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Bin Ibrahim MZ, Wang Z, Sajikumar S. Synapses tagged, memories kept: synaptic tagging and capture hypothesis in brain health and disease. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230237. [PMID: 38853570 DOI: 10.1098/rstb.2023.0237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/13/2024] [Indexed: 06/11/2024] Open
Abstract
The synaptic tagging and capture (STC) hypothesis lays the framework on the synapse-specific mechanism of protein synthesis-dependent long-term plasticity upon synaptic induction. Activated synapses will display a transient tag that will capture plasticity-related products (PRPs). These two events, tag setting and PRP synthesis, can be teased apart and have been studied extensively-from their electrophysiological and pharmacological properties to the molecular events involved. Consequently, the hypothesis also permits interactions of synaptic populations that encode different memories within the same neuronal population-hence, it gives rise to the associativity of plasticity. In this review, the recent advances and progress since the experimental debut of the STC hypothesis will be shared. This includes the role of neuromodulation in PRP synthesis and tag integrity, behavioural correlates of the hypothesis and modelling in silico. STC, as a more sensitive assay for synaptic health, can also assess neuronal aberrations. We will also expound how synaptic plasticity and associativity are altered in ageing-related decline and pathological conditions such as juvenile stress, cancer, sleep deprivation and Alzheimer's disease. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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Huang H, Fan Y, Yan F, Hu Y, He H, Xu T, Zhu X, Zhu Y, Diao W, Xia X, Tu J, Li A, Lin B, Liu Q, Lu Z, Xi T, Wang W, Xu D, Chen Z, Wang Z, Chen X, Shan G. Diabetes and long duration leading to speech-, low/mid-, and high- frequency hearing loss: current evidence from the China National Health Survey 2023. J Endocrinol Invest 2024:10.1007/s40618-024-02406-2. [PMID: 38869778 DOI: 10.1007/s40618-024-02406-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/29/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE To examine the effect of diabetes, duration of diabetes, and blood glucose on speech-, low/mid-, and high-frequency hearing loss. METHODS In this cross-sectional study, 2821 participants aged 20-87 years in the China National Health Survey were included. Diabetes was defined as valid fasting blood glucose (FBG) of ≥ 7.0 mmol/L, a self-reported history of diabetes or the use of anti-diabetic medications. Speech-(500, 1000, 2000, and 4000 Hz), low/mid- (500, 1000 and 2000 Hz), and high-frequency (4000, 6000, and 8000 Hz) hearing loss was defined as pure tone average of responding frequencies > 20 dB HL in the better ear, respectively. RESULTS In fully adjusted models, for speech-, low/mid-, and high-frequency hearing loss, compared with no diabetes, those with diabetes (OR[95%CI]: 1.44 [1.12, 1.86], 1.23 [0.94, 1.61], and 1.75 [1.28, 2.41], respectively) and with diabetes for > 5 years duration (OR[95%CI]: 1.63 [1.09, 2.42], and 1.63 [1.12, 2.36], 2.15 [1.25, 3.70], respectively) were at higher risk. High FBG level was associated with a higher risk of speech-, low/ mid-, and high-frequency hearing loss. And there were stronger associations between HL and diabetes, longer duration and higher in "healthier population" (no hypertension, no dyslipidemia and younger age). CONCLUSION Diabetes, longer duration, and higher FBG level were independently associated with hearing loss for speech-, low/mid- and high-frequency hearing loss, particularly in higher frequency and "healthier population". Paying more attention to hearing loss in those populations could lower the burden of hearing loss.
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Shi XH, Wang SX, Wang Z, Wang J, Zhang ZH, Liu YP, Zhang HY, Gao HW, Zhou XY, Rao Q, Liang L, Yao XH, Liu DG, Liang ZY. [Scientific, transparent and applicable rankings of Chinese pathological guidelines and consensus published in the medical journals in 2022]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2024; 53:528-534. [PMID: 38825895 DOI: 10.3760/cma.j.cn112151-20231105-00333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The STAR tool was used to evaluate and analyze the science, transparency, and applicability of Chinese pathology guidelines and consensus published in medical journals in 2022. There were a total of 18 pathology guidelines and consensuses published in 2022, including 1 guideline and 17 consensuses. The results showed that the guideline score was 21.83 points, lower than the overall guideline average (43.4 points). Consensus ratings scored an average of 27.87 points, on par with the overall consensus level (28.3 points). Areas that scored above the overall level were "conflict of interest" and "working groups", while areas that scored below the overall level were "proposals", "funding", "evidence", "consensus approaches" and "accessibility". To sum up, the formulation of pathology guidelines and consensuses in 2022 is not standardized, and the evidence retrieval process, evidence evaluation methods and grading criteria for recommendations on clinical issues are not provided in the formulation process; the process and method for reaching consensus are not provided, the plan is lacking, and registration is not carried out. It is therefore suggested that guidelines/consensus makers in the field of pathology should attach importance to evidence-based medical evidence, strictly follow guideline formulation methods and processes, further improve the scientific, applicable and transparent guidelines/consensuses in the field, and better provide support for clinicians and patients.
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Wang Z, Burgers JS, Shao R, Bian Z, Wang C, Chen Y, Estill J. Strengthening transparency in randomised trials related to multimorbidity: key points and recommendations to guide reporting. BMJ Evid Based Med 2024:bmjebm-2024-112907. [PMID: 38839262 DOI: 10.1136/bmjebm-2024-112907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
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Hu L, Zhang H, Huang C, Shen T, Feng Z, Mu F, Xu L, Lin Y, Yue C, Guo K, Tian M, Shi J, Zhang C, Wen P, Cao S, Wang Y, Zhang J, Shi X, Wang Z, He Y, Zhang X, Liu X, Lv Y, Liu Z, Guo W, Wang B. Effect of ursodeoxycholic acid on preventing SARS-CoV-2 infection in patients with liver transplantation: a multicenter retrospective cohort study. QJM 2024; 117:339-347. [PMID: 37950449 DOI: 10.1093/qjmed/hcad254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/06/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Immunosuppressed recipients of liver transplantation (LT) are more likely to develop coronavirus disease 2019 (COVID-19) and may have an increased risk of developing worse outcomes. AIM To assess the effect of ursodeoxycholic acid (UDCA) on preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in LT recipients. DESIGN Adult patients (aged ≥ 18 years) who underwent LT between 1 January 2015 and 31 December 2022 were included and categorized into two groups according to their use of UDCA. METHODS The prevalence and severity of COVID-19 among transplantation patients between the UDCA and non-UDCA groups were estimated and compared. RESULTS Among the 897 LT patients who met the inclusion criteria, infection rate of SARS-CoV-2 was 78.4%, and the rate of severe illness was 5.1% from January 2022 to January 2023 in China. In the multivariate analysis, only UDCA treatment (P = 0.006) was found to be a protective factor against SARS-CoV-2 infection. After propensity score matching, the SARS-CoV-2 infection rate in the UDCA group was lower than that in the non-UDCA group (74.1% vs. 84.6%, P = 0.002). This rate was further reduced to 62.1% (P = 0.002) when the oral administration dose was >15 mg/kg/day. There was no difference in the rates of severe COVID-19 illness, ICU admission, or ventilation rate or length of hospital stay with or without UDCA treatment (all P > 0.05). CONCLUSIONS The use of UDCA in LT patients significantly reduced the SARS-CoV-2 infection rate and showed a dose-dependent protective effect.
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Zhou J, Liu C, Amornphimoltham P, Cheong SC, Gutkind JS, Chen Q, Wang Z. Mouse Models for Head and Neck Squamous Cell Carcinoma. J Dent Res 2024; 103:585-595. [PMID: 38722077 DOI: 10.1177/00220345241240997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024] Open
Abstract
The prognosis and survival rate of head and neck squamous cell carcinoma (HNSCC) have remained unchanged for years, and the pathogenesis of HNSCC is still not fully understood, necessitating further research. An ideal animal model that accurately replicates the complex microenvironment of HNSCC is urgently needed. Among all the animal models for preclinical cancer research, tumor-bearing mouse models are the best known and widely used due to their high similarity to humans. Currently, mouse models for HNSCC can be broadly categorized into chemical-induced models, genetically engineered mouse models (GEMMs), and transplanted mouse models, each with its distinct advantages and limitations. In chemical-induced models, the carcinogen spontaneously initiates tumor formation through a multistep process. The resemblance of this model to human carcinogenesis renders it an ideal preclinical platform for studying HNSCC initiation and progression from precancerous lesions. The major drawback is that these models are time-consuming and, like human cancer, unpredictable in terms of timing, location, and number of lesions. GEMMs involve transgenic and knockout mice with gene modifications, leading to malignant transformation within a tumor microenvironment that recapitulates tumorigenesis in vivo, including their interaction with the immune system. However, most HNSCC GEMMs exhibit low tumor incidence and limited prognostic significance when translated to clinical studies. Transplanted mouse models are the most widely used in cancer research due to their consistency, availability, and efficiency. Based on the donor and recipient species matching, transplanted mouse models can be divided into xenografts and syngeneic models. In the latter, transplanted cells and host are from the same strain, making syngeneic models relevant to study functional immune system. In this review, we provide a comprehensive summary of the characteristics, establishment methods, and potential applications of these different HNSCC mouse models, aiming to assist researchers in choosing suitable animal models for their research.
