Correction to: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (Genetics in Medicine, (2020), 22, 8, (1391-1400), 10.1038/s41436-020-0812-7)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Mapping alterations to the endogenous elemental distribution within the lateral ventricles and choroid plexus in brain disorders using X-ray fluorescence imaging

The choroid plexus and cerebral ventricles are critical structures for the production of cerebral spinal fluid (CSF) and play an important role in regulating ion and metal transport in the brain, however many aspects of its roles in normal physiology and disease states, such as psychiatric illness, remain unknown. The choroid plexus is difficult to examine in vivo, and in situ ex vivo, and as such has typically been examined indirectly with radiolabeled tracers or ex vivo stains, making measurements of the endogenous K+, Cl-, and Ca+ distributions unreliable. In the present study, we directly examined the distribution of endogenous ions and biologically relevant transition metals in the choroid plexus and regions surrounding the ventricles (ventricle wall, cortex, corpus callosum, striatum) using X-ray fluorescence imaging (XFI). We find that the choroid plexus was rich in Cl- and Fe while K+ levels increase further from the ventricle as Cl- levels decrease, consistent with the known role of ion transporters in the choroid plexus CSF production. A polyI:C offspring displayed enlarged ventricles, elevated Cl- surrounding the ventricles, and intraventricular calcifications. These observations fit with clinical findings in patients with schizophrenia and suggest maternal treatment with polyI:C may lead to dysfunctional ion regulation in offspring. This study demonstrates the power of XFI for examining the endogenous elemental distributions of the ventricular system in healthy brain tissue as well as disease models

Patient-Reported Outcomes in Cancer Patients with HIV

Elevated cancer-specific mortality in PWH has been demonstrated for non-AIDS-defining malignancies. However, additional clinical endpoints of interest, including patient-reported outcomes (PROs), have not been systematically examined in PWH and cancer. We evaluated differences in patient-reported symptomology between cancer patients with versus without HIV using data from 12,529 patients at the Moffitt Cancer Center, including 55 with HIV. The symptoms were assessed using the Edmonton Symptom Assessment Scale (ESAS), which asks patients to rank 12 symptoms on a scale of 1–10, with scores ≥7 considered severe. The responses across all questions were summed to create a composite score. Vital status through t July 2021 was determined through linkage to the electronic health record. PWH reported a higher composite ESAS score on average (44.4) compared to HIV-uninfected cancer patients (30.7, p-value < 0.01). In zero-inflated negative binomial regression models adjusted for cancer site, sex, and race, the composite ESAS scores and the count of severe symptoms were 1.41 times (95% CI: 1.13–1.77) and 1.45 times (95% CI: 1.09–1.93) higher, respectively, in cancer patients with HIV. Among PWH, higher ESAS scores were associated with mortality (p-value = 0.02). This is the first demonstration of uniquely poor PROs in PWH and cancer and suggests that patient symptom monitoring to improve clinical endpoints deserves further study

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

PURPOSE: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. METHODS: We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. RESULTS: Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. CONCLUSION: Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase