Melgaardlyon3713

Faculty and trainee well-being at academic medical centers is a nationwide concern. In response, the University of Utah Health created a system-wide provider wellness program that used individual faculty champions who were empowered to 1) examine the unique needs of their department or division using a lens of quality improvement, 2) design projects to address well-being, and 3) measure impact of projects addressing well-being. One team used a feedback tool to attempt to improve the well-being of Family Medicine faculty by better understanding challenges and developing a roadmap for action.

Evaluate the effectiveness of an anonymous feedback tool on faculty well-being.

The Division of Family Medicine developed and implemented a quarterly anonymous faculty survey to facilitate an ongoing improvement process for faculty wellness in 2016. The faculty survey identified thematic concerns, which were used to develop constructive solutions and systemic changes.

A closed loop feedback structure provided rich ah, could help improve well-being in a variety of health care professions.

To evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution fundus images acquired with a smartphone and indirect ophthalmoscope lens adapter.

An automated deep learning algorithm trained on 92,364 traditional fundus camera images was tested on a dataset of smartphone fundus images from 103 eyes acquired from two previously published studies. Images were extracted from live video screenshots from fundus examinations using a commercially available lens adapter and exported as a screenshot from live video clips filmed at 1080p resolution. Each image was graded twice by a board-certified ophthalmologist and compared to the output of the algorithm, which classified each image as having RDR (moderate nonproliferative DR or worse) or no RDR.

In spite of the presence of multiple artifacts (lens glare, lens particulates/smudging, user hands over the objective lens) and low-resolution images achieved by users of various levels of medical training, the algorithm achieved a 0.89 (95% confidence interval [CI] 0.83-0.95) area under the curve with an 89% sensitivity (95% CI 81%-100%) and 83% specificity (95% CI 77%-89%) for detecting RDR on mobile phone acquired fundus photos.

The fully data-driven artificial intelligence-based grading algorithm herein can be used to screen fundus photos taken from mobile devices and identify with high reliability which cases should be referred to an ophthalmologist for further evaluation and treatment.

The implementation of this algorithm on a global basis could drastically reduce the rate of vision loss attributed to DR.

The implementation of this algorithm on a global basis could drastically reduce the rate of vision loss attributed to DR.

To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs.

We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices. read more We further assessed the clinical relevance of the model by visualizing the principal components of features at different layers and through the use of gradient-weighted class activation maps, which presented retinal regions having the most significant influence on the model.

Our model was trained, validated, and tested on 1500 fundus images (training, 1200; validation, 150; testing, 150) and achieved an average AUC of 0.98 for identifying the normal, trace (small and local lesions), and disease classes (large and spreading lesions). The AUCs of the model using an independent subset with 180 images were 1.00 (95% confidence interval [CI], 0.99-1.00), 0.97 (95% CI, 0.94-0.99), and 0.96 (95% CI, 0.90-1.00) for the normal, trace and disease classes, respectively.

The proposed deep learning model is able to identify three severity levels of EAU with high accuracy. The model also achieved high accuracy on independent validation subsets, reflecting a substantial degree of generalizability.

The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice.

The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice.

This study was designed to investigate whether COVID-19 patients with recently received immunotherapy or other anti-cancer treatments had more severe symptoms and higher mortality.

A literature search was performed using the electronic platforms to obtain relevant research studies published up to June 28, 2020. Odds ratio (OR) and 95% confidence intervals (CI) of research endpoints in each study were calculated and merged. Statistical analyses were performed with Stata 12.0 (Stata Corp LP, College Station, TX).

A total of 17 studies comprising 3581 cancer patients with COVID-19 were included in this meta-analysis. SARS-CoV-2-infected cancer patients who recently received anti-cancer treatment did not observe a higher risk of exacerbation and mortality (All

-value >0.05). We also found that surgery, targeted therapy, chemotherapy, immunotherapy, and radiotherapy were not associated with increased risk of exacerbation and mortality (All

-value >0.05). Chemotherapy within 28d increased the risk oemotherapy was not associated with increased risk of severe COVID-19. The role of anti-cancer therapy in cancer patients with COVID-19 still needs further exploration, especially chemotherapy and immunotherapy.Dysregulated expression of microRNAs (miRNAs or miRs) has been implicated in the pathophysiology of type 2 diabetes mellitus (T2DM). However, their underlying role in the complication of detrusor fibrosis remains poorly understood. Therefore, this study aimed to examine the potential functional relevance of miR-363 in detrusor fibrosis of rats with streptozotocin (STZ)-induced T2DM through the predicted target gene collagen type I alpha 2 (Col1a2). Immunohistochemical analysis found an increase in the positive expression of collagen type III alpha 1 (Col3a1) and Col1a2 in detrusor tissues, where miR-363 expression was decreased. Next, gain- and loss-of-function experiments were performed to clarify the effects of miR-363 and Col1a2 on the activities of bladder detrusor cells. Of note, binding affinity between miR-363 and Col1a2 was verified by a dual-luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay. Upregulated miR-363 inhibited Col1a2 expression, which led to increased expression of B-cell lymphoma 2 (Bcl-2) and Smad7 and accelerated cell viability, along with decreases in cell apoptosis and Col3a1, Bcl-2-associated X protein (Bax), transforming growth factor (TGF)-β1, and Smad4 expressions.