Hypercalcemia in children induced by denosumab: a case report and an analysis of the FDA adverse event reporting system database.

BACKGROUND: The potential risks of denosumab on pediatric patients have raised concerns about its safety. This article aims to analyze the adverse effects of denosumab in minors, with a specific focus on hypercalcemia. RESEARCH DESIGN AND METHODS: A case study involving a child was analyzed. The OpenVigil 2.1 was utilized to extract adverse event data from the FAERS database, focusing on denosumab as the primary suspect drug in pediatric patients. The study also reviewed published cases of children developing hypercalcemia after discontinuing denosumab. RESULTS: The incidence of denosumab induced hypercalcemia in individuals under 18 years old is significantly higher than the overall incidence. The signal value for hypercalcemia was higher in the male group and was highest in the adolescent group. Hypercalcemia usually appeared approximately 4 months after denosumab discontinuation. Males had a higher peak blood calcium level. Patients aged 0-11 years had a higher average peak serum calcium compared to aged 12-17 years. CONCLUSIONS: This study highlights the risk of hypercalcemia after discontinuation of denosumab in minors, with young age and male gender identified as potential high-risk factors. These findings offer valuable safety warnings and preventative measures for the secure administration of this drug in pediatric populations.

Proton spot dose estimation based on positron activity distributions with neural network.

BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. PURPOSE: This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. METHODS: Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. RESULTS: The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. CONCLUSIONS: Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

Improved Diversity-Promoting Collaborative Metric Learning for Recommendation.

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML.

Macrophages in tissue repair and regeneration: insights from zebrafish.

Macrophages play crucial and versatile roles in regulating tissue repair and regeneration upon injury. However, due to their complex compositional heterogeneity and functional plasticity, deciphering the nature of different macrophage subpopulations and unraveling their dynamics and precise roles during the repair process have been challenging. With its distinct advantages, zebrafish (Danio rerio) has emerged as an invaluable model for studying macrophage development and functions, especially in tissue repair and regeneration, providing valuable insights into our understanding of macrophage biology in health and diseases. In this review, we present the current knowledge and challenges associated with the role of macrophages in tissue repair and regeneration, highlighting the significant contributions made by zebrafish studies. We discuss the unique advantages of the zebrafish model, including its genetic tools, imaging techniques, and regenerative capacities, which have greatly facilitated the investigation of macrophages in these processes. Additionally, we outline the potential of zebrafish research in addressing the remaining challenges and advancing our understanding of the intricate interplay between macrophages and tissue repair and regeneration.

3DOF displacement sensor based on the self-imaging effect of optical micro-gratings.

In recent years, there has been an increasing demand for a multiple degrees of freedom (DOF) measurement system with high performance and high integration. Here, we report a 3DOF displacement sensor based on the self-imaging effect of optical micro-gratings. The optical field distribution behind a micro-grating with a period of 3 µm is analyzed theoretically. The transmission properties of a double-grating structure are investigated in theory. In the experiment, 3DOF displacement measurement within a range of 1 mm is demonstrated. Using an interpolation circuit with a subdividing factor of 1000, displacement measurement with a theoretical resolution of 3 nm is realized. The experimental resolution is ∼8n m. An error within 2 µm is obtained experimentally within a range of 1 mm for 3DOF measurement. With a few optical components such as a beam splitter prism and beam expanders, the sensor shows potential in developing ultra-compact multi-DOF displacement measuring systems. Together with a nanometric resolution, the 3DOF displacement sensor has shown great potential in applications such as high-precision mechanical engineering and semiconductor processing.

Ancient nitrogenases are ATP dependent.

