Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists

CRISPR: On the road to restoring sight to the blind

CRISPR-Cas9 is a novel gene-editing tool that promised to revolutionize our ability to treat genetic conditions when first introduced. Today, it continues to fuel many areas of health research, ranging from cancers to sickle cell disease to Huntington’s disease.1–3 Vision science researchers immediately saw the potential of CRISPR, with some of the earliest experiments exploring CRISPR as a treatment option for inherited ocular disorders.4 Only a few years later, in March 2020, vision research was again at the forefront of this field.5 A CRISPR therapy was injected into the human body for the first time in an attempt to correct a vision threatening mutation.5 Inherited retinal diseases have traditionally presented therapeutic challenges, but CRISPR is now providing hope for a cure

Challenges in transition to practice

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Role of Spy1 in Mammary Development

Breast cancer accounts for 25% of all new cancer cases in women and 13% of all cancer deaths in women. Determining the key mediators that regulate aspects of both normal and abnormal development of the breast is crucial to the development of better diagnostics and treatment options. Proper cell cycle regulation guides cellular changes during the stages of mammary development, and misregulation or mutation/deletion of key cell regulatory genes represents an important step in breast cancer initiation and progression. The cyclin-like protein Spy1 is tightly regulated during normal mammary gland development and has been implicated in several cancers, including breast cancer. Spy1 binds and activates cyclin-dependent kinases (Cdks), promoting progression through the G1/S and G2/M phase of the cell cycle. Elevated levels of Spy1 significantly increases cell proliferation and has been shown to override the DNA damage response. This study seeks to explore the question - Is Spy1 required for normal and abnormal development of the breast? My thesis work involved using the novel genome-editing tool CRISP-Cas9 to knockout Spy1 in the mouse mammary epithelial cell line HC11 and the breast cancer cell line, MDA-MB 231. These knockout cells were tested for effects on mammary cell growth and development such as proliferation, differentiation, stem cell expansion and migration. My results support that this unique family of cell cycle regulators play a critical role in the differentiation and stem cell maintenance in the mammary gland. This work sheds light on the mechanisms of normal mammary development as well as breast cancer initiation and progression and supports further exploration of this mechanism as a therapeutic direction for breast cancer

Assessing the Performance of a Novel Bayesian Algorithm at Point of Care for Red Eye Complaints

The current diagnostic aids for red eye are static flowcharts that do not provide dynamic, stepwise workups. The diagnostic accuracy of a novel dynamic Bayesian algorithm for red eye was tested. Fifty-seven patients with red eye were evaluated by an emergency medicine physician who completed a questionnaire about symptoms/findings (without requiring extensive slit lamp findings). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm used questionnaire data to suggest a differential diagnosis. The referrer’s diagnostic accuracy was 70.2%, while the algorithm’s accuracy was 68.4%, increasing to 75.4% with the algorithm’s top two diagnoses included and 80.7% with the top three included. In urgent cases of red eye (n = 26), the referrer diagnostic accuracy was 76.9%, while the algorithm’s top diagnosis was 73.1% accurate, increasing to 84.6% (top two included) and 88.5% (top three included). The algorithm’s sensitivity for urgent cases was 76.9% (95% CI: 56–91%) using its top diagnosis, with a specificity of 93.6% (95% CI: 79–99%). This novel algorithm provides dynamic workups using clinical symptoms, and may be used as an adjunct to clinical judgement for triaging the urgency of ocular causes of red eye