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Li T, Du Y, Yao H, Zhao B, Wang Z, Chen R, Ji Y, Du M. Isobavachin attenuates osteoclastogenesis and periodontitis-induced bone loss by inhibiting cellular iron accumulation and mitochondrial biogenesis. Biochem Pharmacol 2024; 224:116202. [PMID: 38615917 DOI: 10.1016/j.bcp.2024.116202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/24/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
As bone-resorbing cells rich in mitochondria, osteoclasts require high iron uptake to promote mitochondrial biogenesis and maintain a high-energy metabolic state for active bone resorption. Given that abnormal osteoclast formation and activation leads to imbalanced bone remodeling and osteolytic bone loss, osteoclasts may be crucial targets for treating osteolytic diseases such as periodontitis. Isobavachin (IBA), a natural flavonoid compound, has been confirmed to be an inhibitor of receptor activator of nuclear factor κB ligand (RANKL)-induced osteoclast differentiation from bone marrow-derived macrophages (BMMs). However, its effects on periodontitis-induced bone loss and the potential mechanism of its anti-osteoclastogenesis effect remain unclear. Our study demonstrated that IBA suppressed RANKL-induced osteoclastogenesis in BMMs and RAW264.7 cells and inhibited osteoclast-mediated bone resorption in vitro. Transcriptomic analysis indicated that iron homeostasis and reactive oxygen species (ROS) metabolic process were enriched among the differentially expressed genes following IBA treatment. IBA exerted its anti-osteoclastogenesis effect by inhibiting iron accumulation in osteoclasts. Mechanistically, IBA attenuated iron accumulation in RANKL-induced osteoclasts by inhibiting the mitogen-activated protein kinase (MAPK) pathway to upregulate ferroportin1 (Fpn1) expression and promote Fpn1-mediated intracellular iron efflux. We also found that IBA inhibited mitochondrial biogenesis and function, and reduced RANKL-induced ROS generation in osteoclasts. Furthermore, IBA attenuated periodontitis-induced bone loss by reducing osteoclastogenesis in vivo. Overall, these results suggest that IBA may serve as a promising therapeutic strategy for bone diseases characterized by osteoclastic bone resorption.
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Wang Z, Han K, Liu W, Wang Z, Shi C, Liu X, Huang M, Sun G, Liu S, Guo Q. Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1160-1176. [PMID: 38326533 DOI: 10.1007/s10278-024-01001-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 02/09/2024]
Abstract
In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.
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Wang C, Wang S, Wang Z, Han J, Jiang N, Qu L, Xu K. Andrographolide regulates H3 histone lactylation by interfering with p300 to alleviate aortic valve calcification. Br J Pharmacol 2024; 181:1843-1856. [PMID: 38378175 DOI: 10.1111/bph.16332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND AND PURPOSE Our previous studies have found that andrographolide (AGP) alleviates calcific aortic valve disease (CAVD), but the underlying mechanism is unclear. This study explores the molecular target and signal mechanisms of AGP in inhibiting CAVD. EXPERIMENTAL APPROACH The anti-calcification effects of the aortic valve with AGP treatment were evaluated by alizarin red staining in vitro and ultrasound and histopathological assessment of a high-fat (HF)-fed ApoE-/- mouse valve calcification model. A correlation between the H3 histone lactylation (H3Kla) and calcification was detected. Molecular docking and surface plasmon resonance (SPR) experiments were further used to confirm p300 as a target for AGP. Overexpression (oe) and silencing (si) of p300 were used to verify the inhibitory effect of AGP targeting p300 on the H3Kla in vitro and ex vivo. KEY RESULTS AGP significantly inhibited calcium deposition in valve interstitial cells (VICs) and ameliorated aortic valve calcification. The multi-omics analysis revealed the glycolysis pathway involved in CAVD, indicating that AGP interfered with lactate production by regulating lactate dehydrogenase A (LDHA). In addition, lactylation, a new post-translational modification, was shown to have a role in promoting aortic valve calcification. Furthermore, H3Kla and H3K9la site were shown to correlate with Runx2 expression inhibition by AGP treatment. Importantly, we found that p300 transferase was the molecular target of AGP in inhibiting H3Kla. CONCLUSIONS AND IMPLICATIONS Our findings, for the first time, demonstrated that AGP alleviates calcification by interfering with H3Kla via p300, which might be a powerful drug to prevent CAVD.
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Wang F, Wang H, Zhang H, Sun B, Wang Z. A Novel Mechanism of MSCs Responding to Occlusal Force for Bone Homeostasis. J Dent Res 2024; 103:642-651. [PMID: 38665065 DOI: 10.1177/00220345241236120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024] Open
Abstract
Alveolar bone, as tooth-supporting bone for mastication, is sensitive to occlusal force. However, the mechanism of alveolar bone loss after losing occlusal force remains unclear. Here, we performed single-cell RNA sequencing of nonhematopoietic (CD45-) cells in mouse alveolar bone after removing the occlusal force. Mesenchymal stromal cells (MSCs) and endothelial cell (EC) subsets were significantly decreased in frequency, as confirmed by immunofluorescence and flow cytometry. The osteogenic and proangiogenic abilities of MSCs were impaired, and the expression of mechanotransducers yes associated protein 1 (Yap) and WW domain containing transcription regulator 1 (Taz) in MSCs decreased. Conditional deletion of Yap and Taz from LepR+ cells, which are enriched in MSCs that are important for adult bone homeostasis, significantly decreased alveolar bone mass and resisted any further changes in bone mass induced by occlusal force changes. Interestingly, LepR-Cre; Yapf/f; Tazf/f mice showed a decrease in CD31hi endomucin (Emcn)hi endothelium, and the expression of some EC-derived signals acting on osteoblastic cells was inhibited in alveolar bone. Mechanistically, conditional deletion of Yap and Taz in LepR+ cells inhibited the secretion of pleiotrophin (Ptn), which impaired the proangiogenic capacity of LepR+ cells. Knockdown in MSC-derived Ptn repressed human umbilical vein EC tube formation in vitro. More important, administration of recombinant PTN locally recovered the frequency of CD31hiEmcnhi endothelium and rescued the low bone mass phenotype of LepR-Cre; Yapf/f; Tazf/f mice. Taken together, these findings suggest that occlusal force governs MSC-regulated endothelium to maintain alveolar bone homeostasis through the Yap/Taz/Ptn axis, providing a reference for further understanding of the relationship between dysfunction and bone homeostasis.
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Wang Z, Zhang J, Sun X, Yu J, Liu B, Peng B, Wang L, Yang J, Zhu L. Nanoparticulate bioceramic putty suppresses osteoclastogenesis and inflammatory bone loss in mice via inhibition of TRAF6-mediated signalling pathways: A laboratory investigation. Int Endod J 2024; 57:682-699. [PMID: 38403990 DOI: 10.1111/iej.14051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/27/2024]
Abstract
AIM This study aimed to determine the effects of iRoot BP Plus on receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis in vitro and inflammation-mediated bone resorption in vivo and investigated the underlying molecular mechanisms. METHODOLOGY CCK-8 was performed to test cell viability in RANKL-induced RAW 264.7 cells and BMDMs in response to iRoot BP Plus. The effect of iRoot BP Plus on osteoclastogenesis was determined using TRAP staining and phalloidin staining, respectively. Pit formation assay was conducted to measure osteoclast resorptive capacity. Western blot and qPCR were performed to examine osteoclast-related proteins and gene expression, respectively. Western blot was also used to investigate the signalling pathways involved. For in vivo experiments, an LPS-induced mouse calvarial bone resorption model was established to analyse the effect of iRoot BP Plus on bone resorption (n = 6 per group). At 7 days, mouse calvaria were collected and prepared for histological analysis. RESULTS We identified that iRoot BP Plus extracts significantly attenuated RANKL-induced osteoclastogenesis, reduced sealing zone formation, restrained osteolytic capacity and decreased osteoclast-specific gene expression (p < .01). Mechanistically, iRoot BP Plus extracts reduced TRAF6 via proteasomal degradation, then suppressed the phosphorylation of mitogen-activated protein kinases (MAPKs), blocked the nuclear translocation of c-Fos and diminished nuclear factor-κB (NF-κB) p65 and NFATc1 accumulation. Consistent with the in vitro results, iRoot BP Plus extracts attenuated osteoclast activity thus protecting against inflammatory bone resorption in vivo (p < .05), which was accompanied by a suppression of TRAF6, c-Fos, NFATc1 and cathepsin K expression. CONCLUSION These findings provide valuable insights into the signalling mechanisms underlying nanoparticulate bioceramic putty-mediated bone homeostasis.
