The P2 purinergic receptor family implicated in many physiological processes, including neurotransmission, mechanical adaptation and inflammation, consists of ATP-gated non-specific cation channels P2XRs and G-protein coupled receptors P2YRs. Different cells, including bone forming osteoblasts, express multiple P2 receptors; however, how P2X and P2Y receptors interact in generating cellular responses to various doses of [ATP] remains poorly understood. Using primary bone marrow and compact bone derived osteoblasts and BMP2-expressing C2C12 osteoblastic cells, we demonstrated conserved features in the P2-mediated Ca²⁺responses to ATP, including a transition of Ca²⁺response signatures from transient at low [ATP] to oscillatory at moderate [ATP], and back to transient at high [ATP], and a non-monotonic changes in the response magnitudes which exhibited two troughs at 10⁻⁴and 10⁻²M [ATP]. We identified P2Y2 and P2X7 receptors as predominantly contributing to these responses and constructed a mathematical model of P2Y2R-induced inositol trisphosphate (IP₃) mediated Ca²⁺release coupled to a Markov model of P2X7R dynamics to study this system. Model predictions were validated using parental and CRISPR/Cas9-generated P2Y2 and P2Y7 knockouts in osteoblastic C2C12-BMP cells. Activation of P2Y2 by progressively increasing [ATP] induced a transition from transient to oscillatory to transient Ca²⁺responses due to the biphasic nature of IP₃Rs and the interaction of SERCA pumps with IP₃Rs. At high [ATP], activation of P2X7R modulated the response magnitudes through an interplay between the biphasic nature of IP₃Rs and the desensitization kinetics of P2X7Rs. Moreover, we found that P2Y2 activity may alter the kinetics of P2X7 towards favouring naïve state activation. Finally, we demonstrated the functional consequences of lacking P2Y2 or P2X7 in osteoblast mechanotransduction. This study thus provides important insights into the biophysical mechanisms underlying ATP-dependent Ca²⁺response signatures, which are important in mediating bone mechanoadaptation.

Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. Results: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in $55\pm 6$ additional relevant labels per hour. Conclusions: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Background: Chest point of care ultrasound (POCUS) is a first-line diagnostic test to identify lung sliding, an important artifact to diagnose or rule out pneumothorax. Despite enthusiastic adoption of this modality, the interrater reliability for physicians to identify lung sliding is unknown. Additionally, the relative diagnostic performance of physicians interpreting B-mode and M-mode ultrasound is unclear. We sought to determine the interrater reliability of physicians to detect lung sliding on B-mode and M-mode POCUS. Methods: We performed a cross-sectional interrater agreement study surveying acute care physicians on their interpretation of 20 B-mode and M-mode POCUS clips. Two experienced clinicians determined the reference standard diagnosis. Respondents reported their interpretation of each POCUS B-mode clip or M-mode image. The primary outcome was the interrater agreement, determined by an intra-class correlation coefficient (ICC). Results: From September to November 2023, there were 20 survey respondents. Fourteen (70%) respondents were resident physicians. Respondents were confident or very confident in their skill performing chest POCUS in 14 (70%) cases, with 19 (90%) performing chest POCUS every week or more frequently. The ICC on B-mode was 0.44 and for M-mode was 0.43, indicating moderate agreement. There were no significant differences in interrater reliability between subgroups of confidence or experience. Conclusion: There is only moderate interrater reliability between clinicians to diagnose lung sliding. Clinicians have superior accuracy on B-mode compared to M-mode clips.