Results 191 to 200 of about 4,234,681 (285)
Correction: Cocco et al. Take a Look Towards the Stress Response of Working Dogs: Cortisol and Lactate Trend Mismatches During Training. <i>Animals</i> 2025, <i>15</i>, 3175. [PDF]
Cocco R +8 more
europepmc +1 more source
Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs. [PDF]
Figueirinhas P +6 more
europepmc +1 more source
Objective To evaluate whether extending the American College of Rheumatology–recommended monitoring interval for complete blood count and liver function tests in patients receiving methotrexate (MTX) affects timely detection of medication‐related toxicity.
Spencer Simko +4 more
wiley +1 more source
A Randomized, Blinded, Placebo-Controlled Crossover Study of the Pharmacokinetics and Pharmacodynamics of Naloxone, Naltrexone, and Nalmefene in Methadone-Sedated Working Dogs. [PDF]
Mills T +8 more
europepmc +1 more source
Objective Systemic lupus erythematosus (SLE) significantly impacts employment capacity. This study aimed to investigate the impact of burden of disease activity, damage, and treatment on employment outcomes and transitions in patients with SLE. Methods Using data from a single center, we analyzed employment transitions, adjusted mean disease activity ...
Javier Mencia‐Ledo +4 more
wiley +1 more source
Objective We describe the frequency, risk factors, severity, and management of actionable and serious adverse events (AAE and SAE) in children with newly diagnosed Juvenile Idiopathic Arthritis (JIA) in Canada. Methods We enrolled patients within 3 months of JIA diagnosis in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry,
Bashayer Alnuaimi +10 more
wiley +1 more source
Development of a modified C-BARQ for evaluating behavior in working dogs. [PDF]
Hare E +4 more
europepmc +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Deep learning models for interpretation of point of care ultrasound in military working dogs. [PDF]
Hernandez Torres SI +4 more
europepmc +1 more source