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Wang S, Han J, Wang Z, Liu X, Wang C, Nisar MF, Pan L, Xu K. Targeted Therapy of Tumors and Cancer Stem Cells based on Oxidant-Regulated Redox Pathway and its Mechanism. Curr Comput Aided Drug Des 2024; 20:CAD-EPUB-140719. [PMID: 38818918 DOI: 10.2174/0115734099299174240522115944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/18/2024] [Accepted: 03/28/2024] [Indexed: 06/01/2024]
Abstract
A malignant tumor is a frequent and common disease that severely threatens human health. Many mechanisms, such as cell signaling pathway, anti-apoptosis mechanism, cell stemness, metabolism, and cell phenotype, have been studied to explain the reasons for chemotherapy, radioresistance, and tumor recurrences in antitumor treatment. Cancer stem cells (CSCs) are important tumor cell subclasses that can potentially organize and regulate stem cell properties. Growing evidence suggests that CSCs can initiate tumors and constitute a significant factor in metastasis, recurrence, and treatment resistance. The inability to completely target and remove CSCs is a considerable obstacle in tumor treatment. Therefore, drugs and therapeutic strategies that can effectively intervene with CSCs are essential for the treatment of different tumor types. However, the current strategies and efficacy of targeted elimination of CSCs are very limited. Oxidative stress has been recognized to play a crucial role in cancer pathophysiology. Moreover, reactive oxygen species (ROS) production and imbalance of the built-in cellular antioxidant defense system are hallmarks of tumor and cancer etiology. The current paper will focus on the regulation and mechanism behind oxidative stress in tumors and cancer stem cells and its tumor therapy applications. Additionally, the article discusses the role of CSCs in causing tumor treatment resistance and recurrence based on a redox perspective. The study also emphasizes that targeted modulation of oxidative stress in CSCs has great potential in tumor therapy, providing novel prospects for tumor therapy.
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Luo X, Chen F, Zhu D, Wang L, Wang Z, Liu H, Lyu M, Wang Y, Wang Q, Chen Y. Potential roles of large language models in production of systematic reviews and meta-analyses. J Med Internet Res 2024. [PMID: 38819655 DOI: 10.2196/56780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
UNSTRUCTURED Large language models (LLMs) like ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be employed to expedite various steps, including defining clinical questions, literature search, document screening, information extraction, and language refinement, etc, thereby conserving resources and enhancing efficiency. However, when utilizing LLMs, attention should be given to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. This article reviews the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors involved in systematic reviews and meta-analyses.
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Li Q, Wang Z, Liu N, Zhang H, Li Z. Research on process parameter optimization of irregular coal face. Sci Rep 2024; 14:12086. [PMID: 38802527 PMCID: PMC11130247 DOI: 10.1038/s41598-024-62517-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
Based on the engineering background of 1353 working face in Daizhuang Coalmine, the paper identifies three primary issues with the working face mining process: conventional pressure relief means are limited, risk of impact and the length of working face changes, It also proposes comprehensive control measures to improve the blasting roof cutting scheme, optimize mining speed and the location of the stopping line. The three improvement measures mentioned above are simulated numerically, and the effects of the drilling and blasting plan, mining speed, and stopping line location on stress distribution are determined. The results show that by implementing the three improvement measures, the stress variation interval can be efficiently controlled and the working face's production safety can be increased. Finally, it is determined that the 1353 working face of Daizhuang Coalmine adopts the pressure relief method of drilling on one side and cutting the roof 20 m deep on the other side, and the mining speed of the working face is 3 m/day and the length of the stopping line is 85 m. Based on the on-site monitoring results, the implementation of comprehensive treatment measures can effectively improve the surrounding rock state of 1353 working face, which has certain guiding significance for the mining of irregular working face.
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Adachi I, Adamczyk K, Aggarwal L, Ahmed H, Aihara H, Akopov N, Aloisio A, Asner DM, Atmacan H, Aushev T, Aushev V, Aversano M, Babu V, Bahinipati S, Bambade P, Banerjee S, Bansal S, Barrett M, Baudot J, Baur A, Beaubien A, Becherer F, Becker J, Bennett JV, Bernlochner FU, Bertacchi V, Bertemes M, Bertholet E, Bessner M, Bettarini S, Bhuyan B, Bianchi F, Bilka T, Bilokin S, Biswas D, Bobrov A, Bodrov D, Bolz A, Bondar A, Bozek A, Bračko M, Branchini P, Briere RA, Browder TE, Budano A, Bussino S, Campajola M, Cao L, Casarosa G, Cecchi C, Cerasoli J, Chang MC, Chang P, Cheema P, Cheon BG, Chilikin K, Chirapatpimol K, Cho HE, Cho K, Cho SJ, Choi SK, Choudhury S, Corona L, Cremaldi LM, Dattola F, De La Cruz-Burelo E, De La Motte SA, de Marino G, De Nardo G, De Nuccio M, De Pietro G, de Sangro R, Destefanis M, Dhamija R, Di Canto A, Di Capua F, Dingfelder J, Doležal Z, Dong TV, Dorigo M, Dort K, Dreyer S, Dubey S, Dujany G, Ecker P, Eliachevitch M, Epifanov D, Feichtinger P, Ferber T, Ferlewicz D, Fillinger T, Finocchiaro G, Fodor A, Forti F, Frey A, Fulsom BG, Ganiev E, Garcia-Hernandez M, Garg R, Gaudino G, Gaur V, Gaz A, Gellrich A, Ghevondyan G, Ghosh D, Ghumaryan H, Giakoustidis G, Giordano R, Giri A, Gobbo B, Godang R, Gogota O, Goldenzweig P, Gradl W, Grammatico T, Granderath S, Graziani E, Greenwald D, Gruberová Z, Gu T, Gudkova K, Halder S, Han Y, Hara T, Hayashii H, Hazra S, Hearty C, Hedges MT, Heidelbach A, Heredia de la Cruz I, Hernández Villanueva M, Higuchi T, Hoek M, Hohmann M, Horak P, Hsu CL, Humair T, Iijima T, Ipsita N, Ishikawa A, Itoh R, Iwasaki M, Jackson P, Jacobs WW, Jaffe DE, Jang EJ, Jia S, Jin Y, Joo KK, Junkerkalefeld H, Kalita D, Kaliyar AB, Kandra J, Kang KH, Karyan G, Kawasaki T, Keil F, Kiesling C, Kim CH, Kim DY, Kim KH, Kim YK, Kinoshita K, Kodyš P, Koga T, Kohani S, Kojima K, Korobov A, Korpar S, Kovalenko E, Kowalewski R, Kraetzschmar TMG, Križan P, Krokovny P, Kuhr T, Kulii Y, Kumar M, Kumara K, Kunigo T, Kuzmin A, Kwon YJ, Lacaprara S, Lam T, Lanceri L, Lange JS, Laurenza M, Leboucher R, Le Diberder FR, Lee MJ, Levit D, Lewis PM, Li LK, Li Y, Li YB, Libby J, Liu M, Liu QY, Liu ZQ, Liventsev D, Longo S, Lueck T, Luo T, Lyu C, Ma Y, Maggiora M, Maharana SP, Maiti R, Maity S, Mancinelli G, Manfredi R, Manoni E, Manthei AC, Mantovano M, Marcantonio D, Marcello S, Marinas C, Martel L, Martellini C, Martinov T, Massaccesi L, Masuda M, Matsuda T, Matsuoka K, Matvienko D, Maurya SK, McKenna JA, Mehta R, Meier F, Merola M, Metzner F, Milesi M, Miller C, Mirra M, Miyabayashi K, Miyake H, Mizuk R, Mohanty GB, Molina-Gonzalez N, Mondal S, Moneta