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Zang F, Liu J, Wen Y, Jin X, Yang Y, Li L, Di J, Tang H, Wu J, Liu J, Liu H, Huang J, Zhang J, Li S, Yang L, Wang X, Geng S, Xing H, Xie J, Hua J, Xue X, Zhao Y, Ouyang L, Song P, Zhuang G, Chen W. Adherence to guidelines and central-line-associated bloodstream infection occurrence during insertion and maintenance of intravascular catheters: evidence from 20 tertiary hospitals. J Hosp Infect 2024; 150:17-25. [PMID: 38838743 DOI: 10.1016/j.jhin.2024.05.011] [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: 01/17/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVE To investigate adherence to intravascular catheter (IVC) insertion and maintenance guidelines in Chinese tertiary hospitals. METHODS A cross-sectional questionnaire survey of adult inpatients with IVC placements was conducted from July to September 2022 in 20 tertiary hospitals in China. One clinical staff member from each department in each hospital was assigned to participate in the survey. Questionnaires were uniformly collected and reviewed after three months. RESULTS This study included 1815 cases (62.69%) of central venous catheter, 471 cases (16.27%) of peripherally inserted central catheter, 461 cases (15.92%) of PORT, and 147 cases (5.08%) of haemodialysis catheter insertions. Statistically significant differences in compliance were observed across the four IVC types, specifically in relation to the insertion checklist, standard operating procedure, and insertion environment (P<0.05). Practice adherence during IVC maintenance differed significantly across the four IVC types in aspects such as availability of IVC maintenance verification forms, daily scrubbing of the catheterized patients, and catheter connection methods (P<0.05). A total of 386 (13.34%) patients developed fever, 1086 (37.53%) were treated with therapeutic antibiotics, 16 (0.55%) developed central-line-associated bloodstream infections, two (0.07%) developed local skin infections, and six (0.21%) developed deep vein thrombosis. CONCLUSIONS Adherence to guidelines regarding insertion and maintenance differed across the four IVC types; there is a gap between the recommended measures and the actual operation of the guidelines. Therefore, it is necessary to further enhance training and develop checklists to prevent central-line-associated bloodstream infections.
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Shen Z, Zhang T, Twumasi G, Zhang J, Wang J, Xi Y, Wang R, Wang J, Zhang R, Liu H. Genetic analysis of a Kaijiang duck conservation population through genome-wide scan. Br Poult Sci 2024; 65:378-386. [PMID: 38738932 DOI: 10.1080/00071668.2024.2335937] [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: 09/02/2023] [Accepted: 03/08/2024] [Indexed: 05/14/2024]
Abstract
1. The Kaijiang duck is a native Chinese breed known for its excellent egg laying performance, killing-out percentage (88.57%), and disease resistance. The assessment of population genetic structure is the basis for understanding the genetics of indigenous breeds and for their protection and management.2. In this study, whole-genome sequencing was performed on 60 Kaijiang ducks to identify genetic variations and investigate the population structure. Homozygosity (ROH) analysis was conducted to assess inbreeding levels in the population.3. The study revealed a moderate level of inbreeding, indicated by an average inbreeding coefficient of 0.1043. This may impact the overall genetic diversity.4. Genomic Regions of Interest identified included 168 genomic regions exhibiting high levels of autozygosity. These regions were associated with processes including muscle growth, pigmentation, neuromodulation, and growth and reproduction.5. The significance of these pathways indicated their potential role in shaping the desirable traits of the Kaijiang duck. These findings provide insights into the genetic basis of the Kaijiang duck's desirable traits and can inform future breeding and conservation efforts.
