Survey of Medical Student Empathy at a Canadian Medical School: A Cross-Sectional Quantitative Survey.

Introduction: Within a medical context, empathy is defined as "an appropriate understanding and communication of a patient's experience." While it has been established that empathy is an important quality to have as a doctor, studies have shown that empathy in medical students declines during their clinical years. However, there are no studies to date that evaluate medical student empathy in Canada. Therefore, we aimed to evaluate medical student empathy at McGill University Medical School using the Jefferson Scale of Empathy (JSE). Methods: We used a cross-sectional study design and invited medical students across all 4 years, in October 2019, to complete the JSE. The JSE is a validated psychometric tool that measures empathy at one point in time. The survey was distributed via email and on social media. Results: A total of 133 students from all 4 years responded, proportionate across each year; 119 responses were included in analysis. Differences in mean questionnaire were not statistically significant for gender, age or specialty interest. The analysis of variance for differences in year of medical school was significant (P=.0104). Between groups analysis revealed a statistically significant decrease between Med-2 empathy scores (average score 117.6) and Med-3 (107.5), P<.01. Multivariable analysis demonstrated the decrease in empathy remained statistically significant (P<.05). Discussion: Our statistical analysis determined that medical students' empathy declines between the second and third year of medical school in a Canadian context, consistent with global results. This information can help target changes in the medical curriculum to preserve empathy in students, and prevent this decline, which could then be applied to other medical schools internationally.

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

Resting-state functional connectivity in individuals with bipolar disorder during clinical remission: a systematic review.

BACKGROUND: Bipolar disorder is chronic and debilitating. Studies investigating resting-state functional connectivity in individuals with bipolar disorder may help to inform neurobiological models of illness. METHODS: We conducted a systematic review with the following goals: to summarize the literature on resting-state functional connectivity in bipolar disorder during clinical remission (euthymia) compared with healthy controls; to critically appraise the literature and research gaps; and to propose directions for future research. We searched PubMed/MEDLINE, Embase, PsycINFO, CINAHL and grey literature up to April 2017. RESULTS: Twenty-three studies were included. The most consistent finding was the absence of differences in resting-state functional connectivity of the default mode network (DMN), frontoparietal network (FPN) and salience network (SN) between people with bipolar disorder and controls, using independent component analysis. However, 2 studies in people with bipolar disorder who were positive for psychosis history reported DMN hypoconnectivity. Studies using seed-based analysis largely reported aberrant resting-state functional connectivity with the amygdala, ventrolateral prefrontal cortex, cingulate cortex and medial prefrontal cortex in people with bipolar disorder compared with controls. Few studies used regional homogeneity or amplitude of low-frequency fluctuations. LIMITATIONS: We found heterogeneity in the analysis methods used. CONCLUSION: Stability of the DMN, FPN and SN may reflect a state of remission. Further, DMN hypoconnectivity may reflect a positive history of psychosis in patients with bipolar disorder compared with controls, highlighting a potentially different neural phenotype of psychosis in people with bipolar disorder. Resting-state functional connectivity changes between the amygdala, prefrontal cortex and cingulate cortex may reflect a neural correlate of subthreshold symptoms experienced in bipolar disorder euthymia, the trait-based pathophysiology of bipolar disorder and/or a compensatory mechanism to maintain a state of euthymia.