Psilocybin-Assisted Therapy for Brain Cancer Related Existential Distress: A Case-Report.

Introduction: Psilocybin-assisted therapy (PAT) has gained traction in palliative care as a treatment for existential distress in the last decade. Patients with brain cancer have been excluded from studies, yet they stand to benefit as much as other patients with cancer-related psychological distress. Case description: In this report, we discuss the case of a patient with end-of-life distress secondary to stage 4 astrocytoma that received PAT through Health Canada's Special Access Program. The patient had a positive response to PAT without adverse events. Discussion: Standard treatment for existential distress is often inefficacious and PAT is rarely available, especially for patients with brain cancer. We highlight the importance of making PAT more available as many patients with unresolved existential distress resort to medical assistance in dying without ever knowing of the existence of PAT. Conclusion: PAT was effective in partially alleviating the patient's existential distress. Access to PAT needs to be expanded urgently.

Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.

Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome's composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.