Non-alcoholic fatty liver disease (NAFLD), now referred to as metabolic dysfunction-associated steatotic liver disease (MASLD), is the most prevalent liver disorder globally, linked to obesity, type 2 diabetes, and cardiovascular risk. Understanding its potential progression from simple steatosis to cirrhosis and hepatocellular carcinoma (HCC) is crucial for patient management and treatment strategies. The disease’s complexity requires innovative approaches for early detection and personalized care. Omics technologies—such as genomics, transcriptomics, proteomics, metabolomics, and exposomics—are revolutionizing the study of MASLD. These high-throughput techniques allow for a deeper exploration of the molecular mechanisms driving disease progression. Genomics can identify genetic predispositions, whilst transcriptomics and proteomics reveal changes in gene expression and protein profiles during disease evolution. Metabolomics offers insights into the metabolic alterations associated with MASLD, while exposomics links environmental exposures to MASLD progression and pathology. By integrating data from various omics platforms, researchers can map out the intricate biochemical pathways involved in liver disease progression. This review discusses the roles of omics technologies in enhancing the understanding of disease progression and highlights potential diagnostic and therapeutic targets within the MASLD spectrum, emphasizing the need for non-invasive tools in disease staging and treatment development.

The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value). The K-means tool showed the highest significance for the classification of farms into two clusters for the construction of the computational model: median (interquartile range) prevalence of subclinical mastitis among farms was 20.0% (interquartile range: 15.8%) and 30.0% (16.0%) (p = 0.002). Supervised learning tools were subsequently used to predict the level of prevalence of the infection: decision trees, k-NN, neural networks, and Support vector machines. For each of these, combinations of hyperparameters were employed; 83 models were produced, and 4150 assessments were made in total. A computational model obtained by means of Support vector machines (kernel: ‘linear’, regularization parameter C = 3) was selected. Thereafter, the model was assessed through the results of the prevalence of subclinical mastitis in 373 records from sheep flocks unrelated to the ones employed for the selection of the model; the model was used for evaluation of the correct classification of the data in each of 373 sets, each of which included a test (prediction) subset with one record that referred to the farm under assessment. The median prevalence of the infection in farms classified by the model in each of the two categories was 10.4% (5.5%) and 36.3% (9.7%) (p < 0.0001). The overall accuracy of the model for the results presented by the K-means tool was 94.1%; for the estimation of the level of prevalence (<25.0%/≥25.0%) in the farms, it was 96.3%. The findings of this study indicate that machine learning algorithms can be usefully employed in predicting the level of subclinical mastitis in dairy sheep farms. This can facilitate setting up appropriate health management measures for interventions in the farms.

Non-alcoholic fatty liver disease (NAFLD), now referred to as metabolic dysfunction-associated steatotic liver disease (MASLD), is the most prevalent liver disorder globally, linked to obesity, type 2 diabetes, and cardiovascular risk. Understanding its potential progression, from simple steatosis to cirrhosis and hepatocellular carcinoma (HCC), is crucial for patient management and treatment strategies. The disease's complexity requires innovative approaches for early detection and personalized care. Omics technologies—such as genomics, transcriptomics, proteomics, metabolomics and exposomics—are revolutionizing the study of MASLD. These high-throughput techniques allow for a deeper exploration of the molecular mechanisms driving disease progression. Genomics can identify genetic predispositions, whilst transcriptomics and proteomics reveal changes in gene expression and protein profiles during disease evolution. Metabolomics offer insights into the metabolic alterations associated with MASLD, while exposomics links environmentak exposures to MASLD progression and pathology. By integrating data from various omics platforms, researchers can map out the intricate biochemical pathways involved in liver disease progression. This review discusses the roles of -omics technologies in enhancing understanding of disease progression and highlights potential diagnostic and therapeutic targets within MASLD spectrum, emphasizing the need for non-invasive tools in disease staging and treatment development.

