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Background Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucose monitoring, and lifestyle adjustments. The accurate prediction of the short-term course of glucose levels in the subcutaneous space in T1D people, as measured by a continuous glucose monitoring (CGM) system, is essential for improving glucose control by avoiding harmful hypoglycaemic and hyperglycaemic glucose swings, facilitating precise insulin management and individualized care and, in turn, minimizing long-term vascular complications. Methods In this study, we propose an ensemble univariate short-term predictive model of the subcutaneous glucose concentration in T1D targeting at improving its error in the hypoglycaemic region. As such, the underlying basis functions are selected to minimize the percentage of erroneous predictions (EP) in the hypoglycaemic region, with EP being evaluated with continuous glucose error grid analysis (CG-EGA). The dataset comprises 29 individuals with T1D, who were monitored for 2 to 4 weeks during the GlucoseML prospective observational clinical study. Results Among six different basis models (i.e., linear regression (LR), automatic relevance determination (ARD), support vector regression (SVR), Gaussian process regression (GPR), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)), XGBoost and SVR showed a dominant performance in the hypoglycaemic region and were selected as the constituent basis models of the ensemble model. The results indicate that the ensemble model significantly reduces the percentage of EP in the hypoglycaemic region for a 30 min prediction horizon to 19% as compared with its individual basis models (i.e., XGBoost and SVR), whilst its errors over the entire glucose range (hypoglycaemia, euglycaemia, and hyperglycaemia) are similar to those of the basis models. Conclusions The consideration of the performance of the basis functions in the hypoglycaemic region during the construction of the ensemble model contributes to enhancing their joint performance in that specific area. This could lead to more precise insulin management and a reduced risk of short-term hypoglycaemic fluctuations.

The accurate prediction of blood glucose is critical for the effective management of diabetes. Modern continuous glucose monitoring (CGM) technology enables real-time acquisition of interstitial glucose concentrations, which can be calibrated against blood glucose measurements. However, a key challenge in the effective management of type 2 diabetes lies in forecasting critical events driven by glucose variability. While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. These limitations highlight the need for multimodal approaches that integrate additional personalized physiological information. One of the primary reasons for multimodal approaches not being widely studied in this field is the bottleneck associated with the availability of subjects’ health records. In this paper, we propose a multimodal approach trained on sequences of CGM values and enriched with physiological context derived from health records of 40 individuals with type 2 diabetes. The CGM time series were processed using a stacked Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network followed by an attention mechanism. The BiLSTM learned long-term temporal dependencies, while the CNN captured local sequential features. Physiological heterogeneity was incorporated through a separate pipeline of neural networks that processed baseline health records and was later fused with the CGM modeling stream. To validate our model, we utilized CGM values of 30 min sampled with a moving window of 5 min to predict the CGM values with a prediction horizon of (a) 15 min, (b) 30 min, and (c) 60 min. We achieved the multimodal architecture prediction results with Mean Absolute Point Error (MAPE) between 14 and 24 mg/dL, 19–22 mg/dL, 25–26 mg/dL in case of Menarini sensor and 6–11 mg/dL, 9–14 mg/dL, 12–18 mg/dL in case of Abbot sensor for 15, 30 and 60 min prediction horizon respectively. The results suggested that the proposed multimodal model achieved higher prediction accuracy compared to unimodal approaches; with upto 96.7% prediction accuracy; supporting its potential as a generalizable solution for interstitial glucose prediction and personalized management in the type 2 diabetes population.

Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6–10), glucose management indicator (GMI) 6.9% (5.7–8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101–235) mg/dL, TIR (time in range, glucose 70–180 mg/dL) 66% (25–94), TBR70 (time below range, 54–69 mg/dL) 4% (0–16), TBR54 (<54 mg/dL) 1% (0–11), TAR180 (time above range, 181–250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0–40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight.

We investigated the association between wild canid predators reported near sheep farms throughout Greece and somatic cell counts in bulk-tank milk as a reflection of milk quality. The study included 325 dairy sheep flocks, where bulk-tank milk somatic cell counts and total bacterial counts were measured and staphylococci were isolated. Farms were divided into three groups: Cohort A (farms with no reports of wild canid predators nearby), B (farms with canid predators (golden jackal and grey wolf) nearby yet with no experience of livestock losses to predation) and C (farms with canid predators nearby and livestock losses to predation). Somatic cell counts in bulk-tank milk of Cohort C farms were significantly higher, + 43% and + 29%, compared to those for Cohorts A and B, respectively: 0.617 × 10 6  cells mL −1 versus 0.433 × 10 6 or 0.477 × 10 6  cells mL −1 , respectively. The presence of wild canid predators near sheep farms was associated with lower quality milk potentially indicative of stress consistent with the potential effects of a landscape of fear. Increasing biosecurity measures at livestock farms, e.g., fencing, and presence of livestock guard dogs could minimise predation risk, whilst also improving livestock welfare by reducing predator-associated stress.

