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Fertility treatments like in vitro fertilization (IVF) or oocyte cryopreservation (OC) require the daily use of injectable gonadotropins and has been associated with treatment burden and attrition from fertility treatment. We conducted a randomized clinical trial to determine (1) whether educational videos about fertility medications improved infertility self-efficacy scale (ISES), fertility quality of life treatment (FertiQoL-T), and Perceived stress scale (PSS) scores and (2) if such videos improved confidence and reduced medication errors during a first ovarian stimulation cycle. Participants were given access to an online portal with randomized access to either placebo control videos focused on an orientation to IVF or experimental videos that reviewed the preparation and administration of medications used during ovarian stimulation in addition to the placebo videos. Participants completed pre and post-treatment questionnaires. 368 patients enrolled and 257 participants completed the study. There were no differences in ISES, FertiQoL-T or PSS scores between the two groups in an intention-to-treat (p = 0.18, 0.72, and 0.92, respectively) or per-protocol analysis (p = 0.11, 0.38, and 0.37, respectively). In the per protocol analysis, participants who watched experimental videos were four-fold more likely to report confidence administering medications OR 4.70 (95% CI: 2.10, 11.1; p < 0.01) and were 63% less likely to make medication errors OR 0.37 (95% CI: 0.14, 0.90; p = 0.03). Participants had similar likelihoods of rating videos as helpful and recommending videos to others (p = 0.06 and 0.3, respectively). Educational videos about fertility medications may not influence psychological well-being but might improve confidence in medication administration and reduce medication errors. Trial registration number: NCT02979990.

MIS-C is a severe hyperinflammatory condition with involvement of multiple organs that occurs in children who had COVID-19 infection. Accurate diagnostic tests are needed to guide management and appropriate treatment and to inform clinical trials of experimental drugs and vaccines, yet the diagnosis of MIS-C is highly challenging due to overlapping clinical features with other acute syndromes in hospitalized patients. Here we developed a gene expression-based classifier for MIS-C by RNA-Seq transcriptome profiling and machine learning based analyses of 195 whole blood RNA and 76 plasma cell-free RNA samples from 191 subjects, including 95 MIS-C patients, 66 COVID-19 infected patients with moderately severe to severe disease, and 30 uninfected controls. We divided the group into a training set (70%) and test set (30%). After selection of the top 300 differentially expressed genes in the training set, we simultaneously trained 13 classification models to distinguish patients with MIS-C and COVID-19 from controls using five-fold cross-validation and grid search hyperparameter tuning. The final optimal classifier models had 100% diagnostic accuracy for MIS-C (versus non-MIS-C) and 85% accuracy for severe COVID-19 (versus mild/asymptomatic COVID-19). Orthogonal validation of a random subset of 11 genes from the final models using quantitative RT-PCR confirmed the differential expression and ability to discriminate MIS-C and COVID-19 from controls. These results underscore the utility of a gene expression classifier for diagnosis of MIS-C and severe COVID-19 as specific and objective biomarkers for these conditions.

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