S, Moser HG, Mrvar M, Mussa R, Nakamura I, Nakao M, Nakazawa Y, Narimani Charan A, Naruki M, Narwal D, Natkaniec Z, Natochii A, Nayak L, Nayak M, Nazaryan G, Niebuhr C, Nishida S, Ogawa S, Ono H, Onuki Y, Oskin P, Otani F, Pakhlov P, Pakhlova G, Panta A, Pardi S, Park H, Park SH, Paschen B, Passeri A, Patra S, Paul S, Pedlar TK, Peschke R, Pestotnik R, Piccolo M, Piilonen LE, Podesta-Lerma PLM, Podobnik T, Pokharel S, Praz C, Prell S, Prencipe E, Prim MT, Purwar H, Rados P, Raeuber G, Raiz S, Rauls N, Reif M, Reiter S, Ripp-Baudot I, Rizzo G, Robertson SH, Rocchetti P, Roehrken M, Roney JM, Rostomyan A, Rout N, Russo G, Sakai Y, Sanders DA, Sandilya S, Santelj L, Sato Y, Savinov V, Scavino B, Schmitt C, Schwanda C, Seino Y, Selce A, Senyo K, Serrano J, Sevior ME, Sfienti C, Shan W, Shen CP, Shi XD, Shillington T, Shimasaki T, Shiu JG, Shtol D, Sibidanov A, Simon F, Singh JB, Skorupa J, Sobie RJ, Sobotzik M, Soffer A, Sokolov A, Solovieva E, Spataro S, Spruck B, Starič M, Stavroulakis P, Stefkova S, Stroili R, Sumihama M, Sumisawa K, Svidras H, Takizawa M, Tamponi U, Tanaka S, Tanida K, Tenchini F, Tittel O, Tiwary R, Tonelli D, Torassa E, Trabelsi K, Tsaklidis I, Uchida M, Ueda I, Uematsu Y, Uglov T, Unger K, Unno Y, Uno K, Uno S, Urquijo P, Ushiroda Y, Vahsen SE, van Tonder R, Varvell KE, Veronesi M, Vinokurova A, Vismaya VS, Vitale L, Volpe R, Wach B, Wakai M, Wallner S, Wang MZ, Wang XL, Wang Z, Warburton A, Watanuki S, Wessel C, Won E, Xu XP, Yabsley BD, Yamada S, Yang SB, Yelton J, Yin JH, Yoshihara K, Yuan CZ, Zhang B, Zhang Y, Zhilich V, Zhou QD, Zhou XY, Zhukova VI. First Measurement of R(X_{τ/ℓ}) as an Inclusive Test of the b→cτν Anomaly. PHYSICAL REVIEW LETTERS 2024; 132:211804. [PMID: 38856254 DOI: 10.1103/physrevlett.132.211804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/19/2024] [Indexed: 06/11/2024]
Abstract
We measure the tau-to-light-lepton ratio of inclusive B-meson branching fractions R(X_{τ/ℓ})≡B(B→Xτν)/B(B→Xℓν), where ℓ indicates an electron or muon, and thereby test the universality of charged-current weak interactions. We select events that have one fully reconstructed B meson and a charged lepton candidate from 189 fb^{-1} of electron-positron collision data collected with the Belle II detector. We find R(X_{τ/ℓ})=0.228±0.016(stat)±0.036(syst), in agreement with standard-model expectations. This is the first direct measurement of R(X_{τ/ℓ}).
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Wang Z, Wang Z, Wang S, Zhang YQ, Yu XL. [Application and progress of ablation therapy for large liver cancer]. ZHONGHUA GAN ZANG BING ZA ZHI = ZHONGHUA GANZANGBING ZAZHI = CHINESE JOURNAL OF HEPATOLOGY 2024; 32:469-473. [PMID: 38858197 DOI: 10.3760/cma.j.cn501113-20230926-00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Primary liver cancer is one of the most common malignant tumors. A liver tumor is defined as a large cancer when its diameter is ≥5 cm. Resection surgical therapy can be performed only on a small portion of large cancers because of its own features. As a result, non-resection surgical therapy has become a hot and difficult issue of widespread concern. In recent years, with the development of ablation technology, research on the use of ablation alone and ablation combined with other modalities for the treatment of large liver cancer has continued to deepen, and good clinical results have been achieved. Although there are many reports on ablation treatment for large liver cancer, there are currently no standardized treatment guidelines, and there are still controversies about treatment strategies. This article reviews the development of ablation therapy, the current status of single and combined ablation therapy, the prevention of related complications, and other aspects of large liver cancer.
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Wu KY, Ren YX, Ruan YM, Ma JL, Li CX, Wang Z. [The effect of the AIM2 inflammasome in noise-induced cognitive dysfunction in rats]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:332-339. [PMID: 38802305 DOI: 10.3760/cma.j.cn121094-20230915-00157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective: To explore the effect of the absent in melanoma 2 (AIM2) -mediated neuroinflammation in noise-induced cognitive dysfunction in rats. Methods: In April 2023, sixteen male Wistar rats were randomly divided into control group and noise group, with 8 rats in each group. The rats in the noise group were placed in 50 cm×50 cm×40 cm transparent boxes and exposed to 100 dB (A) white noise with a sound pressure level of 100 dB (A) (4 h/d for 30 d) . At the same time, rats in the control group were kept in similar boxes with environmental noise less than 60 dB (A) . After 30 days of noise exposure, the Morris water maze experiment was applied to test the learning and memory abilities of the rats; the pathological morphology of hippocampal tissues was observed by Hematoxylin-Eosin (HE) staining. Western blot was used to detect the protein expression levels of AIM2, cysteinyl aspartate specific proteinase-1 (caspase-1) , apoptosis-associated speck-like protein (ASC) , interleukin-1β (IL-1β) , IL-18, ionic calcium-binding articulation molecule-1 (Iba-1) , and glial fibrillary acidic protein (GFAP) . The expression of both Iba-1 and GFAP in hippocampal tissue was assessed by immunohistochemical staining. The co-localization of AIM2 with Iba-1 or GFAP was determined by immunofluorescence double staining. Results: Compared with the control group, the escape latency of rats in the noise group was increased by 16.29 s, 17.71 s, and 20.26 s on days 3, 4, and 5, respectively. On day 6, the noise-exposed rats spent shorter time in the target quadrant and had fewer times in crossing the platform[ (7.25±2.27) s and (1.13±0.64) times] than the control group[ (15.64±3.99) s and (4.25±2.12) times] (P<0.05) . After noise exposure, hippocampal neurons of rats displayed marked nuclear hyperchromatic and pyknosis phenomenon. The noise-exposed rats had higher numbers of both microglia and astrocytes (27.00±2.65 and 43.33±5.51) in the DG area of the hippocampus relative to the control group (14.67±3.06 and 20.00±4.58) (P<0.05) . Moreover, the glial cells in the noise group had larger cell cytosol with more and thicker branches. The protein expression levels of inflammatory cytokines Cleaved-IL-1β and Cleaved-IL-18 in the hippocampus of rats in the noise group (1.55±0.19 and 1.74±0.12) were significantly higher than the control group (1.00±0.11 and 1.00±0.13) (P<0.05) . After noise exposure, the protein expression levels of AIM2, Cleaved-Caspase-1 and ASC (1.19±0.09, 1.34±0.07 and 1.14±0.01) were higher than the control group (1.00±0.07, 1.00±0.14 and 1.00±0.06) and differences between the two groups were statistically significant (P<0.05) . A significant increase in the number of cells co-localizing AIM2 with Iba-1 or GFAP in the noise group (28.67±4.04 and 40.67±5.13) compared with the control group (15.67±4.04 and 17.67±3.79) , and statistically significant differences were observed between the two groups (P<0.05) . Conclusion: Noise exposure may activate the AIM2 inflammasome in hippocampal glial cells of rats, releasing excessive inflammatory cytokines and causing neuroinflammation that damages neurons.
