Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.

Abstract
This study focuses on investigating different CNN architectures and assessing their effectiveness in classifying Diabetic Retinopathy, a diabetes-associated disease that ranks among the primary causes of adult blindness. However, early detection can significantly prevent its debilitating consequences. While regular screening is advised for diabetic patients, limited access to specialized medical professionals can hinder its implementation. To address this challenge, deep learning techniques provide promising solutions, primarily through their application in the analysis of fundus retina images for diagnosis. Several CNN architectures, including MobileNetV2, VGG16, VGG19, InceptionV3, InceptionResNetV2, Xception, DenseNet121, ResNet50, ResNet50V2, and EfficientNet (ranging from EfficientNetB0 to EfficientNetB6), were implemented to assess and analyze their performance in classifying Diabetic Retinopathy. The dataset comprised 3662 Fundus retina images. Prior to training, the networks underwent pre-training using the ImageNet database, with a Gaussian filter applied to the images as a preprocessing step. As a result, the Efficient-Net stands out for achieving the best performance results with a good balance between model size and computational efficiency. By utilizing the EfficientNetB2 network, a model was trained with an accuracy of 85% and a screening capability of 98% for Diabetic Retinopathy. This model holds the potential to be implemented during the screening stages of Diabetic Retinopathy, aiding in the early identification of individuals at risk.

Abstract
Prostate cancer is one of the most common types of cancer in men. The ISUP grade and Gleason Score are terms related to the classification of this cancer based on the histological characteristics of the tissues examined in a biopsy. This paper explains an approach that utilizes and evaluates pre-trained models such as ResNet-50, VGG19, and InceptionV3, regarding their ability to automatically classify prostate cancer and its severity based on images and masks annotated with ISUP grades and Gleason Scores. At the end of the training, the performance of each trained model is presented, as well as the comparison between the original and predicted data. This comparison aims to understand if this approach can indeed be used for a more automated classification of prostate cancer.

Abstract
Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. Unlike most AI models that directly classify CHD severity using convolutional neural networks, this system provides an interpretable, measurement-based output to support a more transparent evaluation. The system combines a keypoint regression model for femoral head center localization with a U-Net-based segmentation model for acetabular edge delineation. It was trained on 7967 images for hip joint detection, 571 for keypoints, and 624 for acetabulum segmentation, all from ventrodorsal hip-extended radiographs. On a test set of 70 images, the keypoint model achieved high precision (Euclidean Distance = 0.055 mm; Mean Absolute Error = 0.0034 mm; Mean Squared Error = 2.52 x 10-5 mm2), while the segmentation model showed strong performance (Dice Score = 0.96; Intersection over Union = 0.92). Comparison with expert annotations demonstrated strong agreement (Intraclass Correlation Coefficients = 0.97 and 0.93; Weighted Kappa = 0.86 and 0.79; Standard Error of Measurement = 0.92 to 1.34 mm). By automating anatomical landmark detection, the system enhances standardization, reproducibility, and interpretability in CHD radiographic assessment. Its strong alignment with expert evaluations supports its integration into CHD screening workflows for more objective and efficient diagnosis and CHD scoring.

Abstract
Aim: Aloysia citrodora has a long history of traditional use in treating various ailments. This study evaluated the in vivo chemopreventive efficacy and systemic toxicity of an extract of A. citrodora in a transgenic mouse model of HPV16 (human papillomavirus type 16)-induced cancer. Methods: The experiment involved six groups (n = 5): group 1 (G1, wild-type (WT), water), group 2 (G2, HPV, water), group 3 (G3, WT, 0.013 g/mL), group 4 (G4, HPV, 0.006 g/mL), group 5 (G5, HPV, 0.008 g/mL), and group 6 (G6, HPV, 0.013 g/mL). Throughout the assay, humane endpoints, body weight, food, and water consumption were recorded weekly. The internal organs and skin of the mice were collected for analysis after they were sacrificed. Toxicological parameters that were studied included hematological and biochemical blood markers, splenic and hepatic histology, and hepatic oxidative stress. Results: A. citrodora extract seems to reduce the incidence of dysplastic and in situ carcinoma skin lesions induced by HPV16 in this model, suggesting that dietary supplementation with concentrations of 0.008 g/mL and 0.013 g/mL may have beneficial chemopreventive effects. Conclusions: The extract did not induce any concentration-dependent toxicological effects on any of the parameters included in the study, indicating a favorable toxicological profile under these experimental conditions. © 2024 Open Exploration Publishing Inc. All rights reserved.

Abstract
Background and Aim: Papillomaviruses (PVs) infections have been documented in numerous animal species across different regions worldwide. They often exert significant impacts on animal health and livestock production. Scientists have studied natural products for over half a century due to their diverse chemical composition, acknowledging their value in fighting cancer. Acorns (Quercus ilex) are believed to have several unexplored pharmacological properties. This study aimed to evaluate the in vivo safety and cancer chemopreventive activity of an infusion extract of Q. ilex in a transgenic mouse model of human PV (HPV)-16, which developed squamous cell carcinomas through a multistep process driven by HPV16 oncogenes. Materials and Methods: Q. ilex extract was prepared by heating in water at 90 degrees C and then characterized by mass spectrometry. Phenolic compounds from this extract were administered in drinking water to female mice in three different concentrations (0.03, 0.06, and 0.09 g/mL) over a period of 28 consecutive days. Six groups (n = 6) were formed for this study: group 1 (G1, wildtype [WT], water), group 2 (G2, HPV, water), group 3 (G3, WT, 0.09 g/mL), group 4 (G4, HPV, 0.03 g/mL), group 5 (G5, HPV, 0.06 g/ mL), and group 6 (G6, HPV, 0.09 g/mL). Throughout the experiment, humane endpoints, body weight, food intake, and water consumption were recorded weekly. Following the experimental period, all mice were sacrificed, and blood, internal organs, and skin samples were collected. Blood was used to measure glucose and microhematocrit and later biochemical parameters, such as creatinine, urea, albumin, alanine aminotransferase, and total proteins. Histological analysis was performed on skin and organ samples. Results: The administration of Q. ilex extract resulted in a statistically significant increase in relative organ weight among HPV transgenic animals, indicating adaptive biological response to the tested concentrations. Moreover, a reduction in characteristic skin lesions was observed in animals treated with the 0.06 and 0.09 g/mL extract. Conclusion: These results provide a favorable chemopreventive profile for Q. ilex extract at concentrations of 0.06 and 0.09 g/mL. This study highlights the potential of Q. ilex extract as a safe and effective therapeutic strategy against HPV16associated lesions in transgenic mouse models. The limitation of our study was the durability of transgenic animals. As a more sensitive species, we must always be careful with the durability of the test. We intend to study concentrations of 0.06 and 0.09 g/mL for longer to further investigate their possible effects.