藤原 亜紀

BACKGROUND: Although feline nasal and nasopharyngeal diseases (NNDs) often require advanced tests under general anaesthesia for definitive diagnosis, not all patients can undergo them. OBJECTIVES: This study aimed to construct diagnostic prediction models for feline NNDs in Japan using noninvasive examinations, signalment and history. METHODS: Seventy-nine cats diagnosed with NNDs, including representative diseases in Japan-nasal and nasopharyngeal tumours (NNT), rhinitis (RS) and nasopharyngeal stenosis (NPS)-were retrospectively investigated to construct prediction models (model group, GM). Thirty-nine cats diagnosed were prospectively investigated to validate their efficacy (validation group, GV). Three predictive models were developed: Models 1 and 2 were manually constructed, with Model 1 designed to predict NNT, RS and NPS individually and Model 2 distinguishing between these diseases. Model 3 was constructed using least absolute shrinkage and selection operator logistic regression. Sensitivity, indicating the ability to identify cases of each disease, and specificity, reflecting the ability to exclude other diseases, were used to assess performance. RESULTS: In Model 1 of the GV, the sensitivity and specificity for NNT, RS and NPS were 1.00 and 0.73, 0.62 and 0.96 and 0.78 and 0.97, respectively. In Model 2 of the GV, the values were 0.94 and 0.86 for NNT, 0.77 and 0.92 for RS and 0.75 and 0.94 for NPS. In Model 3 of the GV, they were 0.94 and 0.05 for NNT, 0.25 and 1.00 for RS and 0.13 and 0.84 for NPS. CONCLUSIONS: The diagnostic prediction models, particularly Models 1 and 2, could help estimate whether advanced tests are necessary.

In the veterinary field, the utility of disease-identification models that use comprehensive circulating microRNA (miRNA) profiles produced through measurements based on next-generation sequencing (NGS) remains unproven. To integrate NGS technology with automated machine learning (autoML) to create a comprehensive circulating miRNA profile and to assess the clinical utility of a disease-screening model derived from this profile. The study involved dogs diagnosed with or being treated for various diseases, including tumors, across multiple veterinary clinics (n = 254), and healthy dogs without apparent diseases (n = 91). miRNA was extracted from EDTA-treated plasma, and a comprehensive analysis was conducted of one million reads per sample using NGS. Then autoML technology was applied to develop a diagnostic model based on miRNA. Among these models, the one with the highest performance was chosen for evaluation. The diagnostic model, based on the comprehensive circulating miRNA profile developed in this study, achieved an AUC score of 0.89, with a sensitivity of 85 % and a specificity of 88 % for the disease samples. The miRNA-based diagnostic model demonstrated high sensitivity for disease groups and has the potential to be an effective screening test. This study indicates that a comprehensive miRNA profile in dog plasma could serve as a highly sensitive blood biomarker.

BACKGROUND: Few epidemiological studies on respiratory medicine and the relationship between clinical signs and various respiratory diseases in cats have been reported. OBJECTIVES: This retrospective study aimed to investigate the prevalence and breed predisposition to feline respiratory diseases in Japan and determine the association between clinical signs, duration and type of respiratory diseases. METHODS: The medical records of cats with feline respiratory diseases were examined to obtain information on age, sex, breed, final diagnosis, clinical signs and duration. The odds ratios (ORs) and 95% confidence intervals were calculated to evaluate breed predispositions. Mann-Whitney U, Kruskal-Wallis and Dunn's tests were used to assess the duration of clinical signs. RESULTS: This study included 540 cats with 615 respiratory diagnoses. The American Shorthair breed was predisposed to bronchopneumonia (BP; OR: 5.0) and pulmonary tumour (PT; OR: 3.6), while the Russian Blue breed exhibited a predisposition to inflammatory lower airway diseases (OR: 3.4), BP (OR: 6.1) and interstitial lung diseases (OR: 11.1). Similarly, the Scottish Fold breed displayed predisposition to PTs (OR: 5.8). The duration of clinical signs among nasal diseases, nasopharyngeal diseases and lower tracheal/bronchial and pulmonary diseases differed significantly (p = 0.001, p = 0.012, p < 0.0001, respectively). CONCLUSIONS: The results suggest that some popular breeds in Japan are predisposed to feline respiratory diseases, especially the American Shorthair, Russian Blue and Scottish Fold breeds. The characteristics of occurrence, clinical signs and duration of each disease will aid in diagnosing, treating, preventing and elucidating the pathophysiology of feline respiratory disease.

This study aimed to investigate the association of respiratory rate (RR), oxygen saturation (SpO2), and blood findings with respiratory disease in dogs and to compare the examination findings in the chronic and acute phases. Dogs that visited a veterinary referral hospital with respiratory symptoms were classified into the chronic disease group (GC), and those that visited the emergency veterinary hospital were classified into the acute disease group (GA). In total, 704 and 682 dogs were included in GC and GA, respectively. The RR and SpO2 were significantly higher and lower, respectively, in patients with lung disease compared to other disease sites in both groups. White blood cell counts were significantly increased in patients with lung and pleural diseases in both groups. Respiratory alkalosis and respiratory acidosis were most common in GC and GA, respectively. The C-reactive protein levels were elevated in both groups, primarily in patients with lung disease. Associations between the results of several tests for understanding and diagnosing respiratory conditions and diseases were recognized, and differences in the trends of the chronic and acute phases were clarified. These tools may be used as adjuncts to other tests for the diagnosis and monitoring of treatment responses.

Advanced imaging techniques under general anesthesia are frequently employed to achieve a definitive diagnosis of canine nasal diseases. However, these examinations may not be performed immediately in all cases. This study aimed to construct prediction models for canine nasal diseases using less-invasive examinations such as clinical signs and radiography. Dogs diagnosed with nasal disease between 2010 and 2020 were retrospectively investigated to construct a prediction model (Group M; GM), and dogs diagnosed between 2020 and 2021 were prospectively investigated to validate the efficacy (Group V; GV). Prediction models were created using two methods: manual (Model 1) and LASSO logistic regression analysis (Model 2). In total, 103 and 86 dogs were included in GM and GV, respectively. In Model 1, the sensitivity and specificity of neoplasia (NP) and sino-nasal aspergillosis (SNA) were 0.88 and 0.81 in GM and 0.92 and 0.78 in GV, respectively. Those of non-infectious rhinitis (NIR) and rhinitis secondary to dental disease (DD) were 0.78 and 0.88 in GM and 0.64 and 0.80 in GV, respectively. In Model 2, the sensitivity and specificity of NP and SNA were 0.93 and 1 in GM and 0.93 and 0.75 in GV, respectively. Those of NIR and DD were 0.96 and 0.89 in GM and 0.80 and 0.79 in GV, respectively. This study suggest that it is possible to create a prediction model using less-invasive examinations. Utilizing these predictive models may lead to appropriate general anesthesia examinations and treatment referrals.