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Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, Abramowicz H, Abreu H, Abulaiti Y, Acharya BS, Adam Bourdarios C, Adamczyk L, Addepalli SV, Addison MJ, Adelman J, Adiguzel A, Adye T, Affolder AA, Afik Y, Agaras MN, Agarwala J, Aggarwal A, Agheorghiesei C, Ahmad A, Ahmadov F, Ahmed WS, Ahuja S, Ai X, Aielli G, Aikot A, Ait Tamlihat M, Aitbenchikh B, Aizenberg I, Akbiyik M, Åkesson TPA, Akimov AV, Akiyama D, Akolkar NN, Aktas S, Al Khoury K, Alberghi GL, Albert J, Albicocco P, Albouy GL, Alderweireldt S, Alegria ZL, Aleksa M, Aleksandrov IN, Alexa C, Alexopoulos T, Alfonsi F, Algren M, Alhroob M, Ali B, Ali HMJ, Ali S, Alibocus SW, Aliev M, Alimonti G, Alkakhi W, Allaire C, Allbrooke BMM, Allen JF, Allendes Flores CA, Allport PP, Aloisio A, Alonso F, Alpigiani C, Alvarez Estevez M, Alvarez Fernandez A, Alves Cardoso M, Alviggi MG, Aly M, Amaral Coutinho Y, Ambler A, Amelung C, Amerl M, Ames CG, Amidei D, Amor Dos Santos SP, Amos KR, Ananiev V, Anastopoulos C, Andeen T, Anders JK, Andrean SY, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Anthony MT, Antipov E, Antonelli M, Anulli F, Aoki M, Aoki T, Aparisi Pozo JA, Aparo MA, Aperio Bella L, Appelt C, Apyan A, Aranzabal N, Arbiol Val SJ, Arcangeletti C, Arce ATH, Arena E, Arguin JF, Argyropoulos S, Arling JH, Arnaez O, Arnold H, Artoni G, Asada H, Asai K, Asai S, Asbah NA, Assamagan K, Astalos R, Atashi S, Atkin RJ, Atkinson M, Atmani H, Atmasiddha PA, Augsten K, Auricchio S, Auriol AD, Austrup VA, Avolio G, Axiotis K, Azuelos G, Babal D, Bachacou H, Bachas K, Bachiu A, Backman F, Badea A, Baer TM, Bagnaia P, Bahmani M, Bahner D, Bailey AJ, Bailey VR, Baines JT, Baines L, Baker OK, Bakos E, Bakshi Gupta D, Balakrishnan V, Balasubramanian R, Baldin EM, Balek P, Ballabene E, Balli F, Baltes LM, Balunas WK, Balz J, Banas E, Bandieramonte M, Bandyopadhyay A, Bansal S, Barak L, Barakat M, Barberio EL, Barberis D, Barbero M, Barel MZ, Barends KN, Barillari T, Barisits MS, Barklow T, Baron P, Baron Moreno DA, Baroncelli A, Barone G, Barr AJ, Barr JD, Barranco Navarro L, Barreiro F, Barreiro Guimarães da Costa J, Barron U, Barros Teixeira MG, Barsov S, Bartels F, Bartoldus R, Barton AE, Bartos P, Basan A, Baselga M, Bassalat A, Basso MJ, Basson CR, Bates RL, Batlamous S, Batley JR, Batool B, Battaglia M, Battulga D, Bauce M, Bauer M, Bauer P, Bazzano Hurrell LT, Beacham JB, Beau T, Beaucamp JY, Beauchemin PH, Becherer F, Bechtle P, Beck HP, Becker K, Beddall AJ, Bednyakov VA, Bee CP, Beemster LJ, Beermann TA, Begalli M, Begel M, Behera A, Behr JK, Beirer JF, Beisiegel F, Belfkir M, Bella G, Bellagamba L, Bellerive A, Bellos P, Beloborodov K, Benchekroun D, Bendebba F, Benhammou Y, Benoit M, Bensinger JR, Bentvelsen S, Beresford L, Beretta M, Bergeaas Kuutmann E, Berger N, Bergmann B, Beringer J, Bernardi G, Bernius C, Bernlochner FU, Bernon F, Berrocal Guardia A, Berry T, Berta P, Berthold A, Bertram IA, Bethke S, Betti A, Bevan AJ, Bhalla NK, Bhamjee M, Bhatta S, Bhattacharya DS, Bhattarai P, Bhopatkar VS, Bi R, Bianchi RM, Bianco G, Biebel O, Bielski R, Biglietti M, Bindi M, Bingul A, Bini C, Biondini A, Birch-Sykes CJ, Bird GA, Birman M, Biros M, Biryukov S, Bisanz T, Bisceglie E, Biswal JP, Biswas D, Bitadze A, Bjørke K, Bloch I, Blue A, Blumenschein U, Blumenthal J, Bobbink GJ, Bobrovnikov VS, Boehler M, Boehm B, Bogavac D, Bogdanchikov AG, Bohm C, Boisvert V, Bokan P, Bold T, Bomben M, Bona M, Boonekamp M, Booth CD, Borbély AG, Bordulev IS, Borecka-Bielska HM, Borissov G, Bortoletto D, Boscherini D, Bosman M, Bossio Sola JD, Bouaouda K, Bouchhar N, Boudreau J, Bouhova-Thacker EV, Boumediene D, Bouquet R, Boveia A, Boyd J, Boye D, Boyko IR, Bracinik J, Brahimi N, Brandt G, Brandt O, Braren F, Brau B, Brau JE, Brener R, Brenner L, Brenner R, Bressler S, Britton D, Britzger D, Brock I, Brooijmans G, Brooks WK, Brost E, Brown LM, Bruce LE, Bruckler TL, Bruckman de Renstrom PA, Brüers B, Bruni A, Bruni G, Bruschi M, Bruscino N, Buanes T, Buat Q, Buchin D, Buckley AG, Bulekov O, Bullard BA, Burdin S, Burgard CD, Burger AM, Burghgrave B, Burlayenko O, Burr JTP, Burton CD, Burzynski JC, Busch EL, Büscher V, Bussey PJ, Butler JM, Buttar CM, Butterworth JM, Buttinger W, Buxo Vazquez CJ, Buzykaev AR, Cabrera Urbán S, Cadamuro L, Caforio D, Cai H, Cai Y, Cai Y, Cairo VMM, Cakir O, Calace N, Calafiura P, Calderini G, Calfayan P, Callea G, Caloba LP, Calvet D, Calvet S, Calvet TP, Calvetti M, Camacho Toro R, Camarda S, Camarero Munoz D, Camarri P, Camerlingo MT, Cameron D, Camincher C, Campanelli M, Camplani A, Canale V, Canesse A, Cantero J, Cao Y, Capocasa F, Capua M, Carbone A, Cardarelli R, Cardenas JCJ, Cardillo F, Carducci G, Carli T, Carlino G, Carlotto JI, Carlson BT, Carlson EM, Carminati L, Carnelli A, Carnesale M, Caron S, Carquin E, Carrá S, Carratta G, Carrio Argos F, Carter JWS, Carter TM, Casado MP, Caspar M, Castillo FL, Castillo Garcia L, Castillo Gimenez V, Castro NF, Catinaccio A, Catmore JR, Cavaliere V, Cavalli N, Cavasinni V, Cekmecelioglu YC, Celebi E, Celli F, Centonze MS, Cepaitis V, Cerny K, Cerqueira AS, Cerri A, Cerrito L, Cerutti F, Cervato B, Cervelli A, Cesarini G, Cetin SA, Chakraborty D, Chan J, Chan WY, Chapman JD, Chapon E, Chargeishvili B, Charlton DG, Charman TP, Chatterjee M, Chauhan C, Chekanov S, Chekulaev SV, Chelkov GA, Chen A, Chen B, Chen B, Chen H, Chen H, Chen J, Chen J, Chen M, Chen S, Chen SJ, Chen X, Chen X, Chen Y, Cheng CL, Cheng HC, Cheong S, Cheplakov A, Cheremushkina E, Cherepanova E, Cherkaoui El Moursli R, Cheu E, Cheung K, Chevalier L, Chiarella V, Chiarelli G, Chiedde N, Chiodini G, Chisholm AS, Chitan A, Chitishvili M, Chizhov MV, Choi K, Chomont AR, Chou Y, Chow EYS, Chowdhury T, Chu KL, Chu MC, Chu X, Chudoba J, Chwastowski JJ, Cieri D, Ciesla KM, Cindro V, Ciocio A, Cirotto F, Citron ZH, Citterio M, Ciubotaru DA, Clark A, Clark PJ, Clarry C, Clavijo Columbie JM, Clawson SE, Clement C, Clercx J, Coadou Y, Cobal M, Coccaro A, Barrue RFC, Coelho Lopes De Sa R, Coelli S, Coimbra AEC, Cole B, Collot J, Conde Muiño P, Connell MP, Connell SH, Connelly IA, Conroy EI, Conventi F, Cooke HG, Cooper-Sarkar AM, Cordeiro Oudot Choi A, Corpe LD, Corradi M, Corriveau F, Cortes-Gonzalez A, Costa MJ, Costanza F, Costanzo D, Cote BM, Cowan G, Cranmer K, Cremonini D, Crépé-Renaudin S, Crescioli F, Cristinziani M, Cristoforetti M, Croft V, Crosby JE, Crosetti G, Cueto A, Cuhadar Donszelmann T, Cui H, Cui Z, Cunningham WR, Curcio F, Czodrowski P, Czurylo MM, De Sousa MJDCS, Da Fonseca Pinto JV, Da Via C, Dabrowski W, Dado T, Dahbi S, Dai T, Dal Santo D, Dallapiccola C, Dam M, D'amen G, D'Amico V, Damp J, Dandoy JR, Daneri MF, Danninger