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Hu W, Cheng L, Zhao J, Wu Y, Lin Y, Yin J. Purine metabolite inosine induced by transforming growth factor‑β promotes epithelial‑mesenchymal transition in colorectal cancer. Oncol Lett 2024; 28:416. [PMID: 38988443 PMCID: PMC11234247 DOI: 10.3892/ol.2024.14549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/26/2024] [Indexed: 07/12/2024] Open
Abstract
Transforming growth factor-β (TGF-β) signaling pathway serves a pivotal role in the pathogenesis of colorectal cancer (CRC). However, the specific molecular mechanisms by which the TGF-β signaling pathway regulates CRC are still not fully understood. In the present study, metabolomics and transcriptomics were used to screen for key metabolites and regulatory genes most related to the regulation of the TGF-β signaling pathway in CRC. Additionally, reverse transcription-quantitative PCR, western blotting and Transwell assays were performed to assess the process of epithelial-mesenchymal transition (EMT). Metabolomics analysis indicated that TGF-β1 has an impact on purine metabolism, leading to an increase in the purine metabolite inosine. The increase of inosine is essential for facilitating EMT and cell migration in CRC cells. Furthermore, the integrated analysis of metabolomics and transcriptomics data revealed that TGF-β1 induces the expression of laccase domain-containing 1 (LACC1), an enzyme involved in the regulation of inosine. Knockdown of LACC1 resulted in a reduction of TGF-β1-induced alterations in inosine levels, EMT and cell migration in CRC cells. The results of the present study suggest that the TGF-β signaling pathway is involved in the regulation of purine metabolism in CRC through the modulation of LACC1 expression. Furthermore, LACC1 appears to influence EMT and cell migration by elevating the levels of the purine metabolite inosine.
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Yang S, Yang X, Lyu T, Huang JL, Chen A, He X, Braithwaite D, Mehta HJ, Wu Y, Guo Y, Bian J. Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024; 8:463-477. [PMID: 39131104 PMCID: PMC11310180 DOI: 10.1007/s41666-024-00166-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/12/2024] [Accepted: 05/12/2024] [Indexed: 08/13/2024]
Abstract
Pulmonary nodules and nodule characteristics are important indicators of lung nodule malignancy. However, nodule information is often documented as free text in clinical narratives such as radiology reports in electronic health record systems. Natural language processing (NLP) is the key technology to extract and standardize patient information from radiology reports into structured data elements. This study aimed to develop an NLP system using state-of-the-art transformer models to extract pulmonary nodules and associated nodule characteristics from radiology reports. We identified a cohort of 3080 patients who underwent LDCT at the University of Florida health system and collected their radiology reports. We manually annotated 394 reports as the gold standard. We explored eight pretrained transformer models from three transformer architectures including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT), for clinical concept extraction, relation identification, and negation detection. We examined general transformer models pretrained using general English corpora, transformer models fine-tuned using a clinical corpus, and a large clinical transformer model, GatorTron, which was trained from scratch using 90 billion words of clinical text. We compared transformer models with two baseline models including a recurrent neural network implemented using bidirectional long short-term memory with a conditional random fields layer and support vector machines. RoBERTa-mimic achieved the best F1-score of 0.9279 for nodule concept and nodule characteristics extraction. ALBERT-base and GatorTron achieved the best F1-score of 0.9737 in linking nodule characteristics to pulmonary nodules. Seven out of eight transformers achieved the best F1-score of 1.0000 for negation detection. Our end-to-end system achieved an overall F1-score of 0.8869. This study demonstrated the advantage of state-of-the-art transformer models for pulmonary nodule information extraction from radiology reports. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-024-00166-5.
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Wu Y, Wu Q, Fan X, Yang L, Zou L, Liu Q, Shi G, Yang X, Tang K. Study on chitosan/gelatin hydrogels containing ceria nanoparticles for promoting the healing of diabetic wound. J Biomed Mater Res A 2024; 112:1532-1547. [PMID: 38501727 DOI: 10.1002/jbm.a.37701] [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: 07/07/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/20/2024]
Abstract
Chronic inflammation at diabetic wound sites results in the uncontrolled accumulation of pro-inflammatory factors and reactive oxygen species (ROS), which impedes cell proliferation and delays wound healing. To promote the healing of diabetic wounds, chitosan/gelatin hydrogels containing ceria nanoparticles (CNPs) of various sizes were created in the current study. CNPs' efficacy in removingO 2 • - , •OH, and H2O2 was demonstrated, and the scavenging ability of CNPs of varying sizes was compared. The in vitro experiments demonstrated that hydrogels containing CNPs could effectively protect cells from ROS-induced damage and facilitate mouse fibroblast migration. Furthermore, during the treatment of diabetic wounds in vivo, hydrogels containing CNPs exhibited anti-inflammatory activity and could reduce the expression of the pro-inflammatory factors TNF-α (above 30%), IL-6 (above 90%), and IL-1β (above 80%), and effectively promote wound closure (above 80%) by inducing re-epithelialization, collagen deposition, and angiogenesis. In addition, the biological properties and therapeutic effects of hydrogels containing CNPs of various sizes were compared and discussed. The finding revealed that hydrogels with 4 nm CNPs exhibited more significant biological properties and had implications for diabetic wound treatment.
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Chen TF, Sheng XH, Zhang HY, Guo YP, Yang LN, Wu Y, Wang NS. [Extracorporeal carbon dioxide removal combined with continuous renal replacement therapy in a uremic patient with severe COVID-19]. ZHONGHUA NEI KE ZA ZHI 2024; 63:889-892. [PMID: 39183164 DOI: 10.3760/cma.j.cn112138-20240110-00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
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Peng C, Yang X, Chen A, Yu Z, Smith KE, Costa AB, Flores MG, Bian J, Wu Y. Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need. J Am Med Inform Assoc 2024; 31:1892-1903. [PMID: 38630580 PMCID: PMC11339507 DOI: 10.1093/jamia/ocae078] [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: 12/12/2023] [Revised: 02/26/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. METHODS We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (ie, trainable vectors) with frozen LLM, where the LLM parameters were not updated (ie, frozen) and only the vectors of soft prompts were updated, known as prompt tuning. We added additional soft prompts as a prefix to the input layer, which were optimized during the prompt tuning. We evaluated the proposed method using 7 clinical NLP tasks and compared them with previous task-specific solutions based on Transformer models. RESULTS AND CONCLUSION The proposed approach achieved state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one unified generative LLM. Our approach outperformed previous task-specific transformer models by ∼3% for concept extraction and 7% for relation extraction applied to social determinants of health, 3.4% for clinical concept normalization, 3.4%-10% for clinical abbreviation disambiguation, and 5.5%-9% for natural language inference. Our approach also outperformed a previously developed prompt-based machine reading comprehension (MRC) model, GatorTron-MRC, for clinical concept and relation extraction. The proposed approach can deliver the "one model for all" promise from training to deployment using a unified generative LLM.
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Wu Y, Shen N, Hope C, Noh HI, Richardson BN, Swartz MC, Bai J. A systematic review of the gut microbiome, metabolites, and multi-omics biomarkers across the colorectal cancer care continuum. Benef Microbes 2024:1-25. [PMID: 39147373 DOI: 10.1163/18762891-bja00026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 06/20/2024] [Indexed: 08/17/2024]
Abstract
The gut microbiome and the microbial metabolome contribute to treatment efficacy and treatment outcomes across the cancer care spectrum. This study systematically reviewed the existing literature between 2007 to March 2022 to elucidate the role of gut microbiota-metabolite biomarkers in colorectal cancer (CRC) care and treatment-related outcomes. Using Covidence, all studies identified were screened by title and abstract, followed by a full-text review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and data extraction. We analysed 13 non-experimental and 9 experimental CRC studies and found that, usually, the α-diversity of the gut microbiome and short-chain fatty acids decreased in CRC patients, while amino acids (e.g. glutamate) increased in CRC patients. Correlations between specific gut microbial taxa and metabolites were identified, with amino acids, fatty acids, and glycerol positively associated with certain gut microbes. Interventions promoting gut microbes and microbial metabolites associated with better health outcomes (e.g. Bifidobacterium, Lactobacillus, butyric acid, and bile acid) can potentially promote treatment efficacy and improve cancer care outcomes. Gut microbial metabolism should be integrated into targeted cancer interventions for CRC patients, given the confirmed role of the gut microbiome and metabolome pathways across the CRC care continuum.
