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Stanniocalcin 1 (STC1) is a calcium- and phosphate-regulating hormone that is expressed in all tissues, including bone tissues, and is involved in calcium and phosphate homeostasis. Previously, STC1 expression was found to be increased by 1,25-dihydroxyvitamin D 3 [1,25(OH) 2 D 3 ] administration in renal proximal tubular cells. In this study, we investigated whether STC1 directly regulates osteoblast differentiation or reciprocally controls the effects of 1,25(OH) 2 D 3 on osteoblasts to contribute to bone homeostasis. We found that STC1 inhibited osteoblast differentiation in vitro and bone morphogenetic protein 2 (BMP2)-induced ectopic bone formation in vivo. Moreover, 1,25(OH) 2 D 3 increased STC1 expression through direct binding to the Stc1 promoter of the vitamin D receptor (VDR). STC1 activated the 1,25(OH) 2 D 3 –VDR signaling pathway through the upregulation of VDR expression mediated by the inhibition of Akt phosphorylation in osteoblasts. STC1 further increased the effects of 1,25(OH) 2 D 3 on receptor activator of nuclear factor-κB ligand (RANKL) secretion and inhibited osteoblast differentiation by exhibiting a positive correlation with 1,25(OH) 2 D 3 . The long-bone phenotype of transgenic mice overexpressing STC1 specifically in osteoblasts was not significantly different from that of wild-type mice. However, compared with that in the wild-type mice, 1,25(OH) 2 D 3 administration significantly decreased bone mass in the STC1 transgenic mice. Collectively, these results suggest that STC1 negatively regulates osteoblast differentiation and bone formation; however, the inhibitory effect of STC1 on osteoblasts is transient and can be reversed under normal conditions. Nevertheless, the synergistic effect of STC1 and 1,25(OH) 2 D 3 through 1,25(OH) 2 D 3 administration may reduce bone mass by inhibiting osteoblast differentiation. Bone mineralization regulation: Stanniocalcin 1 & Vitamin D3 synergize In the field of bone health, it’s important to understand how our bodies control bone creation. This research investigates how Stanniocalcin 1 and 1,25-dihydroxyvitamin D3 work together to affect bone mineralization by osteoblasts. The team used mouse models and cell cultures in their experiment. They found that STC1 alone can slow down osteoblast differentiation and bone creation, but this effect is stronger when 1,25-dihydroxyvitamin D3 is also present. This suggests a complex relationship that could affect bone health. They conclude that the combined regulation by STC1 and 1,25-dihydroxyvitamin D3 is a key factor in bone mineralization, providing new insights into managing bone health. This knowledge could lead to treatments for bone diseases by targeting these pathways. Future research may show how to adjust these interactions for treatment benefits, potentially improving results for those with bone health problems. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Background Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. Results BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, ’Pathways summary’ and ’Oncoprint’ with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. Conclusions BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer.

Environmental health studies are of great interest in research to evaluate the mortality-temperature relationship by adjusting spatially correlated random effects as well as identifying significant change points in temperature. However, this relationship is often not expressed using parametric models, which makes identifying change points an even more challenging problem. This paper proposes a unified semiparametric approach to simultaneously identify the nonlinear mortality-temperature relationship and detect spatially-dependent change points. A unified method is proposed for the model estimation, spatially dependent change points detection, and testing whether they are significant simultaneously by a permutation-based test. We operate under the assumption that change points remain constant, yet acknowledge the uncertainty regarding their precise number. These change points are influenced by the smoothing of an unknown function, which in turn relies on a smoothing variable and spatial random effects. Consequently, the detection of change points may be influenced by spatial effects. In this paper, several simulation studies are conducted to evaluate the performance of our proposed approach. The advantages of this unified approach are demonstrated using epidemiological data on mortality and temperature.

