The paper reviews the challenges about haemonchosis—a significant and common parasitic infection of small ruminants. Haemonchus contortus is a highly pathogenic parasite that localises in the abomasum of affected animals and exerts its pathogenicity by blood-sucking activity, adversely affecting the health and productivity of animals. The first challenge is the uneven distribution of the infection globally, this being more prevalent in tropical and subtropical and warm temperate and summer rainfall regions than in cool and cold temperate and arid regions; hence, this leads in differences in the approaches required for its control. Another challenge is the widespread presence of Haemonchus strains resistant to the various anthelmintics available: Benzimidazoles, imidazothiazoles, macrocyclic lactones, closantel and monepantel, which makes the control of the infection difficult. The third challenge refers to the difficulty of diagnosing the disease, given that field evidence can provide suspicion about the infection, which needs to be subsequently confirmed by laboratory tests through parasitological or molecular techniques. The final challenge relates to the difficulties in the control of the infection and the necessity to use pharmaceutical products cautiously and with a planned approach, to avoid further development of anthelmintic resistance, also given that use of a recently licenced vaccine is not widespread. In conclusion, at the moment, we should be concerned, but not worried, about this infection, and apply correctly the appropriate health management plans.

Objectives were to investigate somatic cell counts (SCC) and total bacterial counts (TBC) in the raw bulk-tank milk of sheep flocks in Greece, to study factors potentially influencing increased SCC and TBC in the bulk-tank milk of sheep and to evaluate possible associations of SCC and TBC with milk content. Throughout Greece, 325 dairy sheep flocks were visited for collection of milk sampling for somatic cell counting, microbiological examination and composition measurement. Geometric mean SCC were 0.488 × 10⁶ cells mL⁻¹; geometric mean TBC were 398 × 10³ cfu mL⁻¹; 228 staphylococcal isolates were recovered form 206 flocks (63.4%). Multivariable analyses revealed annual incidence risk of clinical mastitis, age of the farmer and month into lactation period (among 53 variables) to be significant for SCC > 1.0 × 10⁶ cells mL⁻¹ and month into lactation period at sampling and availability of mechanical ventilators (among 58 variables) to be significant for TBC > 1500 × 10³ cfu mL⁻¹. Negative correlation of SCC with fat, total protein and lactose and positive correlation of SCC with added water were found. With SCC > 1.0 × 10⁶ cells mL⁻¹, significant reduction of protein content (2%) was observed, whilst in flocks with SCC > 1.5 × 10⁶ cells mL⁻¹, significantly lower annual milk production per ewe (42.9%) was recorded.

‘One Health’ summarises the idea that human health and animal health are interdependent and bound to the health of ecosystems. The purpose of proteomics methodologies and studies is to determine proteins present in samples of interest and to quantify changes in protein expression during pathological conditions. The objectives of this paper are to review the application of proteomics technologies within the One Health concept and to appraise their role in the elucidation of diseases and situations relevant to One Health. The paper develops in three sections. Proteomics Applications in Zoonotic Infections part discusses proteomics applications in zoonotic infections and explores the use of proteomics for studying pathogenetic pathways, transmission dynamics, diagnostic biomarkers and novel vaccines in prion, viral, bacterial, protozoan and metazoan zoonotic infections. Proteomics Applications in Antibiotic Resistance part discusses proteomics applications in mechanisms of resistance development and discovery of novel treatments for antibiotic resistance. Proteomics Applications in Food Safety part discusses the detection of allergens, exposure of adulteration, identification of pathogens and toxins, study of product traits and characterisation of proteins in food safety. Sensitive analysis of proteins, including low-abundant ones in complex biological samples, will be achieved in the future, thus enabling implementation of targeted proteomics in clinical settings, shedding light on biomarker research and promoting the One Health concept.

Staphylococcus epidermidis is an important causal agent of ovine mastitis. A literature search indicated a lack of systematic studies of causal agents of the infection by using multi-locus sequence typing (MLST). The objectives were to analyse MLST-based data and evaluate the antimicrobial resistance of S. epidermidis isolates from ovine mastitis in Greece. The database included 1593 isolates from 46 countries: 1215 of human, 195 of environmental and 134 of animal origin, distributed into 949 sequence types (STs) and cumulatively with 450 alleles therein. Among mastitis isolates, bovine isolates were distributed into 36 different STs and ovine ones into 15 STs. The 33 isolates from ovine mastitis in Greece were in 15 different STs, 6 of these (ST677, ST678, ST700, ST 709, ST710, ST711) assigned for the first time; in addition, 5 alleles (65 for arcC, 59 for aroE, 56 and 57 for gtr and 48 for tpiA) were identified for the first time. The spanning tree of these isolates included 15 nodes and 14 edges (i.e., branches). Among these isolates, 19 showed resistance to antimicrobial agents (tetracycline, penicillin, fucidic adic, erythromycin, clindamycin, cefoxitin). Resistance-related genes (tetK, tetT, msrA, tetM, tetS, ermC, mecA) were detected. There was no association between STs and resistance to antimicrobial agents. Isolates with antimicrobial resistance were recovered more often from flocks where hand-milking was practised.

The objectives of this work were (a) to present the changes in climatic parameters from 1989 to 2019, in 444 locations throughout Greece, where small ruminant farms have been based and (b) to present associations of the changes in the climatic parameters with clinical data related to small ruminant health. Climatic variables (1989–2019) were obtained for 444 locations with small ruminant farms throughout Greece. During this period, significant increases were noted in temperature-related parameters (annually 0.05 °C for average temperature and 0.14 °C for temperature range) and precipitation (annually 0.03 mm). There were significant differences in climatic conditions between locations of farms in accord with the management system applied therein, as well as in accord with the breed of animals on the farms (e.g., higher average temperature in locations with Greek breeds, higher temperature range in locations with imported breeds). There were significant associations of temperature-related parameters with the annual frequency of cases of neonatal hypothermia seen at a veterinary teaching hospital, as well as with the average proportion of Haemonchus contortus larvae in faecal samples and the frequency of cases of H. contortus resistance reported by a veterinary parasitology laboratory.

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development.