Nail clipping followed by periodic acideSchiff with diastase (PAS-D) staining has become the gold standard for diagnosis of onychomycosis, with relative ease of performance and high sensitivity. However studies have suggested this method may be less cost effective than inoffice alternatives due to the expense incurred by staining and pathologist interpretation. We trained a convolutional neural network (CNN) to classify digitized slides of nail clippings as positive or negative for onychomycosis. Specimens stained with both hematoxylin and eosin (H&E, n¼134) and PAS-D (n¼135) were digitized at 40X magnification and subsequently divided into training, validation, and test cohorts. Image preprocessing included tiling each composite image into 299 x 299 pixel patches and labeling non-empty patches as positive or negative based on prior diagnosis. Training was performed with the Inception-v3 CNN architecture with separate models developed for H&E-or PAS-D-stained slides. Initial classification of each stained specimen was obtained by averaging tile scores. For H&E-stained slides, using average tile score on test set specimens, the area under the curve (AUC) for the receiver operating characteristic curve was 0.86 (95% confidence interval [CI]: 0.74-0.96). For the model trained on PAS-D-stained slides, the analogous AUC was 0.88 (95% CI: 0.77-0.97). These results support the potential role of CNNs to automate diagnosis of onychomycosis, even on H&E-stained slides. Further studies to classify all imaged tissue per specimen rather than subcomponent tiles and to compare CNN performance to dermatopathologists are underway.

Human behaviors are at least partially driven by genomic regions that influence human-specific neurodevelopment. This includes genomic regions undergoing human specific sequence acceleration (Human Accelerated Regions or HARs) and regions showing human-specific enhancer activity (Human Gained Enhancers or HGEs) not present in other primates. However, prior studies on HAR/HGE activities involved mixtures of brain cell types and focused only on putative downstream target genes. Here, we directly measured cell type specific HAR/HGE activity in the developing fetal human brain using two independent single-cell chromatin accessibility datasets with matching single-cell gene expression data. Transcription factor (TF) motif analyses identified upstream TFs binding to HARs/HGEs and identified LHX2, a key regulator of forebrain development, as an active HGE regulator in neuronal progenitors. We integrated our TF motif analyses with published chromatin interaction maps to build detailed regulatory networks where TFs are linked to downstream genes via HARs/HGEs. Through these networks, we identified a potential regulatory role for NFIC in human neuronal progenitor networks via modulating the Notch signaling and cell adhesion pathways. Therefore, by using a single cell multi-omics approach, we were able to capture both the upstream and downstream regulatory context of HARs/HGEs, which may provide a more comprehensive picture of the roles HARs/HGEs play amongst diverse fetal cell types of the developing human brain.

Transgenic mice carrying the human UDP‐glucuronosyltransferase‐1 locus (Tg‐UGT1) was recently created and shown to express the nine UGT1A genes in a pattern that resembles their expression in human tissues. In the present study, UGT1A1, 1A3, 1A4, and 1A6 have been identified as targets of PPARα in human hepatocytes and Tg‐UGT1 mice. Oral administration of WY‐14643 to Tg‐UGT1 mice led to inductionof these proteins in either the liver, gastrointestinal tract, or kidney. With UGT1A3 previously identified as the major human enzyme involved in human C24‐glucuronidation of lithocholic acid (LCA), the dramatic induction of liver UGT1A3 RNA in Tg‐UGT1 mice was consistent with the formation of LCA‐24G in plasma. Furthermore, PPAR‐responsive elements were identified flanking the UGT1A1, UGT1A3, and UGT1A6 genes by a combination of site‐directed mutagenesis, specific binding to the PPARα receptor, and functional response in HepG2 cells overexpressing PPARα. In conclusion, these results suggest that oral fibrate treatment in humans will induce the UGT1A family of proteins in the gastrointestinal tract and liver, influencing first‐pass metabolism of other drugs that are taken concurrently with hypolipidemic therapy. (Supported by USPHS grants ES10337 and GM49135).