Growing evidence shows that microRNAs (miRNAs) regulate various developmental and homeostatic events in vertebrates and invertebrates. Osteoblast differentiation is a key step in proper skeletal development and acquisition of bone mass; however, the physiological role of non-coding small RNAs, especially miRNAs, in osteoblast differentiation remains elusive. Here, through comprehensive analysis of miRNAs expression during osteoblast differentiation, we show that miR-206, previously viewed as a muscle-specific miRNA, is a key regulator of this process. miR-206 was expressed in osteoblasts, and its expression decreased over the course of osteoblast differentiation. Overexpression of miR-206 in osteoblasts inhibited their differentiation, and conversely, knockdown of miR-206 expression promoted osteoblast differentiation. In silico analysis and molecular experiments revealed connexin 43 (Cx43), a major gap junction protein in osteoblasts, as a target of miR-206, and restoration of Cx43 expression in miR-206-expressing osteoblasts rescued them from the inhibitory effect of miR-206 on osteoblast differentiation. Finally, transgenic mice expressing miR-206 in osteoblasts developed a low bone mass phenotype due to impaired osteoblast differentiation. Our data show that miRNA is a regulator of osteoblast differentiation.

Background: Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT’s accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. Objective: The aim of this study was to evaluate ChatGPT’s ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Methods: Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. Results: The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, –0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases “essential,” “recommended,” “best,” and “important” were used. Specifically, “essential” occurred in 4 out of 125, “recommended” in 12 out of 125, “best” in 6 out of 125, and “important” in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. Conclusions: The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

Chondrocyte differentiation is strictly regulated by various transcription factors, including Runx2 and Runx3; however, the physiological role of Runx1 in chondrocyte differentiation remains unknown. To examine the role of Runx1, we generated mesenchymal-cell-specific and chondrocyte-specific Runx1-deficient mice [Prx1 Runx1^f/f mice and α1(II) Runx1^f/f mice, respectively] to circumvent the embryonic lethality of Runx1-deficient mice. We then mated these mice with Runx2 mutant mice to obtain mesenchymal-cell-specific or chondrocyte-specific Runx1; Runx2 double-mutant mice [Prx1 DKO mice and α1(II) DKO mice, respectively]. Prx1 Runx1^f/f mice displayed a delay in sternal development and Prx1 DKO mice completely lacked a sternum. By contrast, α1(II) Runx1^f/f mice and α1(II) DKO mice did not show any abnormal sternal morphogenesis or chondrocyte differentiation. Notably, Runx1, Runx2 and the Prx1-Cre transgene were co-expressed specifically in the sternum, which explains the observation that the abnormalities were limited to the sternum. Histologically, mesenchymal cells condensed normally in the prospective sternum of Prx1 DKO mice; however, commitment to the chondrocyte lineage, which follows mesenchymal condensation, was significantly impaired. In situ hybridization analyses demonstrated that the expression of α1(II) collagen (Col2a1 — Mouse Genome Informatics), Sox5 and Sox6 in the prospective sternum of Prx1 DKO mice was severely attenuated, whereas Sox9 expression was unchanged. Molecular analyses revealed that Runx1 and Runx2 induce the expression of Sox5 and Sox6, which leads to the induction of α1(II) collagen expression via the direct regulation of promoter activity. Collectively, these results show that Runx1 and Runx2 cooperatively regulate sternal morphogenesis and the commitment of mesenchymal cells to become chondrocytes through the induction of Sox5 and Sox6.