M, Dao V, Darbo G, Darmora S, Das SJ, D'Auria S, David C, Davidek T, Davis-Purcell B, Dawson I, Day-Hall HA, De K, De Asmundis R, De Biase N, De Castro S, De Groot N, de Jong P, De la Torre H, De Maria A, De Salvo A, De Sanctis U, De Santis F, De Santo A, De Vivie De Regie JB, Dedovich DV, Degens J, Deiana AM, Del Corso F, Del Peso J, Del Rio F, Delagrange L, Deliot F, Delitzsch CM, Della Pietra M, Della Volpe D, Dell'Acqua A, Dell'Asta L, Delmastro M, Delsart PA, Demers S, Demichev M, Denisov SP, D'Eramo L, Derendarz D, Derue F, Dervan P, Desch K, Deutsch C, Di Bello FA, Di Ciaccio A, Di Ciaccio L, Di Domenico A, Di Donato C, Di Girolamo A, Di Gregorio G, Di Luca A, Di Micco B, Di Nardo R, Diaconu C, Diamantopoulou M, Dias FA, Vale TDD, Diaz MA, Diaz Capriles FG, Didenko M, Diehl EB, Diehl L, Díez Cornell S, Diez Pardos C, Dimitriadi C, Dimitrievska A, Dingfelder J, Dinu IM, Dittmeier SJ, Dittus F, Djama F, Djobava T, Djuvsland JI, Doglioni C, Dohnalova A, Dolejsi J, Dolezal Z, Dona KM, Donadelli M, Dong B, Donini J, D'Onofrio A, D'Onofrio M, Dopke J, Doria A, Dos Santos Fernandes N, Dougan P, Dova MT, Doyle AT, Draguet MA, Dreyer E, Drivas-Koulouris I, Drnevich M, Drobac AS, Drozdova M, Du D, du Pree TA, Dubinin F, Dubovsky M, Duchovni E, Duckeck G, Ducu OA, Duda D, Dudarev A, Duden ER, D'uffizi M, Duflot L, Dührssen M, Dülsen C, Dumitriu AE, Dunford M, Dungs S, Dunne K, Duperrin A, Yildiz HD, Düren M, Durglishvili A, Dwyer BL, Dyckes GI, Dyndal M, Dziedzic BS, Earnshaw ZO, Eberwein GH, Eckerova B, Eggebrecht S, Purcino De Souza EE, Ehrke LF, Eigen G, Einsweiler K, Ekelof T, Ekman PA, El Farkh S, El Ghazali Y, El Jarrari H, El Moussaouy A, Ellajosyula V, Ellert M, Ellinghaus F, Ellis N, Elmsheuser J, Elsing M, Emeliyanov D, Enari Y, Ene I, Epari S, Erdmann J, Erland PA, Errenst M, Escalier M, Escobar C, Etzion E, Evans G, Evans H, Evans LS, Evans MO, Ezhilov A, Ezzarqtouni S, Fabbri F, Fabbri L, Facini G, Fadeyev V, Fakhrutdinov RM, Falciano S, Falda Ulhoa Coelho LF, Falke PJ, Faltova J, Fan C, Fan Y, Fang Y, Fanti M, Faraj M, Farazpay Z, Farbin A, Farilla A, Farooque T, Farrington SM, Fassi F, Fassouliotis D, Faucci Giannelli M, Fawcett WJ, Fayard L, Federic P, Federicova P, Fedin OL, Fedotov G, Feickert M, Feligioni L, Fellers DE, Feng C, Feng M, Feng Z, Fenton MJ, 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N, Spagnolo S, Spalla M, Sperlich D, Spigo G, Spinali S, Spiteri DP, Spousta M, Staats EJ, Stabile A, Stamen R, Stampekis A, Standke M, Stanecka E, Stange MV, Stanislaus B, Stanitzki MM, Stapf B, Starchenko EA, Stark GH, Stark J, Starko DM, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stewart JR, Stockton MC, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Stratmann M, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom DM, Stroynowski R, Strubig A, Stucci SA, Stugu B, Stupak J, Styles NA, Su D, Su S, Su W, Su X, Sugizaki K, Sulin VV, Sullivan MJ, Sultan DMS, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Gudnadottir OS, Sur N, Sutton MR, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, 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A, Tsiskaridze V, Tskhadadze EG, Tsopoulou M, Tsujikawa Y, Tsukerman II, Tsulaia V, Tsuno S, Tsuri K, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna AN, Turchikhin S, Turk Cakir I, Turra R, Turtuvshin T, Tuts PM, Tzamarias S, Tzanis P, Tzovara E, Ukegawa F, Ulloa Poblete PA, Umaka EN, Unal G, Unal M, Undrus A, Unel G, Urban J, Urquijo P, Urrejola P, Usai G, Ushioda R, Usman M, Uysal Z, Vacek V, Vachon B, Vadla KOH, Vafeiadis T, Vaitkus A, Valderanis C, Valdes Santurio E, Valente M, Valentinetti S, Valero A, Valiente Moreno E, Vallier A, Valls Ferrer JA, Van Arneman DR, Van Daalen TR, Van Der Graaf A, Van Gemmeren P, Van Rijnbach M, Van Stroud S, Van Vulpen I, Vanadia M, Vandelli W, Vandenbroucke M, Vandewall ER, Vannicola D, Vannoli L, Vari R, Varnes EW, Varni C, Varol T, Varouchas D, Varriale L, Varvell KE, Vasile ME, Vaslin L, Vasquez GA, Vasyukov A, Vazeille F, Vazquez Schroeder T, Veatch J, Vecchio V, Veen MJ, Veliscek I, Veloce LM, Veloso F, Veneziano S, Ventura A, Ventura 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Weber HA, Weber MS, Weber SM, Wei C, Wei Y, Weidberg AR, Weik EJ, Weingarten J, Weirich M, Weiser C, Wells CJ, Wenaus T, Wendland B, Wengler T, Wenke NS, Wermes N, Wessels M, Wharton AM, White AS, White A, White MJ, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wollrath J, Wolter MW, Wolters H, Wongel AF, Woodward EL, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu SL, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt TR, Wynne BM, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, Xiong A, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi Y, Yamashita E, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang HJ, Yang HT, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita K, Younas S, Young CJS, Young C, Yu C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zak ZK, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Azimuthal Angle Correlations of Muons Produced via Heavy-Flavor Decays in 5.02 TeV Pb+Pb and pp Collisions with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:202301. [PMID: 38829073 DOI: 10.1103/physrevlett.132.202301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/13/2024] [Indexed: 06/05/2024]
Abstract
Angular correlations between heavy quarks provide a unique probe of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions. Results are presented of a measurement of the azimuthal angle correlations between muons originating from semileptonic decays of heavy quarks produced in 5.02 TeV Pb+Pb and pp collisions at the LHC. The muons are measured with transverse momenta and pseudorapidities satisfying p_{T}^{μ}>4 GeV and |η^{μ}|<2.4, respectively. The distributions of azimuthal angle separation Δϕ for muon pairs having pseudorapidity separation |Δη|>0.8, are measured in different Pb+Pb centrality intervals and compared to the same distribution measured in pp collisions at the same center-of-mass energy. Results are presented separately for muon pairs with opposite-sign charges, same-sign charges, and all pairs. A clear peak is observed in all Δϕ distributions at Δϕ∼π, consistent with the parent heavy-quark pairs being produced via hard-scattering processes. The widths of that peak, characterized using Cauchy-Lorentz fits to the Δϕ distributions, are found to not vary significantly as a function of Pb+Pb collision centrality and are similar for pp and Pb+Pb collisions. This observation will provide important constraints on theoretical descriptions of heavy-quark interactions with the quark-gluon plasma.
