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The mammalian hair cycle requires both the vitamin D receptor (VDR) and the hairless (Hr) corepressor, each of which is expressed in the hair follicle. Hr interacts directly with VDR to repress VDR-targeted transcription. Herein, we further map the VDR-interaction domain to regions in the C-terminal half of Hr that contain two LXXLL-like pairs of motifs known to mediate contact of Hr with the RAR-related orphan receptor alpha and with the thyroid hormone receptor, respectively. Site-directed mutagenesis indicates that all four hydrophobic motifs are required for VDR transrepression by Hr. Point mutation of rat Hr at conserved residues corresponding to natural mutants causing alopecia in mice (G985W and a C-terminal deletion ΔAK) and in humans (P95S, C422Y, E611G, R640Q, C642G, N988S, D1030N, A1040T, V1074M and V1154D), as well as alteration of residues in the C-terminal Jumonji C domain implicated in histone demethylation activity (C1025G/E1027G and H1143G) revealed that all Hr mutants retained VDR association, and that transrepressor activity was selectively abrogated in C642G, G985W, N988S, D1030N, V1074M, H1143G and V1154D. Four of these latter Hr mutants (C642G, N988S, D1030N and V1154D) were found to associate normally with histone deacetylase-3. Finally, we identified three regions of human VDR necessary for association with Hr, namely residues 109-111, 134-201, and 202-303. It is concluded that Hr and VDR interact via multiple protein-protein interfaces, with Hr recruiting histone deacetylases and possibly itself catalyzing histone demethylation to effect chromatin remodeling and repress the transcription of VDR target genes that control the hair cycle.Keywords calcitriol receptors; histone deacetylase; histone demethylase; human HR protein; rat hr protein; Jumonji domainThe hairless protein (Hr), a 130 kDa nuclear transcription factor, is mutated in at least two forms of human alopecia: alopecia universalis congenita and atrichia with papular lesions Ahmad et al., 1999]. Also, mouse models in which hr gene expression has been reduced or eliminated [Zarach et al., 2004] display a phenotype of hair loss, hyperproliferation of skin and dermal cysts. However, the molecular mechanisms by which Hr exerts its effects on the skin and on hair growth/maintenance are still being elucidated. Hr, which is also highly expressed in brain, has been shown to interact with the thyroid hormone receptor (TR) and with the RAR-related orphan receptors (RORs), especially RORα [Moraitis et al., 2002]. In addition, some of the functional domains in Hr mediating these interactions have been mapped (Fig. 1D and Fig. 2A). Hr possesses a nuclear localization signal (NLS) from residues 437-454 [Djabali et al., 2001] and a single zinc-finger motif (amino acids 620-645) [Cachon-Gonzalez et al., 1994] (see also Fig. 2A), as well as four motifs of hydrophobic amino acids, two of the form LXXLL (where L = leucine and X = any amino acid residue) and two of the form ΦXXΦΦ (where Φ = any one of the hydrophobic amino acids leucine, i...

At time of transplant, pediatric HCT patients frequently have 25-hydroxyvitamin D insufficiency/deficiency, and this occurs more commonly than in the healthy pediatric population. HCT patients rarely follow recommended guidelines to take supplemental vitamin D, consume the Recommended Daily Allowance for vitamin D, or regularly use sunscreen. Further studies are needed to determine whether vitamin D insufficiency/deficiency persists long term in HCT patients and requires dietary and behavioral interventions.

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Introduction Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence. Methods We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions. Results The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups. Conclusion The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence. Support