The aim of this paper is to analyse sunitinib malate in vitro ability to enhance cisplatin cytotoxicity in T24, 5637, and HT1376 human urinary bladder-cancer cell lines. Cells were treated with cisplatin (3, 6, 13, and 18 μM) and sunitinib malate (1, 2, 4, 6, and 20 μM), either in isolation or combined, over the course of 72 hours. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, acridine orange, and monodansylcadaverine staining and flow cytometry were performed. The combination index (CI) was calculated based on the Chou and Talalay method. In isolation, cisplatin and sunitinib malate statistically (P < 0.05) decrease cell viability in all cell lines in a dose-dependent manner, with the presence of autophagic vacuoles. A cell cycle arrest in early S-phase and in G0/G1-phase was also found after exposure to cisplatin and sunitinib malate, in isolation, respectively. Treatment of urinary bladder-cancer cells with a combination of cisplatin and sunitinib malate showed a synergistic effect (CI < 1). Autophagy and apoptosis studies showed a greater incidence when the combined treatment was put into use. This hints at the possibility of a new combined therapeutic approach. If confirmed in vivo, this conjugation may provide a means of new perspectives in muscle-invasive urinary bladder cancer treatment.

No abstract

Elbow joint incongruity is recognized as an important factor in the development, treatment, and prognosis of canine elbow dysplasia. Elbow incongruity has been measured based on radiographic joint space widths, however these values can be affected by the degree of elbow joint flexion. Recent studies have reported radiographic curvature radii as more precise measures of humeroulnar congruity in dogs. The aim of this prospective observational study was to describe radiographic curvature radii measured from flexed and extended elbow radiographs for a sample of dogs representing a medium breed (Portuguese Pointing Dog) and a large breed (Estrela Mountain Dog). The curvature radii from the ulnar trochlear notch and humeral trochlea were measured in 114 mediolateral elbow extended radiographic views (30 Portuguese Pointing Dog and 27 Estrela Mountain Dog), and 84 mediolateral flexed views (22 Portuguese Pointing Dog and 20 Estrela Mountain Dog). The sampled animals' ages ranged from 12 to 84 months (34.6 ± 17.8 months). Good agreement was observed between curvature radii measurements for flexed vs. extended views in both breed groups. Ulnar trochlear notch curvature radii measurements were greater than humeral trochlea curvature radii measurements in both breed groups. Both curvature radii were greater in the large-breed dog group vs. the medium-breed dog group. Both breed groups had ulnar and humeral curves with similar typology. However, the large breed group had greater intermediate differences between the humeroulnar surface curvature radii. Results from this study supported the use of curvature radii as measures of humeroulnar congruity in mediolateral flexed elbow radiographs of medium and large breed dogs.

Accurate radiographic screening evaluation is essential in the genetic control of canine HD, however, the qualitative assessment of hip congruency introduces some subjectivity, leading to excessive variability in scoring. The main objective of this work was to validate a method-Hip Congruency Index (HCI)-capable of objectively measuring the relationship between the acetabulum and the femoral head and associating it with the level of congruency proposed by the Fédération Cynologique Internationale (FCI), with the aim of incorporating it into a computer vision model that classifies HD autonomously. A total of 200 dogs (400 hips) were randomly selected for the study. All radiographs were scored in five categories by an experienced examiner according to FCI criteria. Two examiners performed HCI measurements on 25 hip radiographs to study intra- and inter-examiner reliability and agreement. Additionally, each examiner measured HCI on their half of the study sample (100 dogs), and the results were compared between FCI categories. The paired t-test and the intraclass correlation coefficient (ICC) showed no evidence of a systematic bias, and there was excellent reliability between the measurements of the two examiners and examiners’ sessions. Hips that were assigned an FCI grade of A (n = 120), B (n = 157), C (n = 68), D (n = 38) and E (n = 17) had a mean HCI of 0.739 ± 0.044, 0.666 ± 0.052, 0.605 ± 0.055, 0.494 ± 0.070 and 0.374 ± 0.122, respectively (ANOVA, p < 0.01). Therefore, these results show that HCI is a parameter capable of estimating hip congruency and has the potential to enrich conventional HD scoring criteria if incorporated into an artificial intelligence algorithm competent in diagnosing HD.

Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.