Optimizing a mentorship program from the perspective of academic medicine leadership - a qualitative study.

BACKGROUND: Effective mentorship is an important contributor to academic success. Given the critical role of leadership in fostering mentorship, this study sought to explore the perspectives of departmental leadership regarding 1) current departmental mentorship processes; and 2) crucial components of a mentorship program that would enhance the effectiveness of mentorship. METHODS: Department Division Directors (DDDs), Vice-Chairs, and Mentorship Facilitators from the Department of Medicine at the University of Toronto Temerty Faculty of Medicine were interviewed between April and December 2021 using a semi-structured guide. Interviews were audio-recorded and transcribed verbatim, then coded. Analysis occurred in 2 steps: 1) codes were organized to identify emergent themes; then 2) the Social Ecological Model (SEM) was applied to interpret the findings. RESULTS: Nineteen interviews (14 DDDs, 3 Vice-Chairs, and 2 Mentorship Facilitator) were completed. Analysis revealed three themes: (1) a culture of mentorship permeated the department as evidenced by rigorous mentorship processes, divisional mentorship innovations, and faculty that were keen to mentor; (2) barriers to the establishment of effective mentoring relationships existed at 3 levels: departmental, interpersonal (mentee-mentor relationships), and mentee; and (3) strengthening the culture of mentorship could entail scaling up pre-existing mentorship processes and promoting faculty engagement. Application of SEM highlighted critical program features and determined that two components of interventions (creating tools to measure mentorship outcomes and systems for mentor recognition) were potential enablers of success. CONCLUSIONS: Establishing 'mentorship outcome measures' can incentivize and maintain relationships. By tangibly delineating departmental expectations for mentorship and creating systems that recognize mentors, these measures can contribute to a culture of mentorship.

Tracing brain genotoxic stress in Parkinson's disease with a novel single-cell genetic sensor.

To develop an in vivo tool to probe brain genotoxic stress, we designed a viral proxy as a single-cell genetic sensor termed PRISM that harnesses the instability of recombinant adeno-associated virus genome processing and a hypermutable repeat sequence-dependent reporter. PRISM exploits the virus-host interaction to probe persistent neuronal DNA damage and overactive DNA damage response. A Parkinson's disease (PD)-associated environmental toxicant, paraquat (PQ), inflicted neuronal genotoxic stress sensitively detected by PRISM. The most affected cell type in PD, dopaminergic (DA) neurons in substantia nigra, was distinguished by a high level of genotoxic stress following PQ exposure. Human alpha-synuclein proteotoxicity and propagation also triggered genotoxic stress in nigral DA neurons in a transgenic mouse model. Genotoxic stress is a prominent feature in PD patient brains. Our results reveal that PD-associated etiological factors precipitated brain genotoxic stress and detail a useful tool for probing the pathogenic significance in aging and neurodegenerative disorders.

An automated platform for in situ serial crystallography at room temperature.

Direct observation of functional motions in protein structures is highly desirable for understanding how these nanomachineries of life operate at the molecular level. Because cryogenic temperatures are non-physiological and may prohibit or even alter protein structural dynamics, it is necessary to develop robust X-ray diffraction methods that enable routine data collection at room temperature. We recently reported a crystal-on-crystal device to facilitate in situ diffraction of protein crystals at room temperature devoid of any sample manipulation. Here an automated serial crystallography platform based on this crystal-on-crystal technology is presented. A hardware and software prototype has been implemented, and protocols have been established that allow users to image, recognize and rank hundreds to thousands of protein crystals grown on a chip in optical scanning mode prior to serial introduction of these crystals to an X-ray beam in a programmable and high-throughput manner. This platform has been tested extensively using fragile protein crystals. We demonstrate that with affordable sample consumption, this in situ serial crystallography technology could give rise to room-temperature protein structures of higher resolution and superior map quality for those protein crystals that encounter difficulties during freezing. This serial data collection platform is compatible with both monochromatic oscillation and Laue methods for X-ray diffraction and presents a widely applicable approach for static and dynamic crystallographic studies at room temperature.

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.