Diabetes mellitus in companion animals is a complex metabolic disorder increasingly associated with alterations in the intestinal microbiome and the modulatory effects of parasitic infections. Current evidence highlights the bidirectional interactions between gut microbiota, immune responses, and metabolic regulation, all of which influence disease progression and therapeutic outcomes. Dysbiosis contributes to insulin resistance and chronic inflammation, while certain parasitic infections particularly helminths may exert protective immunomodulatory effects. Understanding these interconnected mechanisms opens new perspectives for innovative and personalized therapeutic strategies, including microbiome-targeted and immunomodulatory interventions.

Diabetes mellitus in companion animals represents a complex endocrine disorder with increasing prevalence, particularly in dogs and cats, requiring individualized and species-specific therapeutic approaches. This systematic review was conducted in accordance with the PRISMA 2020 Statement and compares two major therapeutic strategies—insulin therapy and sodium–glucose cotransporter 2 (SGLT2) inhibitors—while also analyzing factors influencing treatment selection, safety profiles, and emerging therapeutic perspectives. Insulin remains the cornerstone of diabetes management in both dogs and cats, providing reliable glycemic control across all disease stages, although its use is limited by the risk of hypoglycemia and the need for injectable administration and continuous monitoring. In contrast, SGLT2 inhibitors offer a novel oral, insulin-independent approach that improves owner compliance and demonstrates promising efficacy in selected stable feline patients, but their use is restricted by the risk of euglycemic ketoacidosis and limited applicability in insulin-dependent diabetes. Therapeutic decision-making is influenced by multiple factors, including species differences, disease pathophysiology, comorbidities, hepatic function, clinical experience, and owner-related constraints. Emerging therapies such as stem cell therapy, gene therapy, GLP-1 analogs, and advanced insulin delivery systems highlight the evolving landscape of veterinary diabetology and the potential for more personalized and regenerative treatment strategies in the future.

Diabetes mellitus is a common endocrine disorder in cats and is frequently associated with obesity, insulin resistance, and multiple metabolic complications, making long-term therapeutic management challenging. This case report describes the off-label use of dapagliflozin (Forxiga), a sodium–glucose cotransporter 2 (SGLT2) inhibitor, as part of the multimodal management of a geriatric obese diabetic cat during a two-month follow-up period. Clinical evaluation included physical examination, hematological and biochemical analyses, urinalysis, urinary sediment examination, and serial monitoring of metabolic and renal parameters. Following the introduction of dapagliflozin, the patient showed clinical stabilization, improved hydration status, reduction of azotemia, partial improvement of hepatic biochemical abnormalities, and favorable evolution of renal parameters, without clinically significant adverse effects during the observation period. Glycemic monitoring demonstrated variable glucose concentrations, highlighting the need for careful interpretation within the overall clinical context. In this individual patient, these findings suggest that off-label dapagliflozin therapy may represent a feasible adjunctive therapeutic option in carefully selected feline diabetic patients when combined with appropriate clinical monitoring. However, because this report describes a single clinical case, the observations cannot be generalized, and further prospective controlled studies are required to establish the efficacy, safety, and appropriate clinical indications of SGLT2 inhibitors in feline diabetes mellitus.

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient.