Abstract
Radiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis. © The Author(s) 2024.

Abstract
Radiography is the primary modality for diagnosing canine hip dysplasia (CHD), with visual assessment of radiographic features sometimes used for accurate diagnosis. However, these features typically constitute small regions of interest (ROI) within the overall image, yet they hold vital diagnostic information and are crucial for pathological analysis. Consequently, automated detection of ROIs becomes a critical preprocessing step in classification or segmentation systems. By correctly extracting the ROIs, the efficiency of retrieval and identification of pathological signs can be significantly improved. In this research study, we employed the most recent iteration of the YOLO (version 8) model to detect hip joints in a dataset of 133 pelvic radiographs. The best-performing model achieved a mean average precision (mAP50:95) of 0.81, indicating highly accurate detection of hip regions. Importantly, this model displayed feasibility for training on a relatively small dataset and exhibited promising potential for various medical applications.

Abstract
Adequate radiographic positioning on the X-ray table is paramount for canine hip dysplasia (HD) screening. The aims of this study were to evaluate femoral parallelism on normal ventrodorsal hip extended (VDHE) view and the effect of femoral angulation (FA) on Norberg Angle (NA) and Hip Congruency Index (HCI). The femoral parallelism was evaluated comparing the alignment of the long femoral axis with the long body axis in normal VDHE views and the effect of FA on NA and HCI on repeated VDHE views with different levels of FA. The femoral long axis in normal VDHE views showed a ranged of FA from -4.85 degrees to 5.85 degrees, mean +/- standard deviation (SD) of -0.06 +/- 2.41 degrees, 95% CI [-4.88, 4.76 degrees]. In the paired views, the mean +/- SD femur adduction of 3.69 +/- 1.96 degrees led to a statistically significant decrease NA, and HCI, and femur abduction of 2.89 +/- 2.12 led to a statistically significant increase in NA and HCI (p < 0.05). The FA differences were also significantly correlated with both NA differences (r = 0.83) and HCI differences (r = 0.44) (p < 0.001). This work describes a methodology that allows evaluation of femoral parallelism in VDHE views and the results suggest that femur abduction yielded more desirable NA and HCI values and adduction impaired NA and HCI values. The positive linear association of FA with NA and HCI allows the use of regression equations to create corrections, to reduce the influence of poor femoral parallelism in the HD scoring.

Abstract
This work aimed to define a humane endpoint scoring system able to objectively identify signs of animal suffering in a rat model of type 2 diabetes. Sprague-Dawley male rats were divided into control and induced group. The induced animals drink a 10% fructose solution for 14 days. Then, received an administration of streptozotocin (40 mg/kg). Animals’ body weight, water and food consumption were recorded weekly. To evaluate animal welfare, a score sheet with 14 parameters was employed. Blood glucose levels were measured at three time points. After seven weeks of initiating the protocol, the rats were euthanized. The induced animals showed weight loss, polyuria, polyphagia, and polydipsia. According to our humane endpoints table, changes in animal welfare became noticeable after the STZ administration. None of the animals hit the critical score limit (four). Data showed that the most effective parameters to assess welfare in this type 2 diabetes rat induction model were dehydration, grooming, posture, abdominal visualization, and stool appearance. The glycemia was significantly higher in the induced group when compared to the controls (p < 0.01). Induced animals’ murinometric and nutritional parameters were significantly lower than the controls (p < 0.01). Our findings suggest that in this rat model of type 2 diabetes with STZ-induced following fructose consumption, our list of humane endpoints is suitable for monitoring the animals’ welfare.

Abstract
This paper proposes a new robust approach to nonlinear clustering based on the Principal Component Analysis (PCA) approach. A robust c-means partition is derived by using the natural PCA noise-rejection mechanism and the nonlinearity captured by a sliding process of the clusters prototype. A non-linear extension of PCA has been developed for detecting the lower-dimensional representation of real world data sets. For these cases local linear approaches are used widely because of their computational simplicity and understandability. We will present a new method that joins (merges) the fuzzy clustering algorithm with a local sliding PCA analysis. With this strategy it is possible to identify the non-linear relations and obtain morphological information of the data. The Sliding PCA-Fuzzy cluster algorithm (SPCA-FCA) is a fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters, performed on the neighborhood of the center of cluster and normal approximations in order to estimate a tangent surface that characterizes the trend and curvature of the data points or contours region. Numerical experiments demonstrate that the proposed method is useful for capturing cluster cores by rejecting noise samples, and we can easily assess cluster validity by using cluster-crossing curves.

Abstract
Virtually ever since it was first commercialized in 1995, there have been several studies focusing on the use of olive leaf extract (OLE) as a natural therapy and its medical properties. The aim of this study was to investigate the effects of three different concentrations of OLE on the function of mice livers over the course of 14 weeks. Female ICR mice were divided into four groups, depending on OLE concentration used: 0%, 0.25%, 0.5%, and 0.75%. Alanine aminotransferase, alkaline phosphatase, total bilirubin and albumin serum concentrations were all measured. Histopathological changes of the liver were observed after haematoxylin and eosin, reticulin, and Masson's trichrome staining was carried out while liver mitochondrial bioenergetics were also evaluated. Alanine aminotransferase and alkaline phosphatase serum enzyme activities increased significantly in the groups in which 0.5% and 0.75% OLE concentrations were used. Histologically, all the groups exposed to OLE exhibited hyperplasia of the bile ducts, cholestasis, hepatocyte necrosis and inflammatory infiltrated. Hepatic fibrosis was observed in the groups featuring 0.5% and 0.75% OLE concentrations. The mitochondrial membrane potential, respiratory control ratio and ADP/O of samples from animals fed the higher OLE concentration was significantly decreased when compared to the control group.