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Wang S, Wu J, Wang Q, Zhang Y, Yuan H, Wang J, Wu Y, Xu Y, Ji N, Quan B, Wang H, Shen Q. Evaluation of a miniature mass spectrometer based point-of-care-test method for direct analysis of amlodipine and benazepril in whole blood. J Pharm Biomed Anal 2024; 245:116194. [PMID: 38704878 DOI: 10.1016/j.jpba.2024.116194] [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: 03/17/2024] [Revised: 04/22/2024] [Accepted: 04/30/2024] [Indexed: 05/07/2024]
Abstract
A miniature mass spectrometer (mMS) based point-of-care testing (POCT) method was evaluated for on-site detecting the hypertension drugs, amlodipine and benazepril. The instrument parameters, including voltage, ISO1, ISO2, and CID, were optimized, under which the target compounds could be well detected in MS2. When these two drugs were injected simultaneously, the mutual ionization inhibition and mutual reduction between amlodipine and benazepril were evaluated. This phenomenon was severe on the precursor ions but had a small impact on the product ions, thus making this POCT method suitable for analysis using product ions. Finally, the method was validated and applied. The blood samples from patients were tested one hour after oral administration of the drugs (20 mg), and the benazepril was quantitatively analyzed using a standard curve, with detected concentrations ranging from 190.6 to 210 μg L-1 and a relative standard deviation (RSD) of 8.6 %. In summary, amlodipine has low sensitivity and can only be detected at higher concentrations, while benazepril has high sensitivity, good linearity, and even meets semi-quantitative requirements. The research results of this study are of great clinical significance for monitoring blood drug concentrations during hypertension medication, predicting drug efficacy, and customizing individualized medication plans.
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Wu Y, Yang M, Fan J, Peng Y, Deng L, Ding Y, Yang R, Zhou J, Miao D, Fu Q. Correction to: Deficiency of osteoblastic Arl6ip5 impaired osteoblast differentiation and enhanced osteoclastogenesis via disturbance of ER calcium homeostasis and induction of ER stress-mediated apoptosis. Cell Death Dis 2024; 15:550. [PMID: 39085230 PMCID: PMC11291709 DOI: 10.1038/s41419-024-06858-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
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Yan Y, Wu Y, Lu C, Wei Y, Wang J, Weng B, Huang WY, Zhang JL, Yang K, Lu K. Electrostatic Self-Assembly of CdS Quantum Dots with Co 9S 8 Hollow Nanotubes for Enhanced Visible Light Photocatalytic H 2 Production. Molecules 2024; 29:3530. [PMID: 39124934 PMCID: PMC11314185 DOI: 10.3390/molecules29153530] [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: 06/21/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 08/12/2024] Open
Abstract
CdS quantum dots (CdS QDs) are regarded as a promising photocatalyst due to their remarkable response to visible light and suitable placement of conduction bands and valence bands. However, the problem of photocorrosion severely restricts their application. Herein, the CdS QDs-Co9S8 hollow nanotube composite photocatalyst has been successfully prepared by loading Co9S8 nanotubes onto CdS QDs through an electrostatic self-assembly method. The experimental results show that the introduction of Co9S8 cocatalyst can form a stable structure with CdS QDs, and can effectively avoid the photocorrosion of CdS QDs. Compared with blank CdS QDs, the CdS QDs-Co9S8 composite exhibits obviously better photocatalytic hydrogen evolution performance. In particular, CdS QDs loaded with 30% Co9S8 (CdS QDs-30%Co9S8) demonstrate the best photocatalytic performance, and the H2 production rate reaches 9642.7 μmol·g-1·h-1, which is 60.3 times that of the blank CdS QDs. A series of characterizations confirm that the growth of CdS QDs on Co9S8 nanotubes effectively facilitates the separation and migration of photogenerated carriers, thereby improving the photocatalytic hydrogen production properties of the composite. We expect that this work will facilitate the rational design of CdS-based photocatalysts, thereby enabling the development of more low-cost, high-efficiency and high-stability composites for photocatalysis.
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Bjarnadottir RI, Wu Y, Snigurska UA, Ser SE, Solberg LM, Martinez KA, Bolin S, Dwarica SE, Dunn E, Duckworth LJD, Lou C, Paredes DJ, Yu Z, Lucero RJ. Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing. Stud Health Technol Inform 2024; 315:373-378. [PMID: 39049286 DOI: 10.3233/shti240172] [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: 07/27/2024]
Abstract
Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls. The resulting annotated data corpus had moderately high interrater reliability (F-score=0.74) and captured a breadth of clinical concepts for extraction with potential utility beyond patient falls. Further research is needed to determine which annotation tasks most benefit from nursing expert annotators, to optimize efficiency when tapping into the invaluable resource represented by the nursing workforce.
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Jing H, Wu Y, Wu F, Zhang ZL, Ma L, Ren LP. [Analysis of clinical characteristics and causes of death in 38 patients with pneumoconiosis]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:530-533. [PMID: 39075008 DOI: 10.3760/cma.j.cn121094-20230905-00051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Objective: To analyze the clinical characteristics and causes of death of patients with pneumoconiosis, and to provide evidence for the prevention and management of pneumoconiosis. Methods: From June 2022 to July 2023, 38 dead patients with pneumoconiosis confirmed by Shandong Academy of Occupational Health and Occupational Medicine were selected as the research objects. The clinical data of patients were collected through hospital information system (HIS) and laboratory information management system (LIS) to analyze the basic situation of patients with pneumoconiosis who died, the distribution of industry types, the course of disease, the nature of work, the type of reimbursement, complications/comorbidities, and the direct causes of death. Univariate analysis of variance was used to compare the course of pneumoconiosis death in patients with different age of exposure to dust. Results: Among the 38 patients with pneumoconiosis, there were 37 males and 1 female. The age of exposure to dust was 5-37 (19.29±8.17) years, the duration of disease was 5-41 (20.26±8.53) years, and the age of death was 27-86 (70.42±12.26) years old. There were 10 cases of stage Ⅰ pneumoconiosis, 18 cases of stage Ⅱ, 10 cases of stage Ⅲ pneumoconiosis and 32 cases (84.21%) of silicosis. There were 30 (78.95%) people aged ≥65 years and 8 (21.05%) people aged <65 years. The industry was mainly metal products (18 workers, 47.37%), and the distribution of work was mainly excavation workers (11 workers, 28.95%). The death course of pneumoconiosis patients with 10-<30 years of exposure to dust accounted for 76.32% (29/38). The average course of pneumoconiosis patients with 20-<30 years of exposure to dust was the longest[ (24.00±9.39) years], and there was no statistically significant difference in the average course of disease among different age of exposure to dust groups (F=1.81, P=0.165). The working units of the deceased patients were private enterprises or factories, and the hospitalization expenses were borne by individuals for 21 people (55.26%). The working unit was a state-owned enterprise, and 17 people (44.74%) were reimbursed for hospitalization expenses and work-related injuries. The main comorbidities/complications of pneumoconiosis patients were respiratory infection in 18 cases (47.37%) and chronic pulmonary heart disease (47.37%). The top 3 direct causes of death were pneumoconiosis in 13 cases (34.21%), pulmonary infection in 10 cases (26.32%) and lung cancer in 7 cases (18.42%) . Conclusion: Most of 38 cases of pneumoconiosis patients death diseases such as multiple combination of respiratory system, cardiovascular system, respiratory system disease is a major cause of death in pneumoconiosis patients.
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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, 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, 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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. Statistical Combination of ATLAS Run 2 Searches for Charginos and Neutralinos at the LHC. PHYSICAL REVIEW LETTERS 2024; 133:031802. [PMID: 39094132 DOI: 10.1103/physrevlett.133.031802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/07/2024] [Indexed: 08/04/2024]
Abstract
Statistical combinations of searches for charginos and neutralinos using various decay channels are performed using 139 fb^{-1} of pp collision data at sqrt[s]=13 TeV with the ATLAS detector at the Large Hadron Collider. Searches targeting pure-wino chargino pair production, pure-wino chargino-neutralino production, or Higgsino production decaying via standard model W, Z, or h bosons are combined to extend the mass reach to the produced supersymmetric particles by 30-100 GeV. The depth of the sensitivity of the original searches is also improved by the combinations, lowering the 95% C.L. cross-section upper limits by 15%-40%.
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Wen XJ, Lu Y, Huang PL, Zhang YY, Yu JJ, Qi PJ, Huang Q, Yang J, Liu HQ, Hou B, Wang LY, Li HQ, Lin W, Wu Y, Zhang RD, Li J, Luo D, Li YN, Liu M, Zhao ZW, Liu Y, Zheng HY. [Efficacy of venetoclax-based induction regimen in newly diagnosed pediatric acute myeloid leukemia]. ZHONGHUA YI XUE ZA ZHI 2024; 104:2513-2520. [PMID: 38978375 DOI: 10.3760/cma.j.cn112137-20240108-00056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Objective: To explore the efficacy of venetoclax-based induction regimen for children with newly diagnosed acute myeloid leukemia (AML). Methods: Children with newly diagnosed AML in Beijing Children's Hospital Affiliated to Capital Medical University and Baoding Hospital Affliliated to Capital Medical University from November 2019 and December 2023 were prospectively included. The patients were divided into DAH group (daunorubicin, cytarabine and homoharringtonine) and VAH group (venetoclax, cytarabine and homoharringtonine) according to induction regimen. The clinical data of the children were collected, the clinical characteristics and induced remission rate between the two groups were compared, and multivariate logistic regression was used to analyze the related factors affecting the induced remission rate. Results: A total of 135 patients were enrolled, including 96 cases in the DAH group (54 males and 42 females), aged [M (Q1, Q3)] 6.4 (3.9, 11.6) years and 39 cases in the VAH group (26 males and 13 females), aged 8.0 (6.2, 13.2) years. Among patients initially diagnosed with low-medium risk AML, the morphologic complete remission rates were 94.7% (18/19) in the VAH group and 84.4% (38/45) in the DAH group, respectively, and the negativity conversion rates of minirnal residual disease (MRD) were 57.9% (11/19) and 46.7% (21/45), respectively, with no statistically difference (all P>0.05). Among patients initially diagnoised with high-risk AML, the morphologic complete remission rates in the VAH group was higher than that in the DAH group [95.0% (19/20) vs 70.6% (36/51), P=0.027], and negativity conversion rates of MRD were 45.0% (9/20) and 33.3% (17/51), respectively, with no statistically difference (P=0.359). The induction regimen (venetoclax, cytarabine and homoharringtonin) was beneficial to morphological remission (OR=0.126, 95%CI: 0.025-0.629). FLT3 mutation was not conducive to morphological remission (OR=5.832, 95%CI: 1.778-19.124) and negative MRD (OR=4.166, 95%CI: 1.396-12.433). Conclusion: Venetoclax-based induction regimen is more effective than traditional chemotherapy regimen for newly diagnosed pediatric AML.
