Atopic dermatitis is one of the most common skin diseases in dogs. Pathogenesis is complex and incompletely understood. Skin colonizing bacteria likely play an important role in the severity of this disease. Studying the canine skin microbiota using traditional microbiological methods has many limitations which can be overcome by molecular procedures. The aim of this study was to describe the bacterial microbiota of the skin and ear canals of healthy non-allergic and allergic German shepherd dogs (GSDs) without acute flare or concurrent skin infection and to compare both. Bacterial 16S rRNA gene amplicon sequence data revealed no differences of bacterial community patterns between the different body sites (axilla, front dorsal interdigital skin, groin, and ear canals) in non-allergic dogs. The microbiota at the different body sites of non-allergic GSDs showed no significant differences. Only for the samples obtained from the axilla the bacterial microbiota of allergic dogs was characterized by a lower species richness compared to that of non-allergic dogs and the bacterial community composition of the skin and ear canals of allergic dogs showed body site specific differences compared to non-allergic dogs. Actinobacteria was the most abundant phylum identified from the non-allergic dogs and Proteobacteria from allergic dogs. Macrococcus spp. were more abundant on non-allergic skin while Sphingomonas spp. were more abundant on the allergic skin. Forward step redundancy analysis of metadata indicated that the household the dogs came from had the strongest impact on the composition of the skin microbiome followed by sex, host health status and body site.

Background: Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology.Animals: Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used.Objectives: We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia.Materials and Methods: The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed.Results: A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS.Conclusions and Clinical Relevance: This novel object detection model has the potential for application in the field of veterinary dermatology.

We investigated four cats with similar clinical skin-related signs strongly suggestive of Ehlers-Danlos syndrome (EDS). Cases no. 1 and 4 were unrelated and the remaining two cases, no. 2 and 3, were reportedly siblings. Histopathological changes were characterized by severely altered dermal collagen fibers. Transmission electron microscopy in one case demonstrated abnormalities in the collagen fibril organization and structure. The genomes of the two unrelated affected cats and one of the affected siblings were sequenced and individually compared to 54 feline control genomes. We searched for private protein changing variants in known human EDS candidate genes and identified three independent heterozygous COL5A1 variants. COL5A1 is a well-characterized candidate gene for classical EDS. It encodes the proα1 chain of type V collagen, which is needed for correct collagen fibril formation and the integrity of the skin. The identified variants in COL5A1 are c.112_118+15del or r.spl?, c.3514A>T or p.(Lys1172*), and c.3066del or p.(Gly1023Valfs*50) for cases no. 1, 2&3, and 4, respectively. They presumably all lead to nonsense-mediated mRNA decay, which results in haploinsufficiency of COL5A1 and causes the alterations of the connective tissue. The whole genome sequencing approach used in this study enables a refinement of the diagnosis for the affected cats as classical EDS. It further illustrates the potential of such experiments as a precision medicine approach in animals with inherited diseases.

Canine leishmaniasis is an emerging infection with increasing importance because the main vector also exists in Germany and autochthonous infections have been described. In this article we present the latest information available regarding therapy and prevention of this disease. Allopurinol-resistant Leishmania infantum strains were isolated from dogs which experienced recurrent disease while being treated with allopurinol, highlighting the need for alternative treatment options. Differing results of long-term studies on the efficacy of miltefosine in combination with allopurinol in comparison to meglumine antimoniate and allopurinol can affect the selection of the treatment protocol. A high number of repellents with distinctive characteristics is available for the prevention of infection in dogs. In Europe, two vaccines are licensed for dogs which aim at reducing the risk of an active infection and the severity clinical disease.

A 15‐year‐old male golden retriever who had been receiving prednisolone for orthopaedic problems for the past 2 years was presented for multifocal symmetrical alopecia, skin nodules and plaques on the dorsal aspect of the body. Cytology was compatible with calcinosis cutis due to suspected iatrogenic hypercortisolism, with secondary pyoderma. The Phovia system, a device that emits fluorescent light energy (FLE), was used as an adjunct to systemic antimicrobial and topical (spray and shampoo) therapies. Part of the lesions were covered with a towel and not exposed to Phovia, to evaluate efficacy. Cytology confirmed accelerated improvement of the Phovia‐illuminated lesions compared with unexposed lesions. To the authors’ knowledge, this is the first report of FLE being used to manage bacterial skin infection associated with canine calcinosis cutis. It highlights the importance of this type of topical management against bacterial skin infections.

Background: Early and accurate diagnosis of otitis externa is crucial for correct management yet can often be challenging. Artificial intelligence (AI) is a valuable diagnostic tool in human medicine. Currently, no such tool is available in veterinary dermatology/otology.Objectives: As a proof‐of‐concept, we developed and evaluated a novel YOLOv5 object detection model for identifying healthy ear canals, otitis or masses in the canine ear canal.Animals: Digital images of ear canals from dogs with healthy ears, otitis and masses in the ear canal were used.Materials and Methods: Four variants of the YOLOv5 model were trained, each using a different training dataset. The prediction performance metrics used to evaluate them include F1/confidence‐curves, mean average precision (mAP50), precision (P), recall (R) and average precision (AP) for accuracy. These are quantifiable performance metrics used to evaluate the efficacy of each variant.Results: All four variants were capable of detecting and classifying the ear canal. However, training datasets with many duplicates (A and C) inflated performance metrics as a consequence of data leakage, potentially compromising their effectiveness on unseen images. Additionally, larger datasets (without duplicates) demonstrated superior performance metrics compared to model variants trained on smaller datasets (without duplicates).Conclusions and Clinical Relevance: This novel AI object detection model has the potential for application in the field of veterinary dermatology. An external validation study is needed prior to clinical deployment.