A cross-sectional study was performed in 325 sheep and 119 goat dairy farms in Greece. Samples of bulk-tank milk were examined by standard microbiological techniques for Listeria spp. Listeria monocytogenes was isolated from one (0.3%) and Listeria ivanovii from three (0.9%) sheep farms. No associations between the isolation of L. monocytogenes or L. ivanovii and milk quality were found. No resistance to antibiotics was identified. Three variables emerged as significant predictors of isolation of the organism: the presence of pigs, low average relative humidity and a high number of ewes on the farm. The three L. ivanovii isolates were assessed in silico for identification of plasmids, prophages, antibiotic resistance genes, virulence factors, CRISPRs and CAS genes. Phylogenetic analysis using the core genome revealed that the three strains belonged to the L. ivanovii subsp. ivanovii branch and were especially close to the PAM 55 strain. All strains of the branch appeared to be very similar, with the distance between them being small.

The specific objective of the present study was to develop computational models, by means of which predictions could be performed regarding the quality of the bulk-tank milk in dairy sheep and goat farms. Our hypothesis was that use of specific variables related to the health management applied in the farm can facilitate the development of predictions regarding values related to milk quality, specifically for fat content, protein content, fat and protein content combined, somatic cell counts, and total bacterial counts. Bulk-tank milk from 325 sheep and 119 goat farms was collected and evaluated by established techniques for analysis of fat and protein content, for somatic cell counting, and for total bacterial counting. Subsequently, computational models were constructed for the prediction of five target values: (a) fat content, (b) protein content, (c) fat and protein, (d) somatic cell counts, and (e) total bacterial counts, through the use of 21 independent variables related to factors prevalent in the farm. Five machine learning tools were employed: decision trees (18 different models evaluated), random forests (16 models), XGBoost (240 models), k-nearest neighbours (72 models), and neural networks (576 models) (in total, 9220 evaluations were performed). Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. In sheep farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 3.95%); for the prediction of fat and protein content combined, neural networks was selected (6.00%); and for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (6.55%); no tool provided useful predictions for fat content and for total bacterial counts. In goat farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 6.17%); for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (4.93% and 5.00%); and for the prediction of total bacterial counts, neural networks was selected (8.33%); no tool provided useful prediction models for fat content and for fat and protein content combined. The results of the study will be of interest to farmers, as well as to professionals; the findings will also be useful to dairy processing factories. That way, it will be possible to obtain a distance-aware, rapid, quantitative estimation of the milk output from sheep and goat farms with sufficient data attributes. It will thus become easier to monitor and improve milk quality at the farm level as part of the dairy production chain. Moreover, the findings can support the setup of relevant and appropriate measures and interventions in dairy sheep and goat farms.

The present study is an evaluation of published papers in the field of scientometrics–bibliometrics in veterinary or animal studies. The objective was the quantitative evaluation of the scientific content and the bibliometric details of papers that had used scientometrics as a methodological approach to assess various topics within the broad field of veterinary or animal studies. For the search of published papers, the Web of Science database was used; the search string {ALL FIELDS = [scientometric* OR bibliometric*]} AND {ALL FIELDS = [veterinary OR animal*]} was employed. In all, 163 papers were evaluated individually. There was a progressive increase in the number of papers published annually (slope 0.314 ± 0.063) which originated from 58 countries, mostly from the United States of America (n = 24) and 306 organizations. The databases employed most frequently for record search were the Web of Science, Scopus and PubMed (in 105, 60 and 21 papers, respectively); the median timespan of the searches was 25 years (interquartile range (IQR): 31) and the median number of records was 1082.5 (IQR: 4708) (min.: 14, max.: 207,894). The papers were published in 106 journals (Animals (n = 21), Scientometrics (n = 8)), classified into 56 Web of Science categories (‘veterinary sciences’ (n = 26 journals)). Searches related to 88 different topics. There were 517 unique keywords in the papers, among which ‘bibliometric(s)’ (n = 45) occurred most often. The median number of co-authors per paper was 4 (IQR: 3) (min.: 1, max.: 17), with a total of 689 individual authors. Most papers (n = 110 (67.5%)) were published under open access. The median number of citations received per paper was 4 (IQR: 9) (max.: 68) and the h-index was 21. In multivariable analysis, the higher number of cited references in papers and the inclusion of specific animal species in the record search emerged as significant for the number of citations received per paper yearly.