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Gong K, Guo C, Guo W, Jiang L, Liu H. Research on Tremor Suppression Strategies Under a Constant Current Peripheral Electrical Stimulation Device for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2024; PP:1-1. [PMID: 39078766 DOI: 10.1109/tnsre.2024.3435749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
Tremor, a prevalent symptom in Parkinson's patients, is conventionally treated with medications and craniotomy. However, the associated side effects and high surgical costs pose challenges for some individuals. In this study, a lightweight constant current electrical stimulator was developed, which is driven by the FPGA to control the underlying logic and has multiple programmable stimulation parameters. Clinical experiments involving patients with Parkinson's-related resting tremor symptoms were conducted to assess the efficacy of peripheral electrical stimulation. Two Co-contraction Avoidance Stimulation (CAS) strategies targeting nerves and muscles were proposed to reduce tremors. Four Parkinson's disease (PD) patients were recruited to verify the effectiveness of these strategies. Kinematic data recorded by inertial sensors showed that the radial nerve and muscle intervention strategies reduced the average angular velocity amplitude of finger joints during resting tremor by 75.92% and 82.41%, respectively. Notably, under low-frequency pulse stimulation (100 Hz) focused on muscle interference, a low-intensity current of no more than 8 mA maintained a tremor suppression rate of 59.91% even 5 minutes post-stimulation. Based on the experimental results, it is concluded that the constant current electrical stimulator developed in this study can effectively suppress tremor under specific stimulation strategies. These findings have significant implications for the development of lightweight, wearable tremor suppression devices. The stimulator's adaptability, coupled with its precise control parameters, demonstrates promise for advancing non-invasive and cost-effective tremor management in Parkinson's patients.
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Chen XH, Lin HX, Chen YH, Wang XD, Liu CQ, Huang HL, Liang HY, Zhang HM, Li FP, Liu H, Hu YF, Li GX, You J, Zhao LY, Yu J. [Effect of preoperative immune checkpoint inhibitors on reducing residual lymph node metastases in patients with gastric cancer: a retrospective study]. ZHONGHUA WEI CHANG WAI KE ZA ZHI = CHINESE JOURNAL OF GASTROINTESTINAL SURGERY 2024; 27:694-701. [PMID: 39004984 DOI: 10.3760/cma.j.cn441530-20240513-00176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Objective: To investigate the effect of immune checkpoint inhibitors on reducing residual lymph node metastasis in patients with gastric cancer. Methods: The cohort of this retrospective study comprised patients from Nanfang Hospital of Southern Medical University and the First Affiliated Hospital of Xiamen University who had undergone systemic treatment prior to gastrectomy with D2 lymphadenectomy and had achieved Grade 1 primary tumor regression (TRG1) from January 2014 to December 2023. After exclusion of patients who had undergone preoperative radiotherapy, data of 58 patients (Nanfang Hospital: 46; First Affiliated Hospital of Xiamen University: 12) were analyzed. These patients were allocated to preoperative chemotherapy (Chemotherapy group, N=36 cases) and preoperative immunotherapy plus chemotherapy groups (Immunotherapy group, N=22 cases). There were no significant differences between these groups in sex, age, body mass index, diabetes, tumor location, pathological type, Lauren classification, tumor differentiation, pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, mismatch repair protein status, number of preoperative treatment cycles, or duration of preoperative treatment (all P>0.05). The primary outcome measure was postoperative lymph node downstaging. Secondary outcomes included postoperative depth of invasion by tumor, number of lymph nodes examined, and factors affecting residual lymph node metastasis status. Results: Lymph node downstaging was achieved significantly more often in the Immunotherapy group than the Chemotherapy group (pN0: 90.9% [20/22] vs. 61.1% [22/36]; pN1: 4.5% [1/22] vs. 36.1% [13/36]; pN2: 4.5% [1/22) vs. 0; pN3: 0 vs. 2.8% [1/36], Z=-2.315, P=0.021). There were no significant difference between the two groups in number of lymph nodes examined (40.5±16.3 vs. 40.8±17.5, t=0.076, P=0.940) or postoperative depth of invasion by primary tumor (pT1a: 50.0% [11/22] vs. 30.6% [11/36]; pT1b: 13.6% [3/22] vs. 19.4% [7/36]; pT2: 13.6% [3/22] vs. 13.9% [5/36]; pT3: 13.6% [3/22] vs. 25.0% [9/36]; pT4a: 9.1% [2/22] vs. 11.1% [4/36], Z=-1.331, P=0.183). Univariate analysis revealed that both preoperative treatment regimens were associated with residual lymph node metastasis status in patients whose primary tumor regression was TRG1 (χ2=6.070, P=0.014). Multivariate analysis incorporated the following factors: pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, number of preoperative treatment cycles, and preoperative treatment duration. We found that a combination of immunotherapy and chemotherapy administered preoperatively was an independent protective factor for reducing residual lymph node metastases in study patients whose primary tumor regression was TRG1 (OR=0.147, 95%CI: 0.026-0.828, P=0.030). Conclusion: Compared with preoperative chemotherapy alone, a combination of preoperative immunotherapy and chemotherapy achieved greater reduction of residual lymph node metastases in the study patients who achieved TRG1 tumor regression in their primary lesions.
