Digital Storytelling as a Patient Engagement and Research Approach With First Nations Women: How the Medicine Wheel Guided Our Debwewin Journey.

When research is conducted from a Western paradigm alone, the findings and resultant policies often ignore Indigenous peoples' health practices and fail to align with their health care priorities. There is a need for decolonized approaches within qualitative health research to collaboratively identify intersecting reasons behind troubling health inequities and to integrate Indigenous knowledge into current health care services. We engaged with First Nations women to explore to what extent digital storytelling could be a feasible, acceptable, and meaningful research method to inform culturally safe health care services. This novel approach created a culturally safe and ethical space for authentic patient engagement. Our conversations were profound and provided deep insights into First Nations women's experiences with breast cancer and guidance for our future qualitative study. We found that the digital storytelling workshop facilitated a Debwewin journey, which is an ancient Anishinabe way of knowing that connects one's heart knowledge and mind knowledge.

Elevating the uses of storytelling approaches within Indigenous health research: a critical and participatory scoping review protocol involving Indigenous people and settlers.

BACKGROUND: There is a complicated and exploitative history of research with Indigenous peoples and accompanying calls to meaningfully and respectfully include Indigenous knowledge in healthcare. Storytelling approaches that privilege Indigenous voices can be a useful tool to break the hold that Western worldviews have within the research. Our collaborative team of Indigenous and non-Indigenous researchers, and Indigenous patients, Elders, healthcare providers, and administrators, will conduct a critical participatory, scoping review to identify and examine how storytelling has been used as a method in Indigenous health research. METHODS: Guided by two-eyed seeing, we will use Bassett and McGibbon's adaption of Arksey and O'Malley's scoping review methodology. Relevant articles will be identified through a systematic search of the gray literature, core Indigenous health journals, and online databases including Scopus, MEDLINE, Embase, CINAHL, AgeLine, Academic Search Complete, Bibliography of Native North Americans, Canadian Reference Centre, and PsycINFO. Qualitative and mixed-methods research articles will be included if the researchers involved Indigenous participants or their healthcare professionals living in Turtle Island (i.e., Canada and the USA), Australia, or Aotearoa (New Zealand); use storytelling as a research method; focus on healthcare phenomena; and are written in English. Two reviewers will independently screen titles/abstracts and full-text articles. We will extract data, identify the array of storytelling approaches, and critically examine how storytelling was valued and used. An intensive collaboration will be woven throughout all review stages as academic researchers co-create this work with Indigenous patients, Elders, healthcare professionals, and administrators. Participatory strategies will include four relational gatherings throughout the project. Based on our findings, we will co-create a framework to guide the respectful use of storytelling as a method in Indigenous health research involving Indigenous and non-Indigenous peoples. DISCUSSION: This work will enable us to elucidate the extent, range, and nature of storytelling within Indigenous health research, to critically reflect on how it has been and could be used, and to develop guidance for the respectful use of this method within research that involves Indigenous peoples and settlers. Our findings will enable the advancement of storytelling methods which meaningfully include Indigenous perspectives, practices, and priorities to benefit the health and wellbeing of Indigenous communities. SYSTEMATIC REVIEW PROTOCOL REGISTRATION: Open Science Framework ( https://osf.io/rvf7q ).

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Analysis of aminophospholipid molecular species by methyl-beta-cyclodextrin modified micellar electrokinetic capillary chromatography with laser-induced fluorescence detection.

Micellar electrokinetic capillary chromatography (MEKC) with laser-induced fluorescence detection is used for the analysis of three classes of aminophospholipids: phosphatidylethanolamine (PE), phosphatidylserine (PS), and lysophosphatidylethanolamine (LPE) molecular species. 3-(2-Furoyl) quinoline-2-carboxaldehyde (FQ), a fluorogenic dye, was employed for labeling of these phospholipids. The FQ-labeled lipid species were then separated by sodium deoxycholate MEKC modified with methyl-beta-cyclodextrin. Baseline resolution of each class of phospholipids was achieved within 7 min. The migration time in each class increased with the carbon number of their side aliphatic chain. Separation efficiencies of approximately 3x10(5) plates were observed for most of these species. Concentration detection limits (3 sigma) were from 10(-9) to 10(-10) M for PE and LPE species and from 10(-8) to 10(-9) M for PS species. The relative standard deviations for migration time and peak area were less than 0.9% and 4.5%, respectively, for seven PE species. This method was applied to the separation of PE isolated from HT29 human colon cancer cells and roughly 30 PE species were resolved.

Capillary sodium dodecyl sulfate-DALT electrophoresis with laser-induced fluorescence detection for size-based analysis of proteins in human colon cancer cells.

Capillary sodium dodecyl sulfate (SDS)-DALT electrophoresis (SDS-DALT-CE) refers to CE separation of proteins based on their size; DALT is the abbreviation for Dalton, the unit used to describe molecular weight. In this work, seven proteins from 18 to 116 kDa were denatured by SDS, labeled by 3-(2-furoyl) quinoline-2-carboxaldehyde, separated by SDS-DALT-CE in polyethylene oxide sieving matrix, and detected by laser-induced fluorescence (LIF) in a sheath flow cuvette. This method was combined with detergent differential fractionation, which is a protein fractionation method using a series of detergent-containing buffers to sequentially extract protein fractions from cells, to analyze the proteins in HT29 human colon adenocarcinoma cells. In addition, on-column labeling was demonstrated for protein analysis by SDS-DALT-CE with LIF, and applied to analysis of proteins in a single HT29 cancer cell. Most proteins had molecular masses from 10 to 120 kDa. Similar protein profiles were obtained for single cells and protein extract of a large cell population.

Use of a small reporter molecule to determine cell-surface proteins by capillary electrophoresis and laser-induced fluorescence: use of 5-SAENTA-x8f for quantitation of the human equilibrative nucleoside transporter 1 protein.

The human equilibrative nucleoside transporter 1 protein (hENT1) is a major mediator of cellular entry of nucleosides and anticancer nucleoside drugs; its assay is important in understanding and diagnosing chemotherapy resistance. Here we present a novel assay for quantifying hENT1 using capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). A cellular population is treated with 5'-S-(2-aminoethyl)-N6-(4-nitrobenzyl)-5'-thioadenosine-x8-fluorescein (5-SAENTA-x8f), which binds with high affinity and specificity to the hENT1 protein. The cells are washed to remove excess reagent, lysed, and centrifuged, and the supernatant is analyzed by CE-LIF with the use of an internal standard. Accuracy was evaluated by comparing the capillary electrophoresis results with those obtained by flow cytometry; the results were highly correlated, r = 0.96. The relative standard deviation of the hENT1 assay was 10%, determined from nine independent assays of the same cell line, which is 3 times superior to results obtained in a flow cytometry assay. The detection limit for 5-SAENTA-x8f was 4300 molecules injected into the capillary.