Life depends on a conserved set of chemical energy currencies that are relics of early biochemistry. One of these is ATP, a molecule that, when paired with a divalent metal ion such as Mg2+, can be hydrolyzed to support numerous cellular and molecular processes. Despite its centrality to extant biochemistry, it is unclear whether ATP supported the function of ancient enzymes. We investigate the evolutionary necessity of ATP by experimentally reconstructing an ancestral variant of the N2-reducing enzyme nitrogenase. The Proterozoic ancestor is predicted to be ~540-2,300 million years old, post-dating the Great Oxidation Event. Growth rates under nitrogen-fixing conditions are ~80% of those of wild type in Azotobacter vinelandii. In the extant enzyme, the hydrolysis of two MgATP is coupled to electron transfer to support substrate reduction. The ancestor has a strict requirement for ATP with no other nucleotide triphosphate analogs (GTP, ITP, and UTP) supporting activity. Alternative divalent metal ions (Fe2+, Co2+, and Mn2+) support activity with ATP but with diminished activities compared to Mg2+, similar to the extant enzyme. Additionally, it is shown that the ancestor has an identical efficiency in ATP hydrolyzed per electron transferred to the extant of two. Our results provide direct laboratory evidence of ATP usage by an ancient enzyme.IMPORTANCELife depends on energy-carrying molecules to power many sustaining processes. There is evidence that these molecules may predate the rise of life on Earth, but how and when these dependencies formed is unknown. The resurrection of ancient enzymes provides a unique tool to probe the enzyme's function and usage of energy-carrying molecules, shedding light on their biochemical origins. Through experimental reconstruction, this research investigates the ancestral dependence of a nitrogen-fixing enzyme on the energy carrier ATP, a requirement for function in the modern enzyme. We show that the resurrected ancestor does not have generalist nucleotide specificity. Rather, the ancestor has a strict requirement for ATP, like the modern enzyme, with similar function and efficiency. The findings elucidate the early-evolved necessity of energy-yielding molecules, delineating their role in ancient biochemical processes. Ultimately, these insights contribute to unraveling the intricate tapestry of evolutionary biology and the origins of life-sustaining dependencies.

Discovery of a novel pyrido[1,2-a]thiazolo[5,4-d]pyrimidinone derivatives with excellent potency against acetylcholinesterase.

As mimetic compounds of the natural alkaloid mackinazolinone, forty pyrido[1,2-a]thiazolo[5,4-d] pyrimidinone were designed and synthesized from a bioisosterism approach. The structure of these compounds was confirmed through analysis using 1H NMR, 13C NMR, and HRMS techniques. All the compounds were evaluated for their anticholinesterase activities and cytotoxicity on normal cells (293 T) by the Ellman method and methyl thiazolyl tetrazolium (MTT) method in vitro. and the structure-activity relationships (SARs) were summarized. The results showed that most of the compounds effectively inhibited acetylcholinesterase (AChE) in the micromolar range with weak cytotoxicity. Compound 7o exhibited the best inhibitory activity against AChE, displaying an IC50 values of 1.67 ± 0.09 µM and an inhibitory constant Ki of 11.31 µM as a competitive inhibitor to AChE. Molecular docking indicated that compound 7o may bind to AChE via hydrogen bond and π-π stacking. Further molecular dynamics (MD) simulations indicated a relatively low binding free energy (- 27.91 kJ·mol-1) of compound 7o with AChE. In summary, the collective findings suggested that 7o was promising as a potential novel drug candidate worthy of further investigation for the treatment of Alzheimer's disease.

In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events.

Objective. In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events.Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image.Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively.Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.

Establishing a river health evaluation index system for seven rivers in Jiamusi City of China.

The Jiamusi section of the Songhua River is one of the first 17 model river construction sections in China. The implementation of river health assessments can determine the health dynamics of rivers and test the management's effectiveness. Targeting seven rivers, this study conducted river zoning and monitoring point deployment to conduct sufficient field research and monitoring. The authors selected hydrological and water resources, physical structure, water quality, aquatic life, social service functions, and management as guideline layers and 15 indicator layers. Subsequently, the authors established an evaluation index system to evaluate and analyze the ecological status and social service status of each river. The results showed that the Yindamu, Alingda, and Gejie rivers scored well as healthy rivers, with health evaluation scores of 78.98, 76.06, and 75.83, respectively. The Wangsanwu, Lujiagang, and Lingdangmai rivers are generally sub-healthy rivers with scores of 71.55, 67.97, and 60.7, respectively. The Yinggetu River has a score of 54.52 and is therefore assessed as unhealthy. Based on the scientific evaluation index method, this study analyses the current river health state in Jiamusi City to provide the basis for the evaluation of the river chief's work and future river management.