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [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: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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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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Zhou J, Liu H, Miao H, Ye S, He Y, Zhao Y, Chen Z, Zhang Y, Liu YL, Pan Z, Su MY, Wang M. Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. Eur Radiol 2024:10.1007/s00330-024-10922-1. [PMID: 38990324 DOI: 10.1007/s00330-024-10922-1] [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: 03/23/2024] [Revised: 03/23/2024] [Accepted: 05/28/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions. METHODS A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed. RESULTS When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity. CONCLUSION The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved. CLINICAL RELEVANCE STATEMENT KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity. KEY POINTS KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.
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Liu H, Xu Z, Song C, Lu Y, Li T, Zheng Z, Li M, Ye H, Wang K, Shi J, Wang P. Burden of gastrointestinal cancers among people younger than 50 years in China, 1990 to 2019. Public Health 2024; 234:112-119. [PMID: 38972229 DOI: 10.1016/j.puhe.2024.06.008] [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: 02/19/2024] [Revised: 05/13/2024] [Accepted: 06/07/2024] [Indexed: 07/09/2024]
Abstract
OBJECTIVES This study aimed to assess the burden of early-onset gastrointestinal (GI) cancers in China over three decades. STUDY DESIGN A comprehensive analysis was performed using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. METHODS Data on early-onset GI cancers in 2020 and from 1990 to 2019 were extracted from GLOBOCAN 2020 database and GBD 2019, respectively. The average annual percent change (AAPC) was calculated to analyze the temporal trends using the Joinpoint Regression Program. The Bayesian age-period-cohort (BAPC) model was used to predict future trends up to 2030. RESULTS In China, there were 185,980 incident cases and 119,116 deaths of early-onset GI cancer in 2020, with the highest incidence and mortality observed in liver cancer (new cases: 71,662; deaths: 62,412). The spectrum of early-onset GI cancers in China has transitioned over the last 30 years. The age-standardized rates of incidence, mortality, and disability-adjusted life years for colorectal and pancreatic cancers exhibited rapid increases (AAPC >0, P ≤ 0.001). The fastest-growing incidence rate was found in colorectal cancer (AAPC: 3.06, P < 0.001). Despite the decreases in liver, gastric, and esophageal cancers, these trends have been reversed or flattened in recent years. High body mass index was found to be the fastest-growing risk factor for early-onset GI cancers (estimated annual percentage change: 2.75-4.19, P < 0.05). Projection analyses showed an increasing trend in age-standardized incidence rates for almost all early-onset GI cancers during 2020-2030. CONCLUSIONS The transitioning pattern of early-onset GI cancers in China emphasizes the urgency of addressing this public health challenge.
