No abstract

Repetitive mild traumatic brain injury (r-mTBI) results in neuropathological and biochemical consequences in the human visual system. Using a recently developed mouse model of r-mTBI, with control mice receiving repetitive anesthesia alone (r-sham) we assessed the effects on the retina and optic nerve using histology, immunohistochemistry, proteomic and lipidomic analyses at 3 weeks post injury. Retina tissue was used to determine retinal ganglion cell (RGC) number, while optic nerve tissue was examined for cellularity, myelin content, protein and lipid changes. Increased cellularity and areas of demyelination were clearly detectable in optic nerves in r-mTBI, but not in r-sham. These changes were accompanied by a ~25% decrease in the total number of Brn3a-positive RGCs. Proteomic analysis of the optic nerves demonstrated various changes consistent with a negative effect of r-mTBI on major cellular processes like depolymerization of microtubules, disassembly of filaments and loss of neurons, manifested by decrease of several proteins, including neurofilaments (NEFH, NEFM, NEFL), tubulin (TUBB2A, TUBA4A), microtubule-associated proteins (MAP1A, MAP1B), collagen (COL6A1, COL6A3) and increased expression of other proteins, including heat shock proteins (HSP90B1, HSPB1), APOE and cathepsin D. Lipidomic analysis showed quantitative changes in a number of phospholipid species, including a significant increase in the total amount of lysophosphatidylcholine (LPC), including the molecular species 16:0, a known demyelinating agent. The overall amount of some ether phospholipids, like ether LPC, ether phosphatidylcholine and ether lysophosphatidylethanolamine were also increased, while the majority of individual molecular species of ester phospholipids, like phosphatidylcholine and phosphatidylethanolamine, were decreased. Results from the biochemical analysis correlate well with changes detected by histological and immunohistochemical methods and indicate the involvement of several important molecular pathways. This will allow future identification of therapeutic targets for improving the visual consequences of r-mTBI.

BackgroundPatient-centred care is an important aspect of quality health care. The learning environment may impact medical students’ adoption of patient-centred behaviours.MethodsAll medical students at a single institution received an anonymous, modified version of the Communication, Curriculum, and Culture instrument that measures patient-centredness in the training environment along three domains: role modelling, students’ experience, and support for patient-centred behaviours. We compared domain scores and individual items by class year and gender, and qualitatively analyzed responses to two additional items that asked students to describe experiences that demonstrated varying degrees of patient-centredness.ResultsYear 1 and 2 students reported greater patient-centredness than year 3 and 4 students in each domain: role modelling (p = 0.03), students’ experience (p = <0.001), and support for patient-centred behaviours (p < 0.001). Female students reported less support for patient-centred behaviours compared with male students (p = 0.03). Qualitative analysis revealed that explicit patient-centred curricula and positive role modelling fostered patient-centredness. Themes relating to low degrees of patient-centredness included negative role modelling and students being discouraged from being patient-centred.ConclusionsStudents’ perceptions of the patient-centredness of the learning environment decreased as students progressed through medical school, despite increasing exposure to patients. Qualitative analysis found that explicit patient-centred curricula cultivated patient-centred attitudes. Role modelling impacted student perceptions of patient-centredness within the learning environment.

In areas with higher poverty rates, access to providers drives the prescribing rate. However, in wealthier areas, where access is less of a problem, a higher density of providers and clinics increases the prescribing rate, potentially due to competition.

A predictive model (BATS) can determine the risk of a bounce back to the ICU after transfer to the floor. We speculate that determination of a patient's BATS upon ICU transfer would allow targeted floor care and decrease bounce back rates, along with postoperative morbidity, mortality, and cost of care.