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A, Feofilov G, Fernández Téllez A, Ferrandi L, Ferrer MB, Ferrero A, Ferrero C, Ferretti A, Feuillard VJG, Filova V, Finogeev D, Fionda FM, Flatland E, Flor F, Flores AN, Foertsch S, Fokin I, Fokin S, Fragiacomo E, Frajna E, Fuchs U, Funicello N, Furget C, Furs A, Fusayasu T, Gaardhøje JJ, Gagliardi M, Gago AM, Gahlaut T, Galvan CD, Gangadharan DR, Ganoti P, Garabatos C, García Chávez T, Garcia-Solis E, Gargiulo C, Gasik P, Gautam A, Gay Ducati MB, Germain M, Ghimouz A, Ghosh C, Giacalone M, Gioachin G, Giubellino P, Giubilato P, Glaenzer AMC, Glässel P, Glimos E, Goh DJQ, Gonzalez V, Gordeev P, Gorgon M, Goswami K, Gotovac S, Grabski V, Graczykowski LK, Grecka E, Grelli A, Grigoras C, Grigoriev V, Grigoryan S, Grosa F, Grosse-Oetringhaus JF, Grosso R, Grund D, Grunwald NA, Guardiano GG, Guernane R, Guilbaud M, Gulbrandsen K, Gündem T, Gunji T, Guo W, Gupta A, Gupta R, Gupta R, Gwizdziel K, Gyulai L, Hadjidakis C, Haider FU, Haidlova S, Hamagaki H, Hamdi A, Han Y, Hanley BG, Hannigan R, Hansen J, Haque MR, Harris JW, Harton A, Hassan H, Hatzifotiadou D, Hauer P, Havener LB, Heckel ST, Hellbär E, Helstrup H, Hemmer M, Herman T, Herrera Corral G, Herrmann F, Herrmann S, Hetland KF, Heybeck B, Hillemanns H, Hippolyte B, Hoffmann FW, Hofman B, Hong GH, Horst M, Horzyk A, Hou Y, Hristov P, Hughes C, Huhn P, Huhta LM, Humanic TJ, Hutson A, Hutter D, Ilkaev R, Ilyas H, Inaba M, Innocenti GM, Ippolitov M, Isakov A, Isidori T, Islam MS, Ivanov M, Ivanov M, Ivanov V, Iversen KE, Jablonski M, Jacak B, Jacazio N, Jacobs PM, Jadlovska S, Jadlovsky J, Jaelani S, Jahnke C, Jakubowska MJ, Janik MA, Janson T, Ji S, Jia S, Jimenez AAP, Jonas F, Jones DM, Jowett JM, Jung J, Jung M, Junique A, Jusko A, Kaewjai J, Kalinak P, Kalteyer AS, Kalweit A, Kaplin V, Karasu Uysal A, Karatovic D, Karavichev O, Karavicheva T, Karczmarczyk P, Karpechev E, Karwowska MJ, Kebschull U, Keidel R, Keijdener DLD, Keil M, Ketzer B, Khade SS, Khan AM, Khan S, Khanzadeev A, Kharlov Y, Khatun A, Khuntia A, Kileng B, Kim B, Kim C, Kim DJ, Kim EJ, Kim J, Kim JS, Kim J, Kim J, Kim M, Kim S, Kim T, Kimura K, Kirsch S, Kisel I, Kiselev S, Kisiel A, Kitowski JP, Klay JL, Klein J, Klein S, Klein-Bösing C, Kleiner M, Klemenz T, Kluge A, Knospe AG, Kobdaj C, Kollegger T, Kondratyev A, Kondratyeva N, Kondratyuk E, Konig J, Konigstorfer SA, Konopka PJ, Kornakov G, Korwieser M, Koryciak SD, Kotliarov A, Kovalenko V, Kowalski M, Kozhuharov V, Králik I, Kravčáková A, Krcal L, Krivda M, Krizek F, Krizkova Gajdosova K, Kroesen M, Krüger M, Krupova DM, Kryshen E, Kučera V, Kuhn C, Kuijer PG, Kumaoka T, Kumar D, Kumar L, Kumar N, Kumar S, Kundu S, Kurashvili P, Kurepin A, Kurepin AB, Kuryakin A, Kushpil S, Kuskov V, Kweon MJ, Kwon Y, La Pointe SL, La Rocca P, Lakrathok A, Lamanna M, Landou AR, Langoy R, Larionov P, Laudi E, Lautner L, Lavicka R, Lea R, Lee H, Legrand I, Legras G, Lehrbach J, Lelek TM, Lemmon RC, León Monzón I, Lesch MM, Lesser ED, Lévai P, Li X, Lien J, Lietava R, Likmeta I, Lim B, Lim SH, Lindenstruth V, Lindner A, Lippmann C, Liu DH, Liu J, Liveraro GSS, Lofnes IM, Loizides C, Lokos S, Lömker J, Loncar P, Lopez X, López Torres E, Lu P, Lugo FV, Luhder JR, Lunardon M, Luparello G, Ma YG, Mager M, Maire A, Majerz EM, Makariev MV, Malaev M, Malfattore G, Malik NM, Malik QW, Malik SK, Malinina L, Mallick D, Mallick N, Mandaglio G, Mandal SK, Manko V, Manso F, Manzari V, Mao Y, Marcjan RW, Margagliotti GV, Margotti A, Marín A, Markert C, Martinengo P, Martínez MI, Martínez García G, Martins MPP, Masciocchi S, Masera M, Masoni A, Massacrier L, Massen O, Mastroserio A, Matonoha O, Mattiazzo S, Matyja A, Mayer C, Mazuecos AL, Mazzaschi F, Mazzilli M, Mdhluli JE, Melikyan Y, Menchaca-Rocha A, Mendez JEM, Meninno E, Menon AS, Meres M, Mhlanga S, Miake Y, Micheletti L, Mihaylov DL, Mikhaylov K, Mishra AN, Miśkowiec D, Modak A, Mohanty B, Mohisin Khan M, Molander MA, Monira S, Mordasini C, Moreira De Godoy DA, Morozov I, Morsch A, Mrnjavac T, Muccifora V, Muhuri S, Mulligan JD, Mulliri A, Munhoz MG, Munzer RH, Murakami H, Murray S, Musa L, Musinsky J, Myrcha JW, Naik B, Nambrath AI, Nandi BK, Nania R, Nappi E, Nassirpour AF, Nath A, Nattrass C, Naydenov MN, Neagu A, Negru A, Nekrasova E, Nellen L, Nepeivoda R, Nese S, Neskovic G, Nicassio N, Nielsen BS, Nielsen EG, Nikolaev S, Nikulin S, Nikulin V, Noferini F, Noh S, Nomokonov P, Norman J, Novitzky N, Nowakowski P, Nyanin A, Nystrand J, Ogino M, Oh S, Ohlson A, Okorokov VA, Oleniacz J, Oliveira Da Silva AC, Onnerstad A, Oppedisano C, Ortiz Velasquez A, Otwinowski J, Oya M, Oyama K, Pachmayer Y, Padhan S, Pagano D, Paić G, Paisano-Guzmán S, Palasciano A, Panebianco S, Park H, Park H, Park J, Parkkila JE, Patley Y, Patra RN, Paul B, Pei H, Peitzmann T, Peng X, Pennisi M, Perciballi S, Peresunko D, Perez GM, Pestov Y, Petrov V, Petrovici M, Pezzi RP, Piano S, Pikna M, Pillot P, Pinazza O, Pinsky L, Pinto C, Pisano S, Płoskoń M, Planinic M, Pliquett F, Poghosyan MG, Polichtchouk B, Politano S, Poljak N, Pop A, Porteboeuf-Houssais S, Pozdniakov V, Pozos IY, Pradhan KK, Prasad SK, Prasad S, Preghenella R, Prino F, Pruneau CA, Pshenichnov I, Puccio M, Pucillo S, Pugelova Z, Qiu S, Quaglia L, Ragoni S, Rai A, Rakotozafindrabe A, Ramello L, Rami F, Rancien TA, Rasa M, Räsänen SS, Rath R, Rauch MP, Ravasenga I, Read KF, Reckziegel C, Redelbach AR, Redlich K, Reetz CA, Regules-Medel HD, Rehman A, Reidt F, Reme-Ness HA, Rescakova Z, Reygers K, Riabov A, Riabov V, Ricci R, Richter M, Riedel AA, Riegler W, Riffero AG, Ristea C, Rodriguez MV, Rodríguez Cahuantzi M, Rodríguez Ramírez SA, Røed K, Rogalev R, Rogochaya E, Rogoschinski TS, Rohr D, Röhrich D, Rojas PF, Rojas Torres S, Rokita PS, Romanenko G, Ronchetti F, Rosano A, Rosas ED, Roslon K, Rossi A, Roy A, Roy S, Rubini N, Ruggiano D, Rui R, Russek PG, Russo R, Rustamov A, Ryabinkin E, Ryabov Y, Rybicki A, Rytkonen H, Ryu J, Rzesa W, Saarimaki OAM, Sadhu S, Sadovsky S, Saetre J, Šafařík K, Saha P, Saha SK, Saha S, Sahoo B, Sahoo B, Sahoo R, Sahoo S, Sahu D, Sahu PK, Saini J, Sajdakova K, Sakai S, Salvan MP, Sambyal S, Samitz D, Sanna I, Saramela TB, Sarma P, Sarritzu V, Sarti VM, Sas MHP, Sawan S, Schambach J, Scheid HS, Schiaua C, Schicker R, Schlepper F, Schmah A, Schmidt C, Schmidt HR, Schmidt MO, Schmidt M, Schmidt NV, Schmier AR, Schotter R, Schröter A, Schukraft J, Schweda K, Scioli G, Scomparin E, Seger JE, Sekiguchi Y, Sekihata D, Selina M, Selyuzhenkov I, Senyukov S, Seo JJ, Serebryakov D, Šerkšnytė L, Sevcenco A, Shaba TJ, Shabetai A, Shahoyan R, Shangaraev A, Sharma A, Sharma B, Sharma D, Sharma H, Sharma M, Sharma S, Sharma S, Sharma U, Shatat A, Sheibani O, Shigaki K, Shimomura M, Shin J, Shirinkin S, Shou Q, Sibiriak Y, Siddhanta S, Siemiarczuk T, Silva TF, Silvermyr D, Simantathammakul T, Simeonov R, Singh B, Singh B, Singh K, Singh R, Singh R, Singh R, Singh S, Singh VK, Singhal V, Sinha T, Sitar B, Sitta M, Skaali TB, Skorodumovs G, Slupecki M, Smirnov N, Snellings RJM, Solheim EH, Song J, Sonnabend C, Soramel F, Soto-Hernandez AB, Spijkers R, Sputowska I, Staa J, Stachel J, Stan I, Steffanic PJ, Stiefelmaier SF, Stocco D, Storehaug I, Stratmann P, Strazzi S, Sturniolo