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Wang H, Xu WH, Liu JR, Peng Y, Peng XX, Wen XH, Tang XL, Xu H, Liu H, Shen YL, Zhang XY, Yang HM, Peng YG, Li HM, Zhao SY. [Clinical phenotyping of severe Mycoplasma pneumoniae pneumonia in children]. ZHONGHUA ER KE ZA ZHI = CHINESE JOURNAL OF PEDIATRICS 2024; 62:669-675. [PMID: 38955686 DOI: 10.3760/cma.j.cn112140-20231227-00466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Objective: To investigate and summarize pediatric patients with severe Mycoplasma pneumoniae pneumonia (MPP) presenting with varied clinical and chest imaging features in order to guide the individualized treatment. Methods: This was a retrospective cohort study. Medical records of clinical, imaging and laboratory data of 505 patients with MPP who were admitted to the Department Ⅱ of Respirology Center, Beijing Children's Hospital, Capital Medical University from January 2016 to October 2023 and met the enrollment criteria were included. They were divided into severe group and non-severe group according to whether lower airway obliterans was developed. The clinical and chest imaging features of the two groups were analyzed. Those severe cases with single lobe ≥2/3 consolidation (lobar consolidation) were further divided into subtype lung-necrosis and subtype non-lung-necrosis based on whether lung necrosis was developed. Comparison on the clinical manifestations, bronchoscopic findings, whole blood C-reactive protein (CRP) and other inflammatory indicators between the two subtypes was performed. Comparisons between two groups were achieved using independent-sample t-test, nonparametric test or chi-square test. Univariate receiver operating characteristic (ROC) curve analyses were performed on the indicators such as CRP of the two subtypes. Results: Of the 505 cases, 254 were male and 251 were female. The age of the onset was (8.2±2.9) years. There were 233 severe cases, among whom 206 were with lobar consolidation and 27 with diffuse bronchiolitis. The other 272 belonged to non-severe cases, with patchy, cloudy infiltrations or single lobe <2/3 uneven consolidation or localized bronchiolitis. Of the 206 cases (88.4%) severe cases with lobar consolidation, 88 harbored subtype lung-necrosis and 118 harbored subtype non-lung-necrosis. All 206 cases (100.0%) presented with persistent high fever, among whom 203 cases (98.5%) presented with inflammatory secretion obstruction and plastic bronchitis under bronchoscopy. Of those 88 cases with subtype lung-necrosis, there were 42 cases (47.7%) with dyspnea and 39 cases (44.3%) with moderate to massive amount of pleural effusion. There were 35 cases (39.8%) diagnosed with lung embolism during the disease course, of which other 34 cases (38.6%) were highly suspected. Extensive airway mucosal necrosis was observed in 46 cases (52.3%), and the level of their whole blood CRP was significantly higher than that of subtype non-lung-necrosis (131.5 (91.0, 180.0) vs. 25.5 (12.0, 43.1) mg/L, U=334.00, P<0.001). They were regarded as subtype "lung consolidation-atelectasis-necrosis". Of those 118 cases with subtype non-lung-necrosis, 27 cases (22.9%) presented with dyspnea and none were with moderate to massive amount of pleural effusion. Sixty-five cases (55.1%) presented with plastic bronchitis and localized airway mucosal necrosis was observed in 32 cases (27.1%). They were deemed as subtype "lung consolidation-atelectasis". ROC curve analyses revealed that whole blood CRP of 67.5 mg/L on the 6-10 th day of disease course exhibited a sensitivity of 0.96, a specificity of 0.89, and an area under the curve of 0.97 for distinguishing between these two subtypes among those with lobar consolidation. Conclusions: Pediatric patients with severe MPP present with lobar consolidation or diffuse bronchiolitis on chest imaging. Those with lobar consolidation harbor 2 subtypes as "lung consolidation-atelectasis-necrosis" and "lung consolidation-atelectasis". Whole blood CRP of 67.5 mg/L can be applied as an early discriminating indicator to discriminate between these two subtypes.
