Objective Multiple single‐nucleotide polymorphisms ( SNP s) conferring susceptibility to osteoarthritis ( OA ) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [ AI ]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease‐driving genetic variation. Methods AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available. The count fraction of the alternative alleles among the alternative and reference alleles together ( φ ) was determined for heterozygous individuals. A meta‐analysis was performed to generate a meta‐ φ and P value for each SNP with a false discovery rate ( FDR ) correction for multiple comparisons. To further validate AI events, we explored them as a function of multiple additional OA features. Results We observed a total of 2,070 SNP s that consistently marked AI of 1,031 unique genes in articular cartilage. Of these genes, 49 were found to be significantly differentially expressed (fold change <0.5 or >2, FDR <0.05) between preserved and paired lesioned cartilage, and 18 had previously been reported to confer susceptibility to OA and/or related phenotypes. Moreover, we identified notable highly significant AI SNP s in the CRLF 1 , WWP 2 , and RPS 3 genes that were related to multiple OA features. Conclusion We present a framework and resulting data set for researchers in the OA research field to probe for disease‐relevant genetic variation that affects gene expression in pivotal disease‐affected tissue. This likely includes putative novel compelling OA risk genes such as CRLF 1 , WWP 2 , and RPS 3 .

Study Objectives Sleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep-wake states of preterm infants in real-time at the bedside. Methods In this study, sleep-wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep-wake states. Independent training (n = 37) and validation datasets were used (n = 9). Finally, a setup was designed for real-time implementation at the bedside. Results The macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77). Conclusions With this study, to the best of our knowledge, a reliable, non-obtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep-wake states, potentially promoting the early brain development and well-being of preterm infants.

No abstract