A modular, cost-effective, versatile, open-source operant box solution for long-term miniscope imaging, 3D tracking, and deep learning behavioral analysis.

In this procedure we have included an open-source method for a customized operant chamber optimized for long-term miniature microscope (miniscope) recordings. •The miniscope box is designed to function with custom or typical med-associates style accessories (e.g., houselights, levers, etc.).•The majority of parts can be directly purchased which minimizes the need for skilled and time-consuming labor.•We include designs and estimated pricing for a single box but it is recommended to build these in larger batches to efficiently utilize bulk ordering of certain components.

Opioid Overprescription and Underprescription to Patients After Otolaryngology-Head and Neck Surgery.

This cohort study examines the extent of mismatched opioid prescribing between hospitalization and discharge after otolaryngology­head and neck surgery.

Using deep learning to study emotional behavior in rodent models.

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.

Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients With Diabetes.

BACKGROUND: Artificial intelligence-based technology systems offer an alternative solution for diabetic retinopathy (DR) screening compared with standard, in-office dilated eye examinations. We performed a cost-effectiveness analysis of Automated Retinal Image Analysis System (ARIAS)-based DR screening in a primary care medicine clinic that serves a low-income patient population. METHODS: A model-based, cost-effectiveness analysis of two DR screening systems was created utilizing data from a recent study comparing adherence rates to follow-up eye care among adults ages 18 or older with a clinical diagnosis of diabetes. In the study, the patients were prescreened with an ARIAS-based, nonmydriatic (undilated), point-of-care tool in the primary care setting and were compared with patients with diabetes who were referred for dilated retinal screening without prescreening, as is the current standard of care. Using a Markov model with microsimulation resulting in a total of 600 000 simulated patient experiences, we calculated the incremental cost-utility ratio (ICUR) of the two screening approaches, with regard to five-year cost-effectiveness of DR screening and treatment of vision-threatening DR. RESULTS: At five years, ARIAS-based screening showed similar utility as the standard of care screening systems. However, ARIAS reduced costs by 23.3%, with an ICUR of $258 721.81 comparing the current practice to ARIAS. CONCLUSIONS: Primary care-based ARIAS DR screening is cost-effective when compared with standard of care screening methods.

Factors Associated with Adherence to Screening Guidelines for Diabetic Retinopathy Among Low-Income Metropolitan Patients.

In this retrospective analysis of patients with diabetes in an academic primary care clinic in St. Louis, attendance at ophthalmic screening appointments was recorded over a two-year observation window. Factors associated with adherence were analyzed by multivariable regression. Among 974 total patients included, only 330 (33.9%) were adherent within a two-year period. Multivariate analyses identified older age, female gender, primary language other than English, and attendance at ancillary diabetes clinic visits as factors associated with improved diabetic retinopathy screening adherence. Factors not associated with adherence included race and insurance status.

Integration of Health Information Systems Using HL7: A Case Study.

Interoperability is a prerequisite for health information systems (HIS) that will reduce waste of unnecessary costs, errors, delays, and futile repetition. Many previous studies had proposed different approaches in the attempt to solve interoperability challenges. In this paper, we report our experiences in using Health Level 7 (HL7) standard and adopting the Common Gateway Model for exchanging heath data. The benefits and challenges of using standards for data interoperability are also described.