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Liu H, Li SR, Si Q. Retraction Note: Regulation of miRNAs on c-met protein expression in ovarian cancer and its implication. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2024; 28:3891. [PMID: 39012239 DOI: 10.26355/eurrev_202407_36518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
The article "Regulation of miRNAs on c-met protein expression in ovarian cancer and its implication", by H. Liu, S.-R. Li, Q. Si, published in Eur Rev Med Pharmacol Sci 2017; 21 (15): 3353-3359-PMID: 28829508 has been retracted by the Editor in Chief. Following some concerns raised on PubPeer regarding a possible manipulation in Figure 5 (link: https://pubpeer.com/publications/7B6E6E6679990661654EBCAF472921), the Editor in Chief has started an investigation to assess the validity of the results as well as possible figure manipulation. The authors were informed about the journal's investigation but remained unresponsive and have not provided the manuscript's raw data. The journal's investigation revealed a figure overlap between panels pcDNA3.1-EGFP and pcDNA3.1-EGFP-204-up in Figure 5. Consequently, the Editor in Chief mistrusts the results presented and has decided to retract the article. This article has been retracted. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/13200.
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Li H, Liu H, Zhu D, Dou C, Gang B, Zhang M, Wan Z. Biological function molecular pathways and druggability of DNMT2/TRDMT1. Pharmacol Res 2024; 205:107222. [PMID: 38782147 DOI: 10.1016/j.phrs.2024.107222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
5-methylcytosine (m5C) is among the most common epigenetic modification in DNA and RNA molecules, and plays an important role in the animal development and disease pathogenesis. Interestingly, unlike other m5C DNA methyltransferases (DNMTs), DNMT2/TRDMT1 has the double-substrate specificity and adopts a DNMT-similar catalytic mechanism to methylate RNA. Moreover, it is widely involved in a variety of physiological regulatory processes, such as the gene expression, precise protein synthesis, immune response, and disease occurrence. Thus, comprehending the epigenetic mechanism and function of DNMT2/TRDMT1 will probably provide new strategies to treat some refractory diseases. Here, we discuss recent studies on the spatiotemporal expression pattern and post-translational modifications of DNMT2/TRDMT1, and summarize the research advances in substrate characteristics, catalytic recognition mechanism, DNMT2/TRDMT1-related genes or proteins, pharmacological application, and inhibitor development. This review will shed light on the pharmacological design by targeting DNMT2/TRDMT1 to treat parasitic, viral and oncologic diseases.
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Liu H, Han X, Chu S, Ma W, Ding W, Li J, Jiang Y. Coexisting PTPN11 and TNNT2 mutations in noonan syndrome with multiple lentigines. QJM 2024; 117:460-461. [PMID: 38366647 DOI: 10.1093/qjmed/hcae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Indexed: 02/18/2024] Open
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Abratenko P, Alterkait O, Andrade Aldana D, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow D, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhat A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Brunetti MB, Camilleri L, Cao Y, Caratelli D, Cavanna F, Cerati G, Chappell A, Chen Y, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Cross R, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Englezos P, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Franco D, Furmanski AP, Gao F, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Gramellini E, Green P, Greenlee H, Gu L, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hilgenberg C, Horton-Smith GA, Imani Z, Irwin B, Ismail MS, James C, Ji X, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Liu H, Louis WC, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Martynenko S, Mastbaum A, Mawby I, McConkey N, Meddage V, Micallef J, Miller K, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Moudgalya MM, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Pophale I, Qian X, Raaf JL, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Safa I, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, John JS, Strauss T, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. First Search for Dark-Trident Processes Using the MicroBooNE Detector. PHYSICAL REVIEW LETTERS 2024; 132:241801. [PMID: 38949335 DOI: 10.1103/physrevlett.132.241801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 07/02/2024]
Abstract
We present a first search for dark-trident scattering in a neutrino beam using a dataset corresponding to 7.2×10^{20} protons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the main injector produce π^{0} and η mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A^{'}. A convolutional neural network is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its imagelike reconstruction capability. In the absence of a DM signal, we provide limits at the 90% confidence level on the squared kinematic mixing parameter ϵ^{2} as a function of the dark-photon mass in the range 10≤M_{A^{'}}≤400 MeV. The limits cover previously unconstrained parameter space for the production of fermion or scalar DM particles χ for two benchmark models with mass ratios M_{χ}/M_{A^{'}}=0.6 and 2 and for dark fine-structure constants 0.1≤α_{D}≤1.