A, Stylianidis CP, Suaide AAP, Suire C, Sukhanov M, Suljic M, Sultanov R, Sumberia V, Sumowidagdo S, Swain S, Szarka I, Szymkowski M, Taghavi SF, Taillepied G, Takahashi J, Tambave GJ, Tang S, Tang Z, Tapia Takaki JD, Tapus N, Tarasovicova LA, Tarzila MG, Tassielli GF, Tauro A, Tavira García A, Tejeda Muñoz G, Telesca A, Terlizzi L, Terrevoli C, Thakur S, Thomas D, Tikhonov A, Tiltmann N, Timmins AR, Tkacik M, Tkacik T, Toia A, Tokumoto R, Tomohiro K, Topilskaya N, Toppi M, Tork T, Torres VV, Torres Ramos AG, Trifiró A, Triolo AS, Tripathy S, Tripathy T, Trogolo S, Trubnikov V, Trzaska WH, Trzcinski TP, Tumkin A, Turrisi R, Tveter TS, Ullaland K, Ulukutlu B, Uras A, Usai GL, Vala M, Valle N, van Doremalen LVR, van Leeuwen M, van Veen CA, van Weelden RJG, Vande Vyvre P, Varga D, Varga Z, Vargas Torres P, Vasileiou M, Vasiliev A, Vázquez Doce O, Vazquez Rueda O, Vechernin V, Vercellin E, Vergara Limón S, Verma R, Vermunt L, Vértesi R, Verweij M, Vickovic L, Vilakazi Z, Villalobos Baillie O, Villani A, Vinogradov A, Virgili T, Virta MMO, Vislavicius V, Vodopyanov A, Volkel B, Völkl MA, Voloshin K, Voloshin SA, Volpe G, von Haller B, Vorobyev I, Vozniuk N, Vrláková J, Wan J, Wang C, Wang D, Wang Y, Wang Y, Wegrzynek A, Weiglhofer FT, Wenzel SC, Wessels JP, Wiechula J, Wikne J, Wilk G, Wilkinson J, Willems GA, Windelband B, Winn M, Wright JR, Wu W, Wu Y, Xu R, Yadav A, Yadav AK, Yalcin S, Yamaguchi Y, Yang S, Yano S, Yin Z, Yoo IK, Yoon JH, Yu H, Yuan S, Yuncu A, Zaccolo V, Zampolli C, Zanone F, Zardoshti N, Zarochentsev A, Závada P, Zaviyalov N, Zhalov M, Zhang B, Zhang C, Zhang L, Zhang S, Zhang X, Zhang Y, Zhang Z, Zhao M, Zherebchevskii V, Zhi Y, Zhou D, Zhou Y, Zhu J, Zhu Y, Zugravel SC, Zurlo N. Observation of Medium-Induced Yield Enhancement and Acoplanarity Broadening of Low-p_{T} Jets from Measurements in pp and Central Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV. PHYSICAL REVIEW LETTERS 2024; 133:022301. [PMID: 39073970 DOI: 10.1103/physrevlett.133.022301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 07/31/2024]
Abstract
The ALICE Collaboration reports the measurement of semi-inclusive distributions of charged-particle jets recoiling from a high transverse momentum (high p_{T}) hadron trigger in proton-proton and central Pb-Pb collisions at sqrt[s_{NN}]=5.02 TeV. A data-driven statistical method is used to mitigate the large uncorrelated background in central Pb-Pb collisions. Recoil jet distributions are reported for jet resolution parameter R=0.2, 0.4, and 0.5 in the range 7
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Ablikim M, Achasov MN, Adlarson P, Afedulidis O, Ai XC, Aliberti R, Amoroso A, An Q, Anderle D, 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, Che GR, Chelkov G, Chen C, Chen CH, Chen C, Chen G, Chen HS, Chen HY, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Chen ZY, Choi SK, Cibinetto G, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng CQ, 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 YY, Duan ZH, Egorov P, Fan YH, Fang J, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fritsch M, Fu CD, Fu JL, Fu YW, Gao H, Gao XB, Gao YN, Gao Y, Garbolino S, Garzia I, Ge L, 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, Hanisch F, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, 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 SL, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hölzken F, Hüsken N, Hüsken N, In der Wiesche N, Jackson J, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji W, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang D, Jiang HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao JK, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, 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, Kui X, Kumar N, Kupsc A, Kühn W, Lane JJ, Larin P, Lavezzi L, Lei TT, Lei ZH, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li K, Li LJ, Li LK, Li L, Li MH, Li MY, Li PR, Li QM, Li QX, Li R, Li SX, Li T, Li WD, Li WG, Li X, Li XH, Li XL, Li XZ, Li X, Li YG, Li ZJ, Li ZX, Li ZY, Liang C, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin CC, 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 X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZD, 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 T, Ma XT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, 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, Nie LS, 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, Qiao XK, Qin JJ, Qin LQ, Qin LY, Qin XS, Qin ZH, Qiu JF, Qu ZH, 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, Shang ZJ, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi H, Shi HC, Shi JL, Shi JY, Shi QQ, Shi SY, 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 ZQ, Sun ZT, Tang CJ, Tang GY, Tang J, Tang M, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian Y, Tian ZF, Uman I, Wan Y, Wang SJ, Wang B, Wang BL, Wang B, Wang DY, Wang F, Wang HJ, Wang JJ, 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 XN, 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 DH, Weidner F, Wen SP, Wen YR, 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 BH, 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 HX, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu M, 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, Yu YC, Yuan CZ, Yuan J, Yuan L, Yuan SC, Yuan Y, Yuan YJ, 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 H, Zhang HC, Zhang HH, Zhang HH, Zhang HQ, Zhang HR, Zhang HY, Zhang J, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JS, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang L, Zhang P, Zhang QY, Zhang RY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang YM, Zhang Y, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhang ZZ, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao N, Zhao RP, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng BM, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou JY, Zhou LP, Zhou S, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu KS, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WD, Zhu YC, Zhu ZA, Zou JH, Zu J. Measurements of Normalized Differential Cross Sections of Inclusive η Production in e^{+}e^{-} Annihilation at Energy from 2.0000 to 3.6710 GeV. PHYSICAL REVIEW LETTERS 2024; 133:021901. [PMID: 39073971 DOI: 10.1103/physrevlett.133.021901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/26/2024] [Accepted: 05/31/2024] [Indexed: 07/31/2024]
Abstract
Using data samples collected with the BESIII detector operating at the BEPCII storage ring, the cross section of the inclusive process e^{+}e^{-}→η+X, normalized by the total cross section of e^{+}e^{-}→hadrons, is measured at eight center-of-mass energy points from 2.0000 to 3.6710 GeV. These are the first measurements with momentum dependence in this energy region. Our measurement shows a significant discrepancy compared to the existing fragmentation functions. To address this discrepancy, a new QCD analysis is performed at the next-to-next-to-leading order with hadron mass corrections and higher twist effects, which can explain both the established high-energy data and our measurements reasonably well.
