Sociodemographic factors linked to food allergy diagnosis among high-risk children with atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a known risk factor for the development of food allergy (FA). Prior work has suggested disparities in diagnosis/management of FA in urban populations. OBJECTIVE: To determine whether socioeconomic conditions, as measured by the area deprivation index and insurance status, or racial/ethnic self-identity was associated with risk of FA diagnosis (DFA), evaluation by an allergist, or objective FA testing among high-risk children with AD. METHODS: This is a retrospective cohort study of pediatric patients with physician-diagnosed AD who had received primary care at a single urban academic tertiary care center between 2009 and 2022. Statistical analysis in SPSS (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0, Armonk, NY) used χ2, analysis of variance, and logistic regression. RESULTS: In a total of 3365 pediatric subjects, 41.3% identified as non-Hispanic Black, 33.9% Hispanic, 6.9% Asian, and 14.9% non-Hispanic White. Hispanic children with AD and DFA were significantly less likely to be evaluated by an allergist than White or Asian children (65.9% vs 82.8% and 80.3%, P = .001 and P = .02). Non-Hispanic Black children with AD and DFA were more likely to have no objective FA testing than White children (20.9% vs 12.1%, P = .04). The White and Asian children were more likely to undergo the thorough combination of both blood and skin testing for DFA than Black or Hispanic children (15.5% and 22.4% vs 7.1% and 7.9%, respectively, P = .007, P = .00005, P = .03, P = .0008). CONCLUSION: Labeling at-risk young children with FA without thorough objective testing can affect their nutrition and quality of life. Barriers to equitable evaluation of DFA should be further investigated and addressed.

On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions.

We exhibit examples of high-dimensional unimodal posterior distributions arising in nonlinear regression models with Gaussian process priors for which Markov chain Monte Carlo (MCMC) methods can take an exponential run-time to enter the regions where the bulk of the posterior measure concentrates. Our results apply to worst-case initialized ('cold start') algorithms that are local in the sense that their step sizes cannot be too large on average. The counter-examples hold for general MCMC schemes based on gradient or random walk steps, and the theory is illustrated for Metropolis-Hastings adjusted methods such as preconditioned Crank-Nicolson and Metropolis-adjusted Langevin algorithm. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

An Unusual Sticky Situation of New-Onset Right Sided Heart Failure.

This case highlights the importance of having constrictive pericarditis (CP) as a differential diagnosis in unexplained sign and symptoms of right-sided heart failure. This case portrays challenges in diagnosing CP caused by certain rheumatologic diseases despite advances in diagnostic modalities, clinical suspicion remains the most important tool for this diagnosis.

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target.

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.

Cyclin C regulates adipogenesis by stimulating transcriptional activity of CCAAT/enhancer-binding protein α.

Brown adipose tissue is important for maintaining energy homeostasis and adaptive thermogenesis in rodents and humans. As disorders arising from dysregulated energy metabolism, such as obesity and metabolic diseases, have increased, so has interest in the molecular mechanisms of adipocyte biology. Using a functional screen, we identified cyclin C (CycC), a conserved subunit of the Mediator complex, as a novel regulator for brown adipocyte formation. siRNA-mediated CycC knockdown (KD) in brown preadipocytes impaired the early transcriptional program of differentiation, and genetic KO of CycC completely blocked the differentiation process. RNA sequencing analyses of CycC-KD revealed a critical role of CycC in activating genes co-regulated by peroxisome proliferator activated receptor γ (PPARγ) and CCAAT/enhancer-binding protein α (C/EBPα). Overexpression of PPARγ2 or addition of the PPARγ ligand rosiglitazone rescued the defects in CycC-KO brown preadipocytes and efficiently activated the PPARγ-responsive promoters in both WT and CycC-KO cells, suggesting that CycC is not essential for PPARγ transcriptional activity. In contrast, CycC-KO significantly reduced C/EBPα-dependent gene expression. Unlike for PPARγ, overexpression of C/EBPα could not induce C/EBPα target gene expression in CycC-KO cells or rescue the CycC-KO defects in brown adipogenesis, suggesting that CycC is essential for C/EBPα-mediated gene activation. CycC physically interacted with C/EBPα, and this interaction was required for C/EBPα transactivation domain activity. Consistent with the role of C/EBPα in white adipogenesis, CycC-KD also inhibited differentiation of 3T3-L1 cells into white adipocytes. Together, these data indicate that CycC activates adipogenesis in part by stimulating the transcriptional activity of C/EBPα.

Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity.

Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.