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Aaij R, Abdelmotteleb ASW, Abellan Beteta C, Abudinén F, Ackernley T, Adeva B, Adinolfi M, Adlarson P, Agapopoulou C, Aidala CA, Ajaltouni Z, Akar S, Akiba K, Albicocco P, Albrecht J, Alessio F, Alexander M, Alfonso Albero A, Aliouche Z, Alvarez Cartelle P, Amalric R, Amato S, Amey JL, Amhis Y, An L, Anderlini L, Andersson M, Andreianov A, Andreola P, Andreotti M, Andreou D, Anelli AA, Ao D, Archilli F, Argenton M, Arguedas Cuendis S, Artamonov A, Artuso M, Aslanides E, Atzeni M, Audurier B, Bacher D, Bachiller Perea I, Bachmann S, Bachmayer M, Back JJ, Bailly-Reyre A, Baladron Rodriguez P, Balagura V, Baldini W, Baptista de Souza Leite J, Barbetti M, Barbosa IR, Barlow RJ, Barsuk S, Barter W, Bartolini M, Baryshnikov F, Basels JM, Bassi G, Batsukh B, Battig A, Bay A, Beck A, Becker M, Bedeschi F, Bediaga IB, Beiter A, Belin S, Bellee V, Belous K, Belov I, Belyaev I, Benane G, Bencivenni G, Ben-Haim E, Berezhnoy A, Bernet R, Bernet Andres S, Bernstein HC, Bertella C, Bertolin A, Betancourt C, Betti F, Bex J, Bezshyiko I, Bhom J, Bieker MS, Biesuz NV, Billoir P, Biolchini A, Birch M, Bishop FCR, Bitadze A, Bizzeti A, Blago MP, Blake T, Blanc F, Blank JE, Blusk S, Bobulska D, Bocharnikov V, Boelhauve JA, Boente Garcia O, Boettcher T, Bohare A, Boldyrev A, Bolognani CS, Bolzonella R, Bondar N, Borgato F, Borghi S, Borsato M, Borsuk JT, Bouchiba SA, Bowcock TJV, Boyer A, Bozzi C, Bradley MJ, Braun S, Brea Rodriguez A, Breer N, Brodzicka J, Brossa Gonzalo A, Brown J, Brundu D, Buonaura A, Buonincontri L, Burke AT, Burr C, Bursche A, Butkevich A, Butter JS, Buytaert J, Byczynski W, Cadeddu S, Cai H, Calabrese R, Calefice L, Cali S, Calvi M, Calvo Gomez M, Cambon Bouzas J, Campana P, Campora Perez DH, Campoverde Quezada AF, Capelli S, Capriotti L, Caravaca-Mora RC, Carbone A, Carcedo Salgado L, Cardinale R, Cardini A, Carniti P, Carus L, Casais Vidal A, Caspary R, Casse G, Castro Godinez J, Cattaneo M, Cavallero G, Cavallini V, Celani S, Cerasoli J, Cervenkov D, Cesare S, Chadwick AJ, Chahrour I, Charles M, Charpentier P, Chavez Barajas CA, Chefdeville M, Chen C, Chen S, Chernov A, Chernyshenko S, Chobanova V, Cholak S, Chrzaszcz M, Chubykin A, Chulikov V, Ciambrone P, Cicala MF, Cid Vidal X, Ciezarek G, Cifra P, Clarke PEL, Clemencic M, Cliff HV, Closier J, Cobbledick JL, Cocha Toapaxi C, Coco V, Cogan J, Cogneras E, Cojocariu L, Collins P, Colombo T, Comerma-Montells A, Congedo L, Contu A, Cooke N, Corredoira I, Correia A, Corti G, Cottee Meldrum JJ, Couturier B, Craik DC, Cruz Torres M, Currie