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Wang W, Zhang Y, Wu G, Zhao Z, Wu Y, Zheng H. Triboelectric-electromagnetic hybrid nanogenerator for harvesting blue energy and creating an ocean wave warning system. NANOSCALE ADVANCES 2024; 6:3566-3572. [PMID: 38989526 PMCID: PMC11232546 DOI: 10.1039/d4na00222a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/06/2024] [Indexed: 07/12/2024]
Abstract
The abundant water wave energy on Earth stands as one of the most promising renewable blue energy sources, as it exhibits minimal dependence on weather, time and temperature. However, the low fluctuation frequency and extremely irregular nature of the wave energy restrict both the methods and efficiency of energy harvesting. In this study, a packed box-like hybrid nanogenerator was designed, comprising two single-electrode triboelectric nanogenerators (TENGs) and two electromagnetic generators (EMGs). The outputs of both the TENG and EMG were demonstrated under different fluctuation frequencies and swing amplitudes, inspiring the development of a wave warning system. The maximum output voltage, current, and transferred charge of the single TENG, as part of hybrid nanogenerator (HG), reach approximately 110 V, 2.3 μA, and 50 nC, respectively. Its peak power reaches 85.3 μW under a resistance load of 20 MΩ at a frequency of 2 Hz. The EMG component produced maximum output voltages and currents of up to 0.45 V and 1.2 mA, respectively. The peak power is approximately 95.6 μW with a resistance load of 200 Ω. The output performances of the TENG and EMG increase linearly with the increase in the swing angle. Most importantly, a packed box-like hybrid nanogenerator can be conveniently packaged for harvesting energy from water waves. A wave energy collection array floating on the sea is proposed for harvesting blue energy and creating a self-powered ocean wave warning system.
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Xu J, Talankar S, Pan J, Harmon I, Wu Y, Fedele DA, Brailsford J, Fishe JN. Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study. JMIR Res Protoc 2024; 13:e57981. [PMID: 38976313 PMCID: PMC11263892 DOI: 10.2196/57981] [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: 03/05/2024] [Revised: 05/23/2024] [Accepted: 06/04/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice. OBJECTIVE The aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time. METHODS This mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma. RESULTS OneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project. CONCLUSIONS Pediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML to large, multicenter RWD data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/57981.
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Yang A, Lin L, Zhang J, Wu Y, Zhao Z. A novel role for endoplasmic reticulum protein ERp72 in the pathogenesis of autoantibody-induced arthritis. Scand J Rheumatol 2024:1-9. [PMID: 38975658 DOI: 10.1080/03009742.2024.2362040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 05/28/2024] [Indexed: 07/09/2024]
Abstract
OBJECTIVE The family of protein disulphide isomerases (PDIs) is a group of oxidoreductases that catalyze the oxidation, reduction and isomerization of disulphide bonds. Recent studies have shown that overexpression of one of the family enzymes, ERp46, potentiates arthritis severity, suggesting that the PDI family participates in arthritis pathogenesis. This study investigated the role of another PDI member, ERp72, in autoantibody-induced arthritis. METHODS Using the Cre-LoxP method, a mouse strain lacking ERp72 (ERp72-/- mice) was generated. Autoantibody-induced arthritis was induced in both ERp72-/- and ERp72+/+ control mice by injecting serum from K/BxN mice. The synovial inflammation severity was evaluated by joint diameter measurements and histological analysis. Proinflammatory cytokines expression in joint tissue and plasma was assessed by quantitative real-time PCR and ELISA. RESULTS : The absence of ERp72 in the joints, white blood cells, spleen, thymus, and bone marrow of ERp72-/- mice was confirmed. In the K/BxN serum transfer-induced arthritis (STIA) model, ERp72-/- mice exhibited exacerbated arthritis compared to ERp72+/+ mice, with greater joint swelling, bone and cartilage erosion, and synovial inflammation. Furthermore, ERp72-/- mice exhibited increased expression of IL-1β, IL-6 and TNF-α in inflamed joint tissues and higher IL-6 levels in plasma. Conversely, IL-10 levels were lower in ERp72-/- mice inflamed joints than in ERp72+/+ mice. Notably, the basal TNF-α level in the blood of ERp72-/- mice was significantly higher than in ERp72+/+ mice. CONCLUSION ERp72 plays a key role in the negative regulation of autoantibody-induced arthritis.
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Yang C, Li SY, Wu Y, Yang ZY, Feng JH, Chen N, Ma QY, Yang YX. [Analysis of myopia status and related factors among primary school students in a certain district of Beijing City in 2022]. ZHONGHUA YU FANG YI XUE ZA ZHI [CHINESE JOURNAL OF PREVENTIVE MEDICINE] 2024; 58:1029-1034. [PMID: 39034787 DOI: 10.3760/cma.j.cn112150-20240409-00290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Objective: To analyze the current situation of myopia and its related factors among primary school students in a certain district of Beijing City in 2022, and provide a basis for the risk assessment of myopia among primary school students. Method: In June 2022, a cluster sampling method was used to include 376 third-grade students from a primary school in a certain district of Beijing. A questionnaire survey was conducted to collect basic information about students, including eye usage habits, reading and writing postures, and parents' myopia conditions. The examination of students' distant visual acuity and refractive status was performed. A multivariate logistic regression model was used to analyze the related factors of myopia occurrence. Results: The age of 376 primary school students was (8.87±0.417) years old, with 48.40% (182) being male. A total of 196 myopia cases were identified, with a myopia rate of 52.13%. The results of the multivariate logistic regression model analysis showed that students who sometimes read while lying down (OR=2.003, 95%CI: 1.128-3.555), often read while lying down (OR=18.853, 95%CI: 4.512-78.778), had outdoor activity time less than 120 minutes per day (OR=4.937, 95%CI: 2.4464-9.892), were engaged in indoor break activities (OR=4.995, 95%CI: 2.773-8.996), performed eye exercises less than once per day (OR=8.710, 95%CI: 4.464-16.995), had a reading distance from the book less than 30 cm (OR=5.098, 95%CI: 2.410-10.787), occasionally maintained a fist distance from the edge of the desk (OR=1.918, 95%CI: 1.086-3.385), and had high school desks and tables (OR=5.325, 95%CI: 1.465-19.359) could have a higher risk of myopia occurrence, compared with those who never read while lying down, had outdoor activity time more than 120 minutes per day, maintained outdoor break activities, performed eye exercises more than once per day, had a reading distance from the book more than 30 cm, always maintained a fist distance from the edge of the desk, and had short school desks and tables. Conclusion: The incidence rate of myopia among primary school students in a certain district of Beijing City. in 2022 is relatively high. The occurrence of myopia is related to insufficient outdoor activity time and poor eye usage habits.
