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Feline urine provides valuable insights into an animal's well-being. However, professional veterinary urine analysis can be invasive, costly, and infrequent. Electrochemical biosensors, widely used in medical diagnosis, environmental monitoring, food quality control, and drug discovery, offer a promising solution for sensing analytes in feline urine. This paper introduces the "Feline Biofluids IoT Hub" concept that aims at making previously inaccessible biological data in pets' fluids visible and integrates biofluid sensing with an Internet of Things (IoT) system to enhace comprehensive animal health monitoring. To implement that concept, our project GluCAT includes a biosensing litter box and an activity sensing mat to facilitate the care of diabetic cats. Chronoamperometic data is capture from the electrochemical biosensor using a potentiostat and send to a database via Wi-Fi, providing data visualization through a mobile application. We present electrochemical biosensor tests across five glucose levels. We compare results from feline urine samples with laboratory-grade tests. Furthermore, we share insights from a real-world user study involving a cat interacting with GluCAT for over 50 hours. We envision our project enabling the monitoring of various illnesses by detecting analytes like pH, sodium, and glucose in feline urine using electrochemical biosensors, complemented by data from pet-oriented IoT devices measuring water intake, activity, weight, and food consumption. 

Feline urine provides valuable information on an animal’s wellbeing, but professional veterinary collection and analysis of urine samples can be intrusive, costly, and infrequent. Electrochemical biosensors recognize biological elements such as pH, glucose and sodium, and have numerous applications, including in medical diagnosis, environmental monitoring, food quality control and drug discovery. This paper presents cirCAT: PURRtentio, a litter box system that uses a electrochemical biosensor to monitor analytes in feline urine. We provide the implementation process of the system that consists of a DIY three-electrode biosensor, a potentiostat, a microcontroller, a ToF sensor and a mobile application. A rinsing mechanism is also included to extend the lifespan of the sensors. The system was tested using three separate electrochemistry tests to ensure accuracy, reliability, and applicability. We prepared and compared electrochemical biosensors with different conductive materials for Do-It-Yourself (DIY) electrodes. The second test compared PURRtentio against an industry-grade potentiostat. The third test compared our system against current veterinary standards for chemical analysis using feline’s urine samples. Additionally, we conducted a case study with a cat using PURRtentio for 72 hours. Finally, with results from these research and another series of interviews we did with veterinarian experts, we provide implications and future directions of this technology. PURRtentio presents an innovative and non-invasive means to consistently monitor chemistry elements in feline urine, potentially allowing for early detection and management of cat’s health conditions. 

Traditional collection and analysis of feline urine samples for health monitoring are invasive, expensive, and infrequent. This paper introduces PURRtentio, a novel litter box system utilizing an electrochemical biosensor to monitor analytes in feline urine. The system comprises a DIY biosensor, potentiostat, microcontroller, distance sensor, and mobile application. Performance validation compared PURRtentio with an industry-grade potentiostat. PURRtentio presents an innovative and non-invasive approach for consistent monitoring of chemistry elements in feline urine, enabling early detection and management of cat’s health conditions. This technology has the potential to revolutionize feline health monitoring, providing a solution for veterinarians and pet owners. 

Leptospirosis is a life-threatening, zoonotic disease with various clinical presentations, including renal injury, hepatic injury, pancreatitis, and pulmonary hemorrhage. With prompt recognition of the disease and treatment, 90% of infected dogs have a positive outcome. Therefore, rapid, early diagnosis of leptospirosis is crucial. Testing for Leptospira-specific serum antibodies using the microscopic agglutination test (MAT) lacks sensitivity early in the disease process, and diagnosis can take >2 wk because of the need to demonstrate a rise in titer. We applied machine-learning algorithms to clinical variables from the first day of hospitalization to create machine-learning prediction models (MLMs). The models incorporated patient signalment, clinicopathologic data (CBC, serum chemistry profile, and urinalysis = blood work [BW] model), with or without a MAT titer obtained at patient intake (=BW + MAT model). The models were trained with data from 91 dogs with confirmed leptospirosis and 322 dogs without leptospirosis. Once trained, the models were tested with a cohort of dogs not included in the model training (9 leptospirosis-positive and 44 leptospirosis-negative dogs), and performance was assessed. Both models predicted leptospirosis in the test set with 100% sensitivity (95% CI: 70.1–100%). Specificity was 90.9% (95% CI: 78.8–96.4%) and 93.2% (95% CI: 81.8–97.7%) for the BW and BW + MAT models, respectively. Our MLMs outperformed traditional acute serologic screening and can provide accurate early screening for the probable diagnosis of leptospirosis in dogs.