Several CC subfamily chemokines have been reported to regulate bone metabolism by affecting osteoblast or osteoclast differentiation. However, the role of monocyte chemotactic protein 3 (MCP-3), a CC chemokine, in bone remodeling is not well understood. Here, we show that MCP-3 regulates bone remodeling by promoting osteoblast differentiation and inhibiting osteoclast differentiation. In a Ccr3-dependent manner, MCP-3 promoted osteoblast differentiation by stimulating p38 phosphorylation and suppressed osteoclast differentiation by upregulating interferon beta. MCP-3 increased bone morphogenetic protein 2-induced ectopic bone formation, and mice with MCP-3-overexpressing osteoblast precursor cells presented increased bone mass. Moreover, MCP-3 exhibited therapeutic effects by abrogating receptor activator of nuclear factor kappa-B ligand-induced bone loss. Therefore, MCP-3 has therapeutic potential for diseases involving bone loss due to its positive role in osteoblast differentiation and negative role in osteoclast differentiation. MCP-3 Enhances Bone Formation and Reduces Bone Loss Our bones are always changing, needing a balance between bone creation and bone breakdown. Researchers investigates how a specific protein, MCP-3, affects this balance. They found that MCP-3 promotes the creation of osteoblasts and stops the creation of osteoclasts. The study involved both in vitro and in vivo methods, looking at how changing MCP-3 levels impacts bone change processes. Researchers found that increasing MCP-3 levels in bone-making cells resulted in stronger bones in mice, suggesting MCP-3 is key in promoting bone creation and stopping excessive bone loss. This was partly due to MCP-3’s interaction with specific cell receptors and signaling pathways that affect bone cell activity. This research paves the way for future studies on MCP-3 as a target for bone-strengthening therapies, potentially leading to new treatments for diseases that weaken bones. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations.

Inter-joint coordination and gait variability in knee osteoarthritis (KOA) has not been well investigated. Hip-knee cyclograms can visualize the relationship between the hip and knee joint simultaneously. The aim of this study was to elucidate differences in inter-joint coordination and gait variability with respect to KOA severity using hip-knee cyclograms. Fifty participants with KOA (early KOA, n = 20; advanced KOA, n = 30) and 26 participants (≥ 50 years) without KOA were recruited. We analyzed inter-joint coordination by hip-knee cyclogram parameters including range of motion (RoM), center of mass (CoM), perimeter, and area. Gait variability was assessed by the coefficient of variance (CV) of hip-knee cyclogram parameters. Knee RoM was significantly reduced and total perimeter tended to be decreased with KOA progression. KOA patients (both early and advanced) had reduced stance phase perimeter, swing phase area, and total area than controls. Reduced knee CoM and swing phase perimeter were observed only in advanced KOA. Both KOA groups had a greater CV for CoM, knee RoM, perimeter (stance phase, swing phase and total) and swing phase area than the controls. Increased CV of hip RoM was only observed in advanced KOA. These results demonstrate that hip-knee cyclograms can provide insights into KOA patient gait.

We present PKPy, an open-source Python framework designed to automate population pharmacokinetic analysis workflows. The framework emphasizes user accessibility by minimizing the need for manual parameter initialization while maintaining analytical rigor. PKPy implements both one-compartment and two-compartment pharmacokinetic models (with and without first-order absorption) with integrated capabilities for parameter estimation, covariate analysis, and comprehensive diagnostics. The framework’s performance was evaluated through simulation studies across varying sample sizes (20–100 subjects) and model complexities. Results demonstrated robust parameter estimation for clearance and volume of distribution, with bias consistently below 3% and recovery rates exceeding 98% in one-compartment models. The framework successfully identified true covariate relationships with 100% accuracy across all scenarios, while maintaining high model fit quality ( R 2  ≥ 0.97). For two-compartment models, the framework showed comparable performance with slightly higher parameter bias (5–10%) but maintained excellent fit quality ( R 2  ≥ 0.99). Advanced validation metrics including average fold error (AFE) and absolute average fold error (AAFE) were implemented, with AFE values ranging from 1.01–1.03 and AAFE < 1.05 across test scenarios, indicating excellent prediction accuracy. The key pharmacokinetic parameters estimated by the framework include clearance (CL), volume of distribution (V or V1/V2 for two-compartment models), inter-compartmental clearance (Q), and when applicable, the absorption rate constant (Ka). Application to the classic Theophylline dataset demonstrated PKPy’s practical utility, achieving comparable results whether or not initial parameter estimates were provided. The framework successfully estimated population parameters with good model fit ( R 2  = 0.933) and automatically identified physiologically plausible covariate relationships. Comprehensive comparisons with existing software packages (Saemix+PKNCA, and simulated comparisons with nlmixr2) revealed PKPy’s advantages in computational efficiency, with installation times of 16s versus 96s and analysis times of 13–15s versus 101–102s. While PKPy employs a two-stage approach rather than full nonlinear mixed-effects modeling, it achieved consistent parameter estimates with minimal bias for data-rich scenarios. PKPy leverages Python’s scientific computing ecosystem to provide an accessible, transparent platform for pharmacokinetic analysis. The framework’s automated approach, support for multiple compartment models, and comprehensive workflow integration demonstrate the potential for reducing barriers to entry in pharmacometric analysis while maintaining scientific rigor.