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Andreazza A, Angelidakis S, Angerami A, Anisenkov A, Annovi A, Antel C, Anthony M, Antipov E, Antonelli M, Anulli F, Aoki M, Aoki T, Aparisi Pozo J, Aparo M, Bella LA, Appelt C, Apyan A, Aranzabal N, Arcangeletti C, Arce A, Arena E, Arguin JF, Argyropoulos S, Arling JH, Arnaez O, Arnold H, Artoni G, Asada H, Asai K, Asai S, Asbah N, Assahsah J, Assamagan K, Astalos R, Atashi S, Atkin R, Atkinson M, Atmani H, Atmasiddha P, Augsten K, Auricchio S, Auriol A, Austrup V, Avolio G, Axiotis K, Azuelos G, Babal D, Bachacou H, Bachas K, Bachiu A, Backman F, Badea A, Bagnaia P, Bahmani M, Bailey A, Bailey V, Baines J, Baines L, Bakalis C, Baker O, Bakos E, Bakshi Gupta D, Balakrishnan V, Balasubramanian R, Baldin E, Balek P, Ballabene E, Balli F, Baltes L, Balunas W, Balz J, Banas E, Bandieramonte M, Bandyopadhyay A, Bansal S, Barak L, Barakat M, Barberio E, Barberis D, Barbero M, Barends K, Barillari T, Barisits MS, Barklow T, Baron P, Baron Moreno D, Baroncelli A, Barone G, Barr A, Barr J, Barranco Navarro L, Barreiro F, da Costa JBG, Barron U, Barros Teixeira M, Barsov S, Bartels F, Bartoldus R, Barton A, Bartos P, Basan A, Baselga M, Bassalat A, Basso M, Basson C, Bates R, Batlamous S, Batley J, Batool B, Battaglia M, Battulga D, Bauce M, Bauer M, Bauer P, Bazzano Hurrell L, Beacham J, Beau T, Beauchemin P, Becherer F, Bechtle P, Beck H, Becker K, Beddall A, Bednyakov V, Bee C, Beemster L, Beermann T, Begalli M, Begel M, Behera A, Behr J, Beirer J, Beisiegel F, Belfkir M, Bella G, Bellagamba L, Bellerive A, Bellos P, Beloborodov K, Belyaev N, Benchekroun D, Bendebba F, Benhammou Y, Benoit M, Bensinger J, Bentvelsen S, Beresford L, Beretta M, Bergeaas Kuutmann E, Berger N, Bergmann B, Beringer J, Bernardi G, Bernius C, Bernlochner F, Bernon F, Berry T, Berta P, Berthold A, Bertram I, Bethke S, Betti A, Bevan A, Bhamjee M, Bhatta S, Bhattacharya D, Bhattarai P, Bhopatkar V, Bi R, Bianchi R, Bianco G, Biebel O, Bielski R, Biglietti M, Billoud T, Bindi M, Bingul A, Bini C, Biondini A, Birch-sykes C, Bird G, Birman M, Biros M, Bisanz T, Bisceglie E, Biswas D, Bitadze A, Bjørke K, Bloch I, Blocker C, Blue A, Blumenschein U, Blumenthal J, Bobbink G, Bobrovnikov V, Boehler M, Boehm B, Bogavac D, Bogdanchikov A, Bohm C, Boisvert V, Bokan P, Bold T, Bomben M, Bona M, Boonekamp M, Booth C, Borbély A, Bordulev I, Borecka-Bielska H, Borgna L, Borissov G, Bortoletto D, Boscherini D, Bosman M, Bossio Sola J, Bouaouda K, Bouchhar N, Boudreau J, Bouhova-Thacker E, Boumediene D, Bouquet R, Boveia A, Boyd J, Boye D, Boyko I, Bracinik J, Brahimi N, Brandt G, Brandt O, Braren F, Brau B, Brau J, Brener R, Brenner L, Brenner R, Bressler S, Britton D, Britzger D, Brock I, Brooijmans G, Brooks W, Brost E, Brown L, Bruce L, Bruckler T, de Renstrom PB, Brüers B, Bruni A, Bruni G, Bruschi M, Bruscino N, Buanes T, Buat Q, Buchin D, Buckley A, Bugge M, Bulekov O, Bullard B, Burdin S, Burgard C, Burger A, Burghgrave B, Burlayenko O, Burr J, Burton C, Burzynski J, Busch E, Büscher V, Bussey P, Butler J, Buttar C, Butterworth J, Buttinger W, Buxo Vazquez C, Buzykaev A, Cabrera Urbán S, Cadamuro L, Caforio D, Cai H, Cai Y, Cairo V, Cakir O, Calace N, Calafiura P, Calderini G, Calfayan P, Callea G, Caloba L, Calvet D, Calvet S, Calvet T, Calvetti M, Camacho Toro R, Camarda S, Camarero Munoz D, Camarri P, Camerlingo M, 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 J, Cardillo F, Carli T, Carlino G, Carlotto J, Carlson B, Carlson E, Carminati L, Carnelli A, Carnesale M, Caron S, Carquin E, Carrá S, Carratta G, Carrio Argos F, Carter J, Carter T, Casado M, Caspar M, Castiglia E, Castillo F, Castillo Garcia L, Castillo Gimenez V, Castro N, Catinaccio A, Catmore J, Cavaliere V, Cavalli N, Cavasinni V, Cekmecelioglu Y, Celebi E, Celli F, Centonze M, Cepaitis V, Cerny K, Cerqueira A, Cerri A, Cerrito L, Cerutti F, Cervato B, Cervelli A, Cesarini G, Cetin S, Chadi Z, Chakraborty D, Chan J, Chan W, Chapman J, Chapon E, Chargeishvili B, Charlton D, Charman T, Chatterjee M, Chauhan C, Chekanov S, Chekulaev S, Chelkov G, Chen A, Chen B, Chen B, Chen H, Chen H, Chen J, Chen J, Chen M, Chen S, Chen S, Chen X, Chen X, Chen Y, Cheng C, Cheng H, Cheong S, Cheplakov A, Cheremushkina E, Cherepanova E, El Moursli RC, Cheu E, Cheung K, Chevalier L, Chiarella V, Chiarelli G, Chiedde N, Chiodini G, Chisholm A, Chitan A, Chitishvili M, Chizhov M, Choi K, Chomont A, Chou Y, Chow E, Chowdhury T, Chu K, Chu M, Chu X, Chudoba J, Chwastowski J, Cieri D, Ciesla K, Cindro V, Ciocio A, Cirotto F, Citron Z, Citterio M, Ciubotaru D, Ciungu B, Clark A, Clark P, Clavijo Columbie J, Clawson S, Clement C, Clercx J, Clissa L, Coadou Y, Cobal M, Coccaro A, Coelho Barrue R, De Sa RCL, Coelli S, Cohen H, Coimbra A, Cole B, Collot J, Conde Muiño P, Connell M, Connell S, Connelly I, Conroy E, Conventi F, Cooke H, Cooper-Sarkar A, Cordeiro Oudot Choi A, Cormier F, Corpe L, Corradi M, Corriveau F, Cortes-Gonzalez A, Costa M, Costanza F, Costanzo D, Cote B, Cowan G, Cranmer K, Cremonini D, Crépé-Renaudin S, Crescioli F, Cristinziani M, Cristoforetti M, Croft V, Crosby J, Crosetti G, Cueto A, Cuhadar Donszelmann T, Cui H, Cui Z, Cunningham W, Curcio F, Czodrowski P, Czurylo M, De Sousa MDCS, Pinto JDF, Da Via C, Dabrowski W, Dado T, Dahbi S, Dai T, Santo DD, Dallapiccola C, Dam M, D’amen G, D’Amico V, Damp J, Dandoy J, Daneri M, Danninger M, Dao V, Darbo G, Darmora S, Das S, D’Auria S, David C, Davidek T, Davis-Purcell B, Dawson I, Day-hall H, 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 Santo A, De Regie JDV, Dedovich D, Degens J, Deiana A, Del Corso F, Del Peso J, Del Rio F, Deliot F, Delitzsch C, Della Pietra M, Della Volpe D, Dell’Acqua A, Dell’Asta L, Delmastro M, Delsart P, Demers S, Demichev M, Denisov S, D’Eramo L, Derendarz D, Derue F, Dervan P, Desch K, Deutsch C, Di Bello F, 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 F, Do Vale TD, Diaz M, Diaz Capriles F, Didenko M, Diehl E, Diehl L, Díez Cornell S, Diez Pardos C, Dimitriadi C, Dimitrievska A, Dingfelder J, Dinu IM, Dittmeier S, Dittus F, Djama F, Djobava T, Djuvsland J, Doglioni C, Dohnalova A, Dolejsi J, Dolezal Z, Donadelli M, Dong B, Donini J, D’Onofrio A, D’Onofrio M, Dopke J, Doria A, Dos Santos Fernandes N, Dougan P, Dova M, Doyle A, Draguet M, Dreyer E, Drivas-koulouris I, Drobac A, Drozdova M, Du D, du Pree T, Dubinin F, Dubovsky M, Duchovni E, Duckeck G, Ducu O, Duda D, Dudarev A, Duden E, D’uffizi M, Duflot L, Dührssen M, Dülsen C, Dumitriu A, Dunford M, Dungs S, Dunne K, Duperrin A, Duran Yildiz H, Düren M, Durglishvili A, Dwyer B, Dyckes G, Dyndal M, Dysch S, Dziedzic B, Earnshaw Z, Eberwein G, Eckerova B, Eggebrecht S, De Souza EEP, Ehrke L, Eigen G, Einsweiler K, Ekelof T, Ekman P, El Farkh S, El Ghazali Y, El Jarrari H, El Moussaouy A, Ellajosyula V, Ellert M, Ellinghaus F, Elliot A, Ellis N, Elmsheuser J, Elsing M, Emeliyanov D, Enari Y, Ene I, Epari S, Erdmann J, Erland P, Errenst M, Escalier M, Escobar C, Etzion E, Evans G, Evans H, Evans L, Evans M, Ezhilov A, Ezzarqtouni S, Fabbri F, Fabbri L, Facini G, Fadeyev V, Fakhrutdinov R, Falciano S, Falda Ulhoa Coelho L, Falke P, Faltova J, Fan C, Fan Y, Fang Y, Fanti M, Faraj M, Farazpay Z, Farbin A, Farilla A, Farooque T, Farrington S, Fassi