class Activation Mapping (Grad-CAM) for post hoc feature description, enabling biochemical characterization of temporal changes. This analysis reveals crucial insights into the dynamic relationship between spectral data patterns over time, addressing key challenges in developing robust NTMs for routine applications.