The objectives of the present work were the investigation of gastrointestinal parasitic infections in dogs in small ruminant farms in Greece, the elucidation of potential predictors for these infections and the description of practices related to administration of anthelmintics to dogs. This study was carried out in 444 small ruminant farms in Greece. Faecal samples were collected directly from the rectum of the dogs in the farms. The samples were processed by means of conventional parasitological techniques, specifically, a combined sedimentation flotation technique. There were dogs in 92.8% of the farms, with a median number of four dogs per farm. The following variables were associated with the presence of dogs in the farms: the presence of wild mammal predators near the farms, the increased daily period of farmers’ presence at the farm, goats as the livestock species at the farm and the management system applied in the farm. Helminth eggs were detected in samples from 72.6% of the farms. The main helminth eggs detected were those of hookworms (Uncinaria/Ancylostoma) and Toxocara canis, in 68.6% and 51.3% of the farms, respectively. In our multivariable analyses, an association emerged between the presence of canid predators near a farm and the detection of these helminths in faecal samples: in 76% and 60% of the samples, respectively, versus in 58% and 39% of the samples from farms with no canid presence. Of farmers with dogs, 16.0% reported that they omitted the administration of anthelmintics to the animals. In multivariable analysis, the semi-extensive or extensive management system applied in the farm, the lower annual milk production per animal and the lack of collaboration with a veterinary practice were the significant predictors for the omission of anthelmintic administration to the farm dogs. There was also a clear association in the omission of anthelmintic administration to the dogs and to the livestock on the farm. The most frequently administered anthelmintic was praziquantel, which was used in 93.6% of the farms.

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.

The present study refers to an extensive investigation of lameness performed countrywide in Greece, on 325 sheep and 119 goat farms. The specific objectives of this work were to present data on the occurrence of lameness on sheep and goat farms and to identify variables (including variables related to climatic factors) associated with the disorder on the farms. Farms were visited and animals on the farm were assessed for the presence of lameness; further, an interview was carried out with the farmer to obtain information regarding practices applied on the farm. Climatic variables at the location of each farm were derived from NASA research. The within farm prevalence rate varied from 0.0% to 25.0% in sheep flocks and from 0.0% to 30.0% in goat herds. The mean ± standard error (median (interquartile range)) within farm prevalence rate among sheep farms was 1.9% ± 0.2 (0.0% (0.0%)); among goat farms, it was 2.6% ± 0.5% (0.0% (0.0%)). Multivariable analysis for within farm prevalence of lameness revealed three significant predictors in sheep farms: application of vaccination against foot-rot, increased precipitation at the farm location and longer annual grazing period for sheep, and one in goat farms: increased precipitation at the farm location. Treatment of lameness involved mostly administration of antibiotics (on 104 farms); the antibiotics administered most often were lincomycin (on 69 farms) and oxytetracycline (on 33 farms). There was a tendency for higher median within farm prevalence of lameness among farms where no antibiotic administration was practiced. Finally, 6.2% of sheep farmers and 4.2% of goat farmers considered lameness as an important health problem for the animals, specifically the third and fifth most important problem on the respective farms.

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.

The objectives of this study were (a) to detect zoonotic gastrointestinal pathogens in faecal samples of horses using the FilmArray® GI Panel and (b) to identify variables potentially associated with their presence. Faecal samples collected from 224 horses obtained during a countrywide study in Greece were tested by means of the BioFire® FilmArray® Gastrointestinal (GI) Panel, which uses multiplex-PCR technology for the detection of 22 pathogens. Gastrointestinal pathogens were detected in the faecal samples obtained from 97 horses (43.3%). Zoonotic pathogens were detected more frequently in samples from horses in courtyard housing (56.0%) than in samples from horses in other housing types (39.7%) (p = 0.040). The most frequently detected zoonotic pathogens were enteropathogenic Escherichia coli (19.2% of horses) and Shiga-like toxin-producing E. coli stx1/stx2 (13.8%). During multivariable analysis, two variables emerged as significant predictors for the outcome ‘detection of at least one zoonotic pathogen in the faecal sample from an animal’: (a) the decreasing age of horses (p = 0.0001) and (b) the presence of livestock at the same premises as the horses (p = 0.013). As a significant predictor for the outcome ‘detection of two zoonotic pathogens concurrently in the faecal sample from an animal’, only the season of sampling of animals (autumn) emerged as significant in the multivariable analysis (p = 0.049). The results indicated a diversity of gastrointestinal pathogens with zoonotic potential in horses and provided evidence for predictors for the infections; also, they can serve to inform horse owners and handlers regarding the possible risk of transmission of pathogens with zoonotic potential. In addition, our findings highlight the importance of continuous surveillance for zoonotic pathogens in domestic animals.