F, Fassouliotis D, Faucci Giannelli M, Fawcett W, Fayard L, Federic P, Federicova P, Fedin O, Fedotov G, Feickert M, Feligioni L, Fellers D, Feng C, Feng M, Feng Z, Fenton M, Fenyuk A, Ferencz L, Ferguson R, Fernandez Luengo S, Fernoux M, Ferrando J, Ferrari A, Ferrari P, Ferrari R, Ferrere D, Ferretti C, Fiedler F, Filipčič A, Filmer E, Filthaut F, Fiolhais M, Fiorini L, Fisher W, Fitschen T, Fitzhugh P, Fleck I, Fleischmann P, Flick T, Flores M, Flores Castillo L, De Acedo LFS, Follega F, Fomin N, Foo J, Forland B, Formica A, Forti A, Fortin E, Fortman A, Foti M, Fountas L, Fournier D, Fox H, Francavilla P, Francescato S, Franchellucci S, Franchini M, Franchino S, Francis D, Franco L, Franconi L, Franklin M, Frattari G, Freegard A, Freund W, Frid Y, Friend J, Fritzsche N, Froch A, Froidevaux D, Frost J, Fu Y, Fujimoto M, Fullana Torregrosa E, Fung K, Furtado De Simas Filho E, Furukawa M, Fuster J, Gabrielli A, Gabrielli A, Gadow P, Gagliardi G, Gagnon L, Gallas E, Gallop B, Gan K, Ganguly S, Gao J, Gao Y, Garay Walls F, Garcia B, García C, Garcia Alonso A, Garcia Caffaro A, García Navarro J, Garcia-Sciveres M, Gardner G, Gardner R, Garelli N, Garg D, Garg R, Gargan J, Garner C, Gasiorowski S, Gaspar P, Gaudio G, Gautam V, Gauzzi P, Gavrilenko I, Gavrilyuk A, Gay C, Gaycken G, Gazis E, Geanta A, Gee C, Gemme C, Genest M, Gentile S, George S, George W, Geralis T, Gessinger-Befurt P, Geyik M, Ghani M, Ghneimat M, Ghorbanian K, Ghosal A, Ghosh A, Ghosh A, Giacobbe B, Giagu S, Giani T, Giannetti P, Giannini A, Gibson S, Gignac M, Gil D, Gilbert A, Gilbert B, Gillberg D, Gilles G, Gillwald N, Ginabat L, Gingrich D, Giordani M, Giraud P, Giugliarelli G, Giugni D, Giuli F, Gkialas I, Gladilin L, Glasman C, Gledhill G, Glemža G, Glisic M, Gnesi I, Go Y, Goblirsch-Kolb M, Gocke B, Godin D, Gokturk B, Goldfarb S, Golling T, Gololo M, Golubkov D, Gombas J, Gomes A, Da Silva GG, Gomez Delegido A, Gonçalo R, Gonella G, Gonella L, Gongadze A, Gonnella F, Gonski J, González Andana R, de la Hoz SG, Gonzalez Fernandez S, Gonzalez Lopez R, Gonzalez Renteria C, Gonzalez Rodrigues M, Gonzalez Suarez R, Gonzalez-Sevilla S, Gonzalvo Rodriguez G, Goossens L, Gorini B, Gorini E, Gorišek A, Gosart T, Goshaw A, Gostkin M, Goswami S, Gottardo C, Gotz S, Gouighri M, Goumarre V, Goussiou A, Govender N, Grabowska-Bold I, Graham K, Gramstad E, Grancagnolo S, Grandi M, Grant C, Gravila P, Gravili F, Gray H, Greco M, Grefe C, Gregor I, Grenier P, Grieco C, Grillo A, Grimm K, Grinstein S, Grivaz JF, Gross E, Grosse-Knetter J, Grud C, Grundy J, Guan L, Guan W, Gubbels C, Guerrero Rojas J, Guerrieri G, Guescini F, Gugel R, Guhit J, Guida A, Guillemin T, Guilloton E, Guindon S, Guo F, Guo J, Guo L, Guo Y, Gupta R, Gurbuz S, Gurdasani S, Gustavino G, Guth M, Gutierrez P, Gutierrez Zagazeta L, Gutschow C, Gwenlan C, Gwilliam C, Haaland E, Haas A, Habedank M, Haber C, Hadavand H, Hadef A, Hadzic S, Hahn J, Haines E, Haleem M, Haley J, Hall J, Hallewell G, Halser L, Hamano K, Hamer M, Hamity G, Hampshire E, Han J, Han K, Han L, Han L, Han S, Han Y, Hanagaki K, Hance M, Hangal D, Hanif H, Hank M, Hankache R, Hansen J, Hansen J, Hansen P, Hara K, Harada D, Harenberg T, Harkusha S, Harris M, Harris Y, Harrison J, Harrison N, Harrison P, Hartman N, Hartmann N, Hasegawa Y, Hasib A, Haug S, Hauser R, Hawkes C, Hawkings R, Hayashi Y, Hayashida S, Hayden D, Hayes C, Hayes R, Hays C, Hays J, Hayward H, He F, He M, He Y, He Y, Heatley N, Hedberg V, Heggelund A, 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Vannoli L, Vari R, Varnes E, Varni C, Varol T, Varouchas D, Varriale L, Varvell K, Vasile M, Vaslin L, Vasquez G, Vasyukov A, Vazeille F, Schroeder TV, Veatch J, Vecchio V, Veen M, Veliscek I, Veloce L, Veloso F, Veneziano S, Ventura A, Gonzalez SV, Verbytskyi A, Verducci M, Vergis C, De Araujo MV, Verkerke W, Vermeulen J, Vernieri C, Vessella M, Vetterli M, Vgenopoulos A, Maira NV, Vickey T, Boeriu OV, Viehhauser G, Vigani L, Villa M, Perez MV, Villhauer E, Vilucchi E, Vincter M, Virdee G, Vishwakarma A, Visibile A, Vittori C, Vivarelli I, Vladimirov V, Voevodina E, Vogel F, Vokac P, Volkotrub Y, Von Ahnen J, Von Toerne E, Vormwald B, Vorobel V, Vorobev K, Vos M, Voss K, Vossebeld J, Vozak M, Vozdecky L, Vranjes N, Milosavljevic MV, Vreeswijk M, Vu N, Vuillermet R, Vujinovic O, Vukotic I, Wada S, Wagner C, Wagner J, Wagner W, Wahdan S, Wahlberg H, Wakida M, Walder J, Walker R, Walkowiak W, Wall A, Wamorkar T, Wang A, Wang C, Wang C, Wang H, Wang J, Wang RJ, Wang R, Wang R, Wang S, Wang S, Wang T, Wang W, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Warburton A, Ward R, Warrack N, Watson A, Watson H, Watson M, Watton E, Watts G, Waugh B, Weber C, Weber H, Weber M, Weber S, Wei C, Wei Y, Weidberg A, Weik E, Weingarten J, Weirich M, Weiser C, Wells C, Wenaus T, Wendland B, Wengler T, Wenke N, Wermes N, Wessels M, Wharton A, White A, White A, White M, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wiglesworth C, Wilbern D, Wilkens H, Williams D, Williams H, Williams S, Willocq S, Wilson B, Windischhofer P, Winkel F, Winklmeier F, Winter B, Winter J, Wittgen M, Wobisch M, Wolffs Z, Wölker R, Wollrath J, Wolter M, Wolters H, Wongel A, Worm S, Wosiek B, Woźniak K, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu S, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt T, Wynne B, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, 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 H, Yang H, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap Y, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo B, Yexley M, Yin P, Yorita K, Younas S, Young C, Young C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Saa JZ, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng J, Zenger D, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang D, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, 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 C, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine N, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas T, Zormpa O, Zou W, Zwalinski L. Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb -1 of s=13 TeV pp collision data. Sci Bull (Beijing) 2024:S2095-9273(24)00399-2. [PMID: 39179456 DOI: 10.1016/j.scib.2024.06.003] [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: 06/13/2023] [Revised: 07/03/2023] [Accepted: 05/27/2024] [Indexed: 08/26/2024]
Abstract
Results from a wide range of searches targeting different experimental signatures with and without missing transverse momentum (ETmiss) are used to constrain a Two-Higgs-Doublet Model (2HDM) with an additional pseudo-scalar mediating the interaction between ordinary and dark matter (2HDM+a). The analyses use up to 139 fb-1 of proton-proton collision data at a centre-of-mass energy s=13 TeV recorded with the ATLAS detector at the Large Hadron Collider during 2015-2018. The results from three of the most sensitive searches are combined statistically. These searches target signatures with large ETmiss and a leptonically decaying Z boson; large ETmiss and a Higgs boson decaying to bottom quarks; and production of charged Higgs bosons in final states with top and bottom quarks, respectively. Constraints are derived for several common and new benchmark scenarios in the 2HDM+a.