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Huang M, Han K, Liu W, Wang Z, Liu X, Guo Q. Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134188. [PMID: 38579587 DOI: 10.1016/j.jhazmat.2024.134188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/25/2024] [Accepted: 03/30/2024] [Indexed: 04/07/2024]
Abstract
Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.
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Soeiro M, Aldá Júnior WL, Alves Gallo Pereira M, Barroso Ferreira Filho M, Brandao Malbouisson H, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, Fonseca De Souza S, Martins J, Mora Herrera C, Mota Amarilo K, Mundim L, Nogima H, Santoro A, Silva Do Amaral SM, Sznajder A, Thiel M, Vilela Pereira A, Bernardes CA, Calligaris L, Fernandez Perez Tomei TR, Gregores EM, Mercadante PG, Novaes SF, Orzari B, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Shopova M, Sultanov G, Dimitrov A, Ivanov T, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Keshri S, Thakur S, Cheng T, Guo Q, Javaid T, Mittal M, Yuan L, Bauer G, Hu Z, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Monti F, Shahzad MA, Sharma R, Song JN, Tao J, Wang C, Wang J, Wang Z, Zhang H, Agapitos A, Ban Y, Levin A, Li C, Li Q, Lyu X, Mao Y, Qian SJ, Sun X, Wang D, Yang H, Zhou C, You Z, Lu N, Gao X, Leggat D, Okawa H, Zhang Y, Lin Z, Lu C, Xiao M, Avila 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G, Tews A, Wolf M, Brommer S, Burkart M, Butz E, Chwalek T, Dierlamm A, Droll A, Faltermann N, Giffels M, Gottmann A, Hartmann F, Hofsaess R, Horzela M, Husemann U, Klute M, Koppenhöfer R, Link M, Lintuluoto A, Maier S, Mitra S, Mormile M, Müller T, Neukum M, Oh M, Quast G, Rabbertz K, Regnery B, Shadskiy N, Shvetsov I, Simonis HJ, Trevisani N, Ulrich R, van der Linden J, Von Cube RF, Wassmer M, Wieland S, Wittig F, Wolf R, Wunsch S, Zuo X, Anagnostou G, Assiouras P, Daskalakis G, Kyriakis A, Papadopoulos A, Stakia A, Kontaxakis P, Melachroinos G, Panagiotou A, Papavergou I, Paraskevas I, Saoulidou N, Theofilatos K, Tziaferi E, Vellidis K, Zisopoulos I, Bakas G, Chatzistavrou T, Karapostoli G, Kousouris K, Papakrivopoulos I, Siamarkou E, Tsipolitis G, Zacharopoulou A, Adamidis K, Bestintzanos I, Evangelou I, Foudas C, Gianneios P, Kamtsikis C, Katsoulis P, Kokkas P, Kosmoglou Kioseoglou PG, Manthos N, Papadopoulos I, Strologas J, Bartók M, Hajdu C, Horvath D, Sikler F, Veszpremi V, 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Karunarathna N, Lee L, Nibigira E, Spanier S, Aebi D, Ahmad M, Bouhali O, Dalchenko M, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Mengke T, Muthumuni S, Peltola T, Volobouev I, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Kunnawalkam Elayavalli R, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Li A, Neu C, Perez Lara CE, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Chadeeva M, Chekhovsky V, Chistov R, Dermenev A, Dimova T, Druzhkin D, Dubinin M, Dudko L, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Kardapoltsev L, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Konstantinov D, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Perfilov M, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Search for exotic decays of the Higgs boson to a pair of pseudoscalars in the μμbb and ττbb final states. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2024; 84:493. [PMID: 38757620 PMCID: PMC11093753 DOI: 10.1140/epjc/s10052-024-12727-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/25/2024] [Indexed: 05/18/2024]
Abstract
A search for exotic decays of the Higgs boson (H ) with a mass of 125Ge V to a pair of light pseudoscalars a 1 is performed in final states where one pseudoscalar decays to two b quarks and the other to a pair of muons or τ leptons. A data sample of proton-proton collisions at s = 13 Te V corresponding to an integrated luminosity of 138fb - 1 recorded with the CMS detector is analyzed. No statistically significant excess is observed over the standard model backgrounds. Upper limits are set at 95% confidence level (CL ) on the Higgs boson branching fraction to μ μ b b and to τ τ b b , via a pair of a 1 s. The limits depend on the pseudoscalar mass m a 1 and are observed to be in the range (0.17-3.3) × 10 - 4 and (1.7-7.7) × 10 - 2 in the μ μ b b and τ τ b b final states, respectively. In the framework of models with two Higgs doublets and a complex scalar singlet (2HDM+S), the results of the two final states are combined to determine upper limits on the branching fraction B ( H → a 1 a 1 → ℓ ℓ b b ) at 95% CL , with ℓ being a muon or a τ lepton. For different types of 2HDM+S, upper bounds on the branching fraction B ( H → a 1 a 1 ) are extracted from the combination of the two channels. In most of the Type II 2HDM+S parameter space, B ( H → a 1 a 1 ) values above 0.23 are excluded at 95% CL for m a 1 values between 15 and 60Ge V .
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Grants
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Ministry of Education and Research
- Estonian Research Council via PRG780, PRG803, and PRG445
- European Regional Development Fund
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Lithuanian Academy of Sciences
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Thailand Center of Excellence in Physics
- Institute for the Promotion of Teaching Science and Technology of Thailand
- Special Task Force for Activating Research
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353 (European Union)
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Science Committee, project no. 22rl-037
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, “Excellence of Science - EOS” - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, “Excellence of Science - EOS” - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Fundamental Research Funds for the Central Universities
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Shota Rustaveli National Science Foundation
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Ministry of Science and Higher Education, project no. FSWU-2023-0073 and FSWW-2020-0008
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand)
- CUAASC
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- Kyungpook National University, Daegu
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- Instrumentation and Detector Consortium, Taipei
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- GridPP, University of Oxford, Oxford
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- San Diego Supercomputer Center (SDSC), La Jolla
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin - Madison, Madison
- Vanderbilt University, Nashville
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang HJ, Jiang LL, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner MK, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang X, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. First Observation of a Three-Resonance Structure in e^{+}e^{-}→Nonopen Charm Hadrons. PHYSICAL REVIEW LETTERS 2024; 132:191902. [PMID: 38804946 DOI: 10.1103/physrevlett.132.191902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 03/10/2024] [Accepted: 03/29/2024] [Indexed: 05/29/2024]
Abstract
We report the measurement of the inclusive cross sections for e^{+}e^{-}→nOCH (where nOCH denotes non-open charm hadrons) with improved precision at center-of-mass (c.m.) energies from 3.645 to 3.871 GeV. We observe three resonances: R(3760), R(3780), and R(3810) with significances of 8.1σ, 13.7σ, and 8.8σ, respectively. The R(3810) state is observed for the first time, while the R(3760) and R(3780) states are observed for the first time in the nOCH cross sections. Two sets of resonance parameters describe the energy-dependent line shape of the cross sections well. In set I [set II], the R(3810) state has mass (3805.7±1.1±2.7) [(3805.7±1.1±2.7)] MeV/c^{2}, total width (11.6±2.9±1.9) [(11.5±2.8±1.9)] MeV, and an electronic width multiplied by the nOCH decay branching fraction of (10.9±3.8±2.5) [(11.0±3.4±2.5)] eV. In addition, we measure the branching fractions B[R(3760)→nOCH]=(25.2±16.1±30.4)%[(6.4±4.8±7.7)%] and B[R(3780)→nOCH]=(12.3±6.6±8.3)%[(10.4±4.8±7.0)%] for the first time. The R(3760) state can be interpreted as an open-charm (OC) molecular state, but containing a simple four-quark state component. The R(3810) state can be interpreted as a hadrocharmonium state.
