Point-of-care platform integrated with deep-learning, convolutional neural network algorithms effectively evaluates canine and feline peripheral blood smears.

Objective: To perform a diagnostic assessment of a point-of-care veterinary multiuse platform integrated with a model comprised of deep-learning, convolutional neural network algorithms for evaluating canine/feline peripheral blood smears compared to board-certified clinical pathologists (CPs).

Methods: This study had a blinded, randomized, incomplete block design, and results were compared between CPs and algorithms. Blood smears from convenience samples from veterinary diagnostic reference laboratories from October to December 2021 were used.

Two-Drug Combination Antiretroviral Therapy for HIV-1: Case Series and Literature Review.

Doravirine (DOR) is a non-nucleoside reverse transcriptase inhibitor (NNRTI) approved for use in combination antiretroviral therapy (cART) for treatment of human immunodeficiency virus (HIV) in treatment-naive patients. Doravirine-based regimens are an option for patients with limited alternatives due to drug-drug interactions, toxicities, or resistance. A paucity of data exists for use of two-drug DOR-based regimens in treatment-experienced individuals.

Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Background: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm.

Phosphorus lability across diverse agricultural contexts with legacy sources.

The buffering of phosphorus (P) in the landscape delays management outcomes for water quality. If stored in labile form (readily exchangeable and bioavailable), P may readily pollute waters. We studied labile P and its intensity for >600 soils and sediments across seven study locations in the United States.