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Although the nonselective β‐blocker, propranolol, improves bone density with parathyroid hormone (PTH) treatment in mice, the mechanism of this effect is unclear. To address this, we used a combination of in vitro and in vivo approaches to address how propranolol influences bone remodeling in the context of PTH treatment. In female C57BL/6J mice, intermittent PTH and propranolol administration had complementary effects in the trabecular bone of the distal femur and fifth lumbar vertebra (L5), with combination treatment achieving microarchitectural parameters beyond that of PTH alone. Combined treatment improved the serum bone formation marker, procollagen type 1 N propeptide (P1NP), but did not impact other histomorphometric parameters relating to osteoblast function at the L5. In vitro, propranolol amplified the acute, PTH‐induced, intracellular calcium signal in osteoblast‐like cells. The most striking finding, however, was suppression of PTH‐induced bone resorption. Despite this, PTH‐induced receptor activator of nuclear factor κ‐B ligand (RANKL) mRNA and protein levels were unaltered by propranolol, which led us to hypothesize that propranolol could act directly on osteoclasts. Using in situ methods, we found Adrb2 expression in osteoclasts in vivo, suggesting β‐blockers may directly impact osteoclasts. Consistent with this, we found propranolol directly suppresses osteoclast differentiation in vitro. Taken together, this work suggests a strong anti‐osteoclastic effect of nonselective β‐blockers in vivo, indicating that combining propranolol with PTH could be beneficial to patients with extremely low bone density. © 2022 American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

2,4-dichlorophenoxy acetic acid (2,4-D) and 1,1-dimethyl-4,4-bipiridinium chloride (paraquat) are among the most widely used herbicides and are known to be toxic. Fabrication of green adsorbents which are capable of removing both herbicides remains a challenge. Here, we fabricate a novel adsorbent from tropical waste wood and use a facile, chitosan-mediated N-heteroatom functionalization technique to augment surface nitrogen and improve specific surface area. The addition of 20 wt% chitosan to the waste wood feedstock prior to activation, increased specific surface area by 300 m 2 /g (∼25%) and nitrogen content by 7-fold. This functionalized material removed 69% of 2,4-D and 82% of paraquat at initial concentrations of 4 ppm and 40 ppm from model solutions at pH 7. It also removed 39% 2,4-D and 93% paraquat from binary mixtures demonstrating its versatility. 2,4-D adsorption increased with chitosan addition suggesting synergistic effects between protonated amine functions and the anionic herbicide form. Paraquat adsorption was negatively correlated with chitosan addition, implying antagonistic interaction between protonated amine functions and quaternary nitrogen atoms on herbicide molecules. Adsorption of both herbicides was spontaneous, entropically favored and exothermic with ΔG °: 19.2 kJ/mol and -28.8 kJ/mol; ΔS °: 7.42 and 28.6 J/Kmol and ΔH °: 17.0 kJ/mol and -20.1 kJ/mol for 2,4-D and paraquat respectively. Chitosan addition therefore provides a facile and green alternative for N-heteroatom functionalization, and these nitrogen-doped materials are promising candidates for the removal of multiple herbicides from aqueous systems., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Hamant E. France reports financial support was provided by Sustainable Guyana Program a partnership between Trent University and the University of Guyana funded by CGX Energy Inc. and Frontera Energy Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Motivated by studies showing an association between beta blocker (BB) use and positive bone outcomes, a pilot randomized control trial (RCT) was performed at the Mayo Clinic which randomized postmenopausal women to placebo, propranolol (40 or 80 mg twice daily), atenolol (50 mg/day), or nebivolol (5 mg/day) to determine changes in bone turnover markers (BTMs) and in bone mineral density (BMD) over 20 weeks. Pharmacogenetic effects and microRNA-mediated mechanisms involving beta adrenergic receptor and related genes have previously been found. We sought to validate these effects and discover new candidates in an ancillary study to the pilot clinical trial. We genotyped all participants and performed microRNA (miRNA) sequencing at baseline and at 20 weeks for 24 participants from the atenolol or placebo groups. We discovered several variants in ADRB1, ADRB2, and HDAC4 which showed significant pharmacogenetic effects with BMD at multiple sites and with BTMs. Our miRNA results showed a significant treatment effect for miR-19a-3p over time with atenolol use in the low-responder group compared to placebo. Overall, the longitudinal miRNA analysis showed a large number of miRNAs which were up-regulated over the trial in the low responders but not the high responders compared to placebo, of which miR-19a-3p was one example. Finally, we compared the response to atenolol treatment for cardiovascular traits (pulse, blood pressure) with the response for the bone resorption marker, CTX, and found a largely independent effect. Our results have implications for personalized therapy and for understanding mechanisms of BB treatment effect on bone., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Osteoclast cell image analysis plays a key role in osteoporosis research, but it typically involves extensive manual image processing and hand annotations by a trained expert. In the last few years, a handful of machine learning approaches for osteoclast image analysis have been developed, but none have addressed the full instance segmentation task required to produce the same output as that of the human expert led process. Furthermore, none of the prior, fully automated algorithms have publicly available code, pretrained models, or annotated datasets, inhibiting reproduction and extension of their work. We present a new dataset with ~2 × 10 5 expert annotated mouse osteoclast masks, together with a deep learning instance segmentation method which works for both in vitro mouse osteoclast cells on plastic tissue culture plates and human osteoclast cells on bone chips. To our knowledge, this is the first work to automate the full osteoclast instance segmentation task. Our method achieves a performance of 0.82 mAP 0.5 (mean average precision at intersection-over-union threshold of 0.5) in cross validation for mouse osteoclasts. We present a novel n uclei-aware o steoclast i nstance se gmentation training strategy ( NOISe ) based on the unique biology of osteoclasts, to improve the model's generalizability and boost the mAP 0.5 from 0.60 to 0.82 on human osteoclasts. We publish our annotated mouse osteoclast image dataset, instance segmentation models, and code at github.com/michaelwwan/noise to enable reproducibility and to provide a public tool to accelerate osteoporosis research.