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Diao H, Lu G, Zhang Y, Wang Z, Liu X, Ma Q, Yu H, Li Y. Risk factors for multidrug-resistant and extensively drug-resistant Acinetobacter baumannii infection of patients admitted in intensive care unit: a systematic review and meta-analysis. J Hosp Infect 2024; 149:77-87. [PMID: 38710306 DOI: 10.1016/j.jhin.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/17/2024] [Accepted: 04/20/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Multidrug-resistant (MDR) and extensively drug-resistant (XDR) Acinetobacter baumannii infections pose challenges for clinical treatment and cause high mortality, particularly in intensive care units (ICUs). AIM To systematically summarize and analyse the risk factors for MDR/XDR A. baumannii-infected patients admitted to ICUs. METHODS PubMed, Embase, Web of Science, and the Cochrane Library were searched for eligible original studies published in English before October 2023. Meta-analysis was conducted where appropriate, with mean differences (MDs) and odds ratios (ORs) calculated for continuous and nominal scaled data. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). FINDINGS Ten studies reporting 1199 ICU patients (604 from general ICUs, 435 from neonatal ICUs, and 160 from paediatric ICUs) from eight countries were included in our analysis. Risk factors associated with MDR A. baumannii infection among patients admitted to general ICUs included high Acute Physiology And Clinical Health II (APACHE Ⅱ) score (mean difference (MD): 7.52; 95% confidence interval (CI): 3.24-11.80; P = 0.0006), invasive procedures (odds ratio (OR): 3.47; 95% CI: 1.70-7.10; P = 0.0006), longer ICU stay (MD: 3.40; 95% CI: 2.94-3.86; P < 0.00001), and use of antibiotics (OR: 2.69; 95% CI: 1.22-5.94; P = 0.01). In the sub-group analysis, longer neonatal ICU stay (MD: 16.88; 95% CI: 9.79-23.97; P < 0.00001) was associated with XDR A. baumannii infection. CONCLUSION Close attention should be paid to patients with longer ICU stays, undergoing invasive procedures, using antibiotics, and with high APACHE Ⅱ scores to reduce the risk of MDR and XDR A. baumannii infections.
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Rubio AA, Baharani VA, Dadonaite B, Parada M, Abernathy ME, Wang Z, Lee YE, Eso MR, Phung J, Ramos I, Chen T, Nesr GE, Bloom JD, Bieniasz PD, Nussenzweig MC, Barnes CO. Bispecific antibodies with broad neutralization potency against SARS-CoV-2 variants of concern. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592584. [PMID: 38766244 PMCID: PMC11100608 DOI: 10.1101/2024.05.05.592584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The ongoing emergence of SARS-CoV-2 variants of concern (VOCs) that reduce the effectiveness of antibody therapeutics necessitates development of next-generation antibody modalities that are resilient to viral evolution. Here, we characterized N-terminal domain (NTD) and receptor binding domain (RBD)-specific monoclonal antibodies previously isolated from COVID-19 convalescent donors for their activity against emergent SARS-CoV-2 VOCs. Among these, the NTD-specific antibody C1596 displayed the greatest breadth of binding to VOCs, with cryo-EM structural analysis revealing recognition of a distinct NTD epitope outside of the site i antigenic supersite. Given C1596's favorable binding profile, we designed a series of bispecific antibodies (bsAbs) termed CoV2-biRNs, that featured both NTD and RBD specificities. Notably, two of the C1596-inclusive bsAbs, CoV2-biRN5 and CoV2-biRN7, retained potent in vitro neutralization activity against all Omicron variants tested, including XBB.1.5, EG.5.1, and BA.2.86, contrasting the diminished potency of parental antibodies delivered as monotherapies or as a cocktail. Furthermore, prophylactic delivery of CoV2-biRN5 significantly reduced the viral load within the lungs of K18-hACE2 mice following challenge with SARS-CoV-2 XBB.1.5. In conclusion, our NTD-RBD bsAbs offer promising potential for the design of resilient, next-generation antibody therapeutics against SARS-CoV-2 VOCs.
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Holt F, Ivanova A, Wang Z, Darby S, Duane F, Ntentas G, Oliveros S, Lavery B, Shah K, Eichholz A, Dodwell D, Taylor C. Estimated Doses to the Heart, Lungs and Oesophagus and Risks From Typical UK Radiotherapy for Early Breast Cancer During 2015-2023. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00177-8. [PMID: 38853062 DOI: 10.1016/j.clon.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/24/2024] [Accepted: 05/01/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE Breast cancer radiotherapy can increase the risks of heart disease, lung cancer and oesophageal cancer. At present, the best dosimetric predictors of these risks are mean doses to the whole heart, lungs and oesophagus, respectively. We aimed to estimate typical doses to these organs and resulting risks from UK breast cancer radiotherapy. METHODS A systematic review and meta-analysis was conducted of planned or delivered mean doses to the whole heart, lungs or oesophagus from UK breast cancer radiotherapy in studies published during 2015-2023. Average mean doses were summarised for combinations of laterality and clinical targets. Heart disease and lung cancer mortality risks were then estimated using established models. RESULTS For whole heart, thirteen studies reported 2893 doses. Average mean doses were higher in left than in right-sided radiotherapy and increased with extent of clinical targets. For left-sided radiotherapy, average mean heart doses were: 2.0 Gy (range 1.2-8.0 Gy) breast/chest wall, 2.7 Gy (range 0.6-5.6 Gy) breast/chest wall with either axilla or supraclavicular nodes and 2.9 Gy (range 1.3-4.7 Gy) breast/chest wall with nodes including internal mammary. For right-sided radiotherapy, average mean heart doses were: 1.0 Gy (range 0.3-1.0 Gy) breast/chest wall and 1.2 Gy (range 1.0-1.4 Gy) breast/chest wall with either axilla or supraclavicular nodes. There were no whole heart dose estimates from right internal mammary radiotherapy. For whole lung, six studies reported 2230 doses. Average mean lung doses increased with extent of targets irradiated: 2.6 Gy (range 1.4-3.0 Gy) breast/chest wall, 3.0 Gy (range 0.9-5.1 Gy) breast/chest wall with either axilla or supraclavicular nodes and 7.1 Gy (range 6.7-10.0 Gy) breast/chest wall with nodes including internal mammary. For whole oesophagus, two studies reported 76 doses. Average mean oesophagus doses increased with extent of targets irradiated: 1.4 Gy (range 1.0-2.0 Gy) breast/chest wall with either axilla or supraclavicular nodes and 5.8 Gy (range 1.9-10.0 Gy) breast/chest wall with nodes including internal mammary. CONCLUSIONS The typical doses to these organs may be combined with dose-response relationships to estimate radiation risks. Estimated 30-year absolute lung cancer mortality risks from modern UK breast cancer radiotherapy for patients irradiated when aged 50 years were 2-6% for long-term continuing smokers, and <1% for non-smokers. Estimated 30-year mortality risks for heart disease were <1%.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Bao HR, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YH, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fischer K, Fritsch M, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Gutierrez J, Han KL, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu BY, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, In der Wiesche N, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kavatsyuk M, Ke BC, Khachatryan V, Khoukaz A, Kiuchi R, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Larin P, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma H, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Moses B, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu QL, Niu WD, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peng YY, Peters K, Ping JL, Ping RG, Plura S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wan Y, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang NY, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YL, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YH, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng SH, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HC, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang J, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao RP, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Determination of Spin-Parity Quantum Numbers of X(2370) as 0^{-+} from J/ψ→γK_{S}^{0}K_{S}^{0}η^{'}. PHYSICAL REVIEW LETTERS 2024; 132:181901. [PMID: 38759175 DOI: 10.1103/physrevlett.132.181901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/28/2024] [Indexed: 05/19/2024]
Abstract
Based on (10087±44)×10^{6} J/ψ events collected with the BESIII detector, a partial wave analysis of the decay J/ψ→γK_{S}^{0}K_{S}^{0}η^{'} is performed. The mass and width of the X(2370) are measured to be 2395±11(stat)_{-94}^{+26}(syst) MeV/c^{2} and 188_{-17}^{+18}(stat)_{-33}^{+124}(syst) MeV, respectively. The corresponding product branching fraction is B[J/ψ→γX(2370)]×B[X(2370)→f_{0}(980)η^{'}]×B[f_{0}(980)→K_{S}^{0}K_{S}^{0}]=(1.31±0.22(stat)_{-0.84}^{+2.85}(syst))×10^{-5}. The statistical significance of the X(2370) is greater than 11.7σ and the spin parity is determined to be 0^{-+} for the first time. The measured mass and spin parity of the X(2370) are consistent with the predictions of the lightest pseudoscalar glueball.
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