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Li SR, Chang XT, Zhang J, Teng XL, Wu Y. [Related factors of acute symptomatic seizures and epilepsy in children with myelin oligodendrocyte glycoprotein antibody-associated disease]. ZHONGHUA ER KE ZA ZHI = CHINESE JOURNAL OF PEDIATRICS 2024; 62:655-660. [PMID: 38955684 DOI: 10.3760/cma.j.cn112140-20231115-00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Objective: To investigate the risk factors of acute symptomatic seizures (ASS) and epilepsy in children with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Methods: A ambispective cohort study was used including 74 children with MOGAD who were admitted to the Department of Pediatrics of Peking University First Hospital from January 2013 to June 2023 and were followed up. Demographic information, clinical information, treatment status, ASS and epilepsy status were collected. The clinical phenotypes were classified. According to the presence or absence of ASS in the course of disease, the children and the course of disease were divided into groups with and without ASS. Chi-square test, Fisher exact test and Mann Whitney U test were used to analyze the correlation between symptoms and auxiliary examination characteristics and the occurrence of ASS in the two groups of children. Multivariate Logistic regression analysis was used for multivariate analysis. Results: The onset age of the 74 children with MOGAD was 6.58 (3.80, 9.67) years, including 38 females (51.4%) and 36 males (48.6%). The duration of the final follow-up was 2.67 (1.10, 4.12) years, with a total of 239 times acute clinical episodes. ASS occurred in 39.2% (29/74) children during the course of disease and in 29.3% (70/239) of attacks. The common phenotypes were ADEM (67 times (28.0%)), optic neuritis (37 times (15.4%)) and cerebral cortical encephalitis (31 times (13.0%)) in 239 times acute clinical episodes. The incidence of ASS in ADEM and cerebral cortical encephalitis phenotype was 28.4%(19/67) and 100.0% (31/31), respectively. Multivariate analysis showed that cortical involvement on magnetic resonance imaging during clinical attacks was an independent risk factor for ASS (β=-1.49, OR=0.23) after excluding attacks involving only optic nerve or spinal cord (49 episodes). During the follow-up, 5 children (6.8%) had epilepsy, and all children with epilepsy had multiple clinical attacks of MOGAD and previous ASS. Conclusions: Cortical involvement on magnetic resonance imaging during clinical episodes is an independent risk factor for ASS in children with MOGAD. All MOGAD children with epilepsy had ASS and multiple MOGAD clinical episodes in the past.
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He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12613. [PMID: 38966622 PMCID: PMC11220631 DOI: 10.1002/dad2.12613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/13/2024] [Accepted: 05/18/2024] [Indexed: 07/06/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.
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Mu J, Wu C, Ouyang L, Yang Y, Wu Y, Jin B. Evaluation of residual bone mass of the mandibular third molar and the risk of mandibular fracture. Med Oral Patol Oral Cir Bucal 2024; 29:e537-e544. [PMID: 38794938 PMCID: PMC11249378 DOI: 10.4317/medoral.26520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND A quantification of the residual bone mass of the mandible (B/A) was utilized in this study to examine the correlation between mandibular fracture and residual bone mass. To improve the clinical utilization rate and reduce the incidence of iatrogenic mandibular fractures, the B/A ratio calculation should be simplified. MATERIAL AND METHODS Data were collected from the Yanbian University Hospital on 175 cases of mandibular fracture with third molar (M3), 67 normal cases without fractures and 20 cases of impacted teeth extraction. Twenty cases of iatrogenic mandibular fracture were collected, and the case records and panoramic radiographs of the patients were recorded. RESULTS The average B/A ratio of mandibular angle fracture group was 0.61±0.10.The value of B/A was found to be statistically significant in terms of whether M3 emerged from alveolar bone (P = 0.001), location (horizontal P < 0.001, vertical P < 0.001), the degree of impaction (P < 0.001), the number of roots (P < 0.001), the difference in impaction (P < 0.001), and the fracture type (P = 0.002). The average B/A ratio of normal group was 0.62±0.10. In the statistical results of the B/A value of normal patients, M3 involving alveolar bone (P < 0.001), position classification (P < 0.05), degree of impaction (P < 0.001) and presence or absence of a root (P < 0.05) were statistically significant. The average B/A ratio of iatrogenic mandibular angle fracture group was 0.28±0.08. The average B/A ratio of the extraction group for impacted teeth was 0.62 ± 0.09. CONCLUSIONS There is a high risk of mandibular angle fracture when the (B/A) value of the residual bone height (B) in the mandibular M3 area compared to the mandibular bone height (A) in the M3 area is less than 0.4.
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Wu Z, Jia X, Lu L, Xu C, Pang Y, Peng S, Liu M, Wu Y. Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024; 36:e209-e223. [PMID: 38631974 DOI: 10.1016/j.clon.2024.03.022] [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: 11/01/2023] [Revised: 02/22/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
AIMS Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. MATERIALS AND METHODS We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. RESULTS The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. CONCLUSION AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.
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Aguilar M, Alpat B, Ambrosi G, Anderson H, Arruda L, Attig N, Bagwell C, Barao F, Barbanera M, Barrin L, Bartoloni A, Battiston R, Bayyari A, Belyaev N, Bertucci B, Bindi V, Bollweg K, Bolster J, Borchiellini M, Borgia B, Boschini MJ, Bourquin M, Brugnoni C, Burger J, Burger WJ, Cai XD, Capell M, Casaus J, Castellini G, Cervelli F, Chang YH, Chen GM, Chen GR, Chen H, Chen HS, Chen Y, Cheng L, Chou HY, Chouridou S, Choutko V, Chung CH, Clark C, Coignet G, Consolandi C, Contin A, Corti C, Cui Z, Dadzie K, D'Angelo F, Dass A, Delgado C, Della Torre S, Demirköz MB, Derome L, Di Falco S, Di Felice V, Díaz C, Dimiccoli F, von Doetinchem P, Dong F, Donnini F, Duranti M, Egorov A, Eline A, Faldi F, Fehr D, Feng J, Fiandrini E, Fisher P, Formato V, Gámez C, García-López RJ, Gargiulo C, Gast H, Gervasi M, Giovacchini F, Gómez-Coral DM, Gong J, Grandi D, Graziani M, Guracho AN, Haino S, Han KC, Hashmani RK, He ZH, Heber B, Hsieh TH, Hu JY, Huang BW, Ionica M, Incagli M, Jia Y, Jinchi H, Karagöz G, Khan S, Khiali B, Kirn T, Klipfel AP, Kounina O, Kounine A, Koutsenko V, Krasnopevtsev D, Kuhlman A, Kulemzin A, La Vacca G, Laudi E, Laurenti G, LaVecchia G, Lazzizzera I, Lee HT, Lee SC, Li HL, Li JQ, Li M, Li M, Li Q, Li Q, Li QY, Li S, Li SL, Li JH, Li ZH, Liang MJ, Liao P, Lin CH, Lippert T, Liu JH, Liu PC, Lu SQ, Lu YS, Luebelsmeyer K, Luo JZ, Luo Q, Luo SD, Luo X, Mañá C, Marín J, Marquardt J, Martínez G, Masi N, Maurin D, Medvedeva T, Menchaca-Rocha A, Meng Q, Mikhailov VV, Molero M, Mott P, Mussolin L, Jozani YN, Nicolaidis R, Nikonov N, Nozzoli F, Ocampo-Peleteiro J, Oliva A, Orcinha M, Palmonari F, Paniccia M, Pashnin A, Pauluzzi M, Pensotti S, Pietzcker P, Plyaskin V, Poluianov S, Pridöhl D, Qu ZY, Quadrani L, Rancoita PG, Rapin D, Conde AR, Robyn E, Rodríguez-García I, Romaneehsen L, Rossi F, Rozhkov A, Rozza D, Sagdeev R, Savin E, Schael S, Schultz von Dratzig A, Schwering G, Seo ES, Shan BS, Shukla A, Siedenburg T, Silvestre G, Song JW, Song XJ, Sonnabend R, Strigari L, Su T, Sun Q, Sun ZT, Tabarroni L, Tacconi M, Tang ZC, Tian J, Tian Y, Ting SCC, Ting SM, Tomassetti N, Torsti J, Urban T, Usoskin I, Vagelli V, Vainio R, Valencia-Otero M, Valente E, Valtonen E, Vázquez Acosta M, Vecchi M, Velasco M, Wang CX, Wang L, Wang LQ, Wang NH, Wang QL, Wang S, Wang X, Wang ZM, Wei J, Weng ZL, Wu H, Wu Y, Wu ZB, Xiao JN, Xiong RQ, Xiong XZ, Xu W, Yan Q, Yang HT, Yang Y, Yelland A, Yi H, You YH, Yu YM, Yu ZQ, Zhang C, Zhang FZ, Zhang J, Zhang JH, Zhang Z, Zhao PW, Zheng C, Zheng ZM, Zhuang HL, Zhukov V, Zichichi A, Zuccon P. Properties of Cosmic Deuterons Measured by the Alpha Magnetic Spectrometer. PHYSICAL REVIEW LETTERS 2024; 132:261001. [PMID: 38996294 DOI: 10.1103/physrevlett.132.261001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/22/2024] [Accepted: 05/06/2024] [Indexed: 07/14/2024]
Abstract
Precision measurements by the Alpha Magnetic Spectrometer (AMS) on the International Space Station of the deuteron (D) flux are presented. The measurements are based on 21×10^{6} D nuclei in the rigidity range from 1.9 to 21 GV collected from May 2011 to April 2021. We observe that over the entire rigidity range the D flux exhibits nearly identical time variations with the p, ^{3}He, and ^{4}He fluxes. Above 4.5 GV, the D/^{4}He flux ratio is time independent and its rigidity dependence is well described by a single power law ∝R^{Δ} with Δ_{D/^{4}He}=-0.108±0.005. This is in contrast with the ^{3}He/^{4}He flux ratio for which we find Δ_{^{3}He/^{4}He}=-0.289±0.003. Above ∼13 GV we find a nearly identical rigidity dependence of the D and p fluxes with a D/p flux ratio of 0.027±0.001. These unexpected observations indicate that cosmic deuterons have a sizable primarylike component. With a method independent of cosmic ray propagation, we obtain the primary component of the D flux equal to 9.4±0.5% of the ^{4}He flux and the secondary component of the D flux equal to 58±5% of the ^{3}He flux.
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