The Therapeutic Effects of Baicalein on the Hepatopulmonary Syndrome in the Rat Model of Chronic Common Bile Duct Ligation.

Background and Aims: Hepatopulmonary syndrome (HPS) is characterized by arterial oxygenation defects due to pulmonary vascular dilation in liver disease. To date, liver transplantation remains the only effective treatment for HPS. This study aimed to explore the preventative role of baicalein in HPS development. Methods: Sixty male rats were randomly assigned to three groups: sham, common bile duct ligation (CBDL), and baicalein, receiving intraperitoneal injections of baicalein (40 mg·kg-1·d-1, diluted in saline) for 21 days. Survival rate, liver and kidney function, and bile acid metabolism levels were evaluated. Liver and lung angiogenesis and hepatic glycogen staining were assessed, and the expression of relevant proteins was evaluated by immunohistochemistry. Results: Baicalein improved survival rates and hypoxemia in rats post-CBDL, reducing angiogenic protein levels and enhancing glucose homeostasis. Compared to the untreated group, baicalein suppressed the expression of vascular endothelial growth factor, placental growth factors, matrix metalloprotease 9 and C-X-C motif chemokine 2, and it increased the expression of glycemic regulatory proteins, including dipeptidyl peptidase-4, sirtuin 1, peroxisome proliferator-activated receptor gamma co-activator 1α, and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3. Conclusion: Baicalein significantly improves hepatic function and hypoxia in HPS rats by attenuating pathological angiogenesis in the liver and lungs, showing promise as a treatment for HPS.

On the enigmatic jumping spider genus Ogdenia Peckham, 1908 (Araneae, Salticidae, Chrysillini).

The monotypic genus Ogdenia Peckham, 1908, is redefined based on the redescription of the holotype of O.mutilla (Peckham & Peckham, 1907), along with the newly discovered male specimens and intraspecific variation from China, Malaysia, Singapore, and Vietnam. Description, illustrations, and photographs are provided.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

Enhancing transmission type frame structures: A BBO algorithm-based integrated design approach.

The stable and site-specific operation of transmission lines is a crucial safeguard for grid functionality. This study introduces a comprehensive optimization design method for transmission line crossing frame structures based on the Biogeography-Based Optimization (BBO) algorithm, which integrates size, shape, and topology optimization. By utilizing the BBO algorithm to optimize the truss structure's design variables, the method ensures the structure's economic and practical viability while enhancing its performance. The optimization process is validated through finite element analysis, confirming the optimized structure's compliance with strength, stiffness, and stability requirements. The results demonstrate that the integrated design of size, shape, and topology optimization, as opposed to individual optimizations of size or shape and topology, yields the lightest structure mass and a maximum stress of 151.4 MPa under construction conditions. These findings also satisfy the criteria for strength, stiffness, and stability, verifying the method's feasibility, effectiveness, and practicality. This approach surpasses traditional optimization methods, offering a more effective solution for complex structural optimization challenges, thereby enhancing the sustainable utilization of structures.

A back propagation neural network based respiratory motion modelling method.

BACKGROUND: This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases. METHODS: Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement. RESULTS: The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm. CONCLUSIONS: The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.

Mixed-size spot scanning with a compact large momentum acceptance superconducting (LMA-SC) gantry beamline for proton therapy.