Background Tripartite motif-containing 27 (TRIM27) is highly expressed in the mouse thymus, spleen, and hematopoietic compartment cells and regulates cell proliferation, apoptosis, and innate immune responses. However, the role of TRIM27 in bone remodeling remains unknown. This study aimed to investigate the role of TRIM27 in the differentiation of osteoclasts and osteoblasts. Methods We measured the effects of overexpression or knockdown of TRIM27 in osteoclasts and osteoblasts using real-time PCR and Western blot analysis to quantify the mRNA and protein levels of marker genes. Additionally, we performed an in vivo analysis of TRIM27 knockout mice through bone mineral density analysis and histological analysis. Results TRIM27 deficiency decreased bone mineral density by enhancing osteoclast differentiation and inhibiting osteoblast differentiation. Overexpression of TRIM27 in osteoclast precursors suppressed osteoclast formation and resorption activity, and ectopic expression of TRIM27 in osteoblast precursors induced osteoblast differentiation and mineralization. Additionally, we found that TRIM27 attenuated NF-κB activation in both osteoclasts and osteoblasts by interacting with TAB2 and promoting TAB2 degradation through lysosomal-dependent pathways, thereby inhibiting NF-κB signaling. Conclusions Our results identify TRIM27 as a novel negative regulator of NF-κB in bone remodeling, suggesting that regulating TRIM27 may be useful in developing treatments for musculoskeletal diseases, such as osteoporosis.

One of the advantages of inkjet printing in digital manufacturing is the ability to use multiple nozzles simultaneously to improve the productivity of the processes. However, the use of multiple nozzles makes inkjet status monitoring more difficult. The jetting nozzles must be carefully selected to ensure the quality of printed products, which is challenging for most inkjet processes that use multi-nozzles. In this article, we improved inkjet print head monitoring based on self-sensing signals by using machine learning algorithms. Specifically, supervised machine learning models were used to classify nozzle jetting conditions. For this purpose, the self-sensing signals were acquired, and the feature information was extracted for training. A vision algorithm was developed to label the nozzle status for classification. The trained models showed that the classification accuracy is higher than 99.6% when self-sensing signals are used for monitoring. We also proposed a so-called hybrid monitoring method using trained machine learning models, which divides the feature space into three regions based on predicted jetting probability: certain jetting, certain non-jetting, and doubt regions. Then, the nozzles with uncertain status in the doubt region can be verified by jet visualization to improve the accuracy and efficiency of the monitoring process.

With the development of technology, wireless and IoT devices are increasingly used from daily life to industry, placing demands on rapid and efficient manufacturing processes. This study demonstrates the fabrication of an IoT device using a roll-to-roll printing process, which could shorten the device fabrication time and reduce the cost of mass production. Here, the fabricated IoT device is designed to acquire data through the sensor, process the data, and communicate with end-user devices via Bluetooth communication. For fabrication, a four-layer circuit platform consisting of two conductive layers, an insulating layer including through holes, and a solder resist layer is directly printed using a roll-to-roll screen printing method. After the printing of the circuit platform, an additional layer of solder paste is printed to assemble the electrical components into the device, inspiring the fully roll-to-roll process for device fabrication. Successful IoT device deployment opens the chance to broaden the roll-to-roll fabrication process to other flexible and multilayer electronic applications.