Prevalence and Antimicrobial Resistance of Campylobacter jejuni and Campylobacter coli in Wild Birds from a Wildlife Rescue Centre.

Climate change, excessive exploitation of agricultural land which reduces natural habitats, wildlife shooting, and the use of pesticides all cause difficulties for wildlife, with considerable numbers of animals being brought to wildlife rescue centres. Although the efforts of staff involved in wildlife management at these centres usually focus on therapeutic treatments to reintroduce them into the wild, the monitoring of pathogens that may be transmitted to humans is of relevance. Campylobacter (C.) jejuni and C. coli are frequently carried by animals without inducing clinical signs and are responsible for enteric disorders and more rarely extra-intestinal disease in humans. Farm species and poultry, in particular, are the main reservoirs of C. jejuni and C. coli, but wild animals may also be carriers. The aim of this paper was to investigate the presence of C. jejuni and C. coli in wild birds housed at a wildlife rescue centre and to evaluate the sensitivity of the detected strains to antibiotics. Campylobacter was found in 52 out of 209 (24.88%) birds from 33 different species. C. jejuni was more prevalent, while C. coli was only detected in three Long-eared Owls (Asio otus). The incidence of the infection was particularly high (72.22%) among omnivorous species. Infection rates were higher in birds housed indoors (57.14%) than outdoors (31.74%). Moreover, Campylobacter was not detected in species whose mean temperature body is below 40 °C or higher than 42.2 °C. The most common antibiotic resistance in the tested strains was against trimethoprim/sulfamethoxazole, ciprofloxacin and enrofloxacin. In addition, multi-drug resistance was also found. The results highlight the need to increase biosecurity measures at rescue centres so as to reduce health-related risks to workers involved in wildlife management.

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.