Objective. Lowering treatment costs and improving treatment quality are two primary goals for next-generation proton therapy (PT) facilities. This work will design a compact large momentum acceptance superconducting (LMA-SC) gantry beamline to reduce the footprint and expense of the PT facilities, with a novel mixed-size spot scanning method to improve the sparing of organs at risk (OAR).Approach. For the LMA-SC gantry beamline, the movable energy slit is placed in the middle of the last achromatic bending section, and the beam momentum spread of delivered spots can be easily changed during the treatment. Simultaneously, changing the collimator size can provide spots with various lateral spot sizes. Based on the provided large-size and small-size spot models, the treatment planning with mixed spot scanning is optimized: the interior of the target is irradiated with large-size spots (to cover the uniform-dose interior efficiently), while the peripheral of the target is irradiated with small-size spots (to shape the sharp dose falloff at the peripheral accurately).Main results. The treatment plan with mixed-size spot scanning was evaluated and compared with small and large-size spot scanning for thirteen clinical prostate cases. The mixed-size spot plan had superior target dose homogeneities, better protection of OAR, and better plan robustness than the large-size spot plan. Compared to the small-size spot plan, the mixed-size spot plan had comparable plan quality, better plan robustness, and reduced plan delivery time from 65.9 to 40.0 s.Significance. The compact LMA-SC gantry beamline is proposed with mixed-size spot scanning, with demonstrated footprint reduction and improved plan quality compared to the conventional spot scanning method.

Elevated circulating BMP9 aggravates pulmonary angiogenesis in hepatopulmonary syndrome rats through ALK1-Endoglin-Smad1/5/9 signalling.

BACKGROUND: Bone morphogenetic protein 9 (BMP9) is a hepatokine that plays a pivotal role in the progression of liver diseases. Moreover, an increasing number of studies have shown that BMP9 is associated with hepatopulmonary syndrome (HPS), but its role in HPS is unclear. Here, we evaluated the influence of CBDL on BMP9 expression and investigated potential mechanisms of BMP9 signalling in HPS. METHODS: We profiled the circulating BMP9 levels in common bile duct ligation-induced HPS rat model, and then investigated the effects and mechanisms of HPS rat serum on pulmonary vascular endothelial dysfunction in rat model, as well as in primarily cultured rat pulmonary microvascular endothelial cells. RESULTS: Our data revealed that circulating BMP9 levels were significantly increased in the HPS rats compared to control group. Besides, the elevated BMP9 in HPS rat serum was not only crucial for promoting endothelial cell proliferation and tube formation through the activin receptor-like kinase1 (ALK1)-Endoglin-Smad1/5/9 pathway, but also important for accumulation of monocytes. Treatments with ALK1-Fc or silencing ALK1 expression to inhibit the BMP9 signalling pathway effectively eliminated these effects. In agreement with these observations, increased circulating BMP9 was associated with an increase in lung vessel density and accumulation of pro-angiogenic monocytes in the microvasculature in HPS rats. CONCLUSIONS: This study provided evidence that elevated circulating BMP9, secreted from the liver, promote pulmonary angiogenesis in HPS rats via ALK1-Endoglin-Smad1/5/9 pathway. In addition, BMP9-regulated pathways are also involved in accumulation of pro-angiogenic monocytes in the pulmonary microvasculature in HPS rats.

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2 mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.

Single-double-band switchable optical circular polarizers based on surface plasmon resonance: publisher's note.

This publisher's note reports a correction in Appl. Opt.63, 1153 (2024)APOPAI0003-693510.1364/AO.513837.

Visible-light-excited robust room-temperature phosphorescence of dimeric single-component luminophores in the amorphous state.

Organic room temperature phosphorescence (RTP) has significant potential in various applications of information storage, anti-counterfeiting, and bio-imaging. However, achieving robust organic RTP emission of the single-component system is challenging to overcome the restriction of the crystalline state or other rigid environments with cautious treatment. Herein, we report a single-component system with robust persistent RTP emission in various aggregated forms, such as crystal, fine powder, and even amorphous states. Our experimental data reveal that the vigorous RTP emissions rely on their tight dimers based on strong and large-overlap π-π interactions between polycyclic aromatic hydrocarbon (PAH) groups. The dimer structure can offer not only excitons in low energy levels for visible-light excited red long-lived RTP but also suppression of the nonradiative decays even in an amorphous state for good resistance of RTP to heat (up to 70 °C) or water. Furthermore, we demonstrate the water-dispersible nanoparticle with persistent RTP over 600 nm and a lifetime of 0.22 s for visible-light excited cellular and in-vivo imaging, prepared through the common microemulsion approach without overcaution for nanocrystal formation.