R, Da Silva CL, Dadabaev S, Dai L, Dai X, Dall'Occo E, Dalseno J, D'Ambrosio C, Daniel J, Danilina A, d'Argent P, Davidson A, Davies JE, Davis A, De Aguiar Francisco O, De Angelis C, de Boer J, De Bruyn K, De Capua S, De Cian M, De Freitas Carneiro Da Graca U, De Lucia E, De Miranda JM, De Paula L, De Serio M, De Simone D, De Simone P, De Vellis F, de Vries JA, Debernardis F, Decamp D, Dedu V, Del Buono L, Delaney B, Dembinski HP, Deng J, Denysenko V, Deschamps O, Dettori F, Dey B, Di Nezza P, Diachkov I, Didenko S, Ding S, Dobishuk V, Docheva AD, Dolmatov A, Dong C, Donohoe AM, Dordei F, Dos Reis AC, Douglas L, Downes AG, Duan W, Duda P, Dudek MW, Dufour L, Duk V, Durante P, Duras MM, Durham JM, Dutta D, Dziurda A, Dzyuba A, Easo S, Eckstein E, Egede U, Egorychev A, Egorychev V, Eirea Orro C, Eisenhardt S, Ejopu E, Ek-In S, Eklund L, Elashri M, Ellbracht J, Ely S, Ene A, Epple E, Escher S, Eschle J, Esen S, Evans T, Fabiano F, Falcao LN, Fan Y, Fang B, Fantini L, Faria M, Farmer K, Fazzini D, Felkowski L, Feng M, Feo M, Fernandez Gomez M, Fernez AD, Ferrari F, Ferreira Rodrigues F, Ferreres Sole S, Ferrillo M, Ferro-Luzzi M, Filippov S, Fini RA, Fiorini M, Firlej M, Fischer KM, Fitzgerald DS, Fitzpatrick C, Fiutowski T, Fleuret F, Fontana M, Fontanelli F, Foreman LF, Forty R, Foulds-Holt D, Franco Sevilla M, Frank M, Franzoso E, Frau G, Frei C, Friday DA, Frontini L, Fu J, Fuehring Q, Fujii Y, Fulghesu T, Gabriel E, Galati G, Galati MD, Gallas Torreira A, Galli D, Gambetta S, Gandelman M, Gandini P, Gao H, Gao R, Gao Y, Gao Y, Gao Y, Garau M, Garcia Martin LM, Garcia Moreno P, García Pardiñas J, Garcia Plana B, Garg KG, Garrido L, Gaspar C, Geertsema RE, Gerken LL, Gersabeck E, Gersabeck M, Gershon T, Ghorbanimoghaddam Z, Giambastiani L, Giasemis FI, Gibson V, Giemza HK, Gilman AL, Giovannetti M, Gioventù A, Gironella Gironell P, Giugliano C, Giza MA, Gkougkousis EL, Glaser FC, Gligorov VV, Göbel C, Golobardes E, Golubkov D, Golutvin A, Gomes A, Gomez Fernandez S, Goncalves Abrantes F, Goncerz M, Gong G, Gooding JA, Gorelov IV, Gotti C, Grabowski JP, Granado Cardoso LA, Graugés E, Graverini E, Grazette L, Graziani G, Grecu AT, Greeven LM, Grieser NA, Grillo L, Gromov S, Gu C, Guarise M, Guittiere M, Guliaeva V, Günther PA, Guseinov AK, Gushchin E, Guz Y, Gys T, Hadavizadeh T, Hadjivasiliou C, Haefeli G, Haen C, Haimberger J, Hajheidari M, Halewood-Leagas T, Halvorsen MM, Hamilton PM, Hammerich J, Han Q, Han X, Hansmann-Menzemer S, Hao L, Harnew N, Harrison T, Hartmann M, Hasse C, He J, Heijhoff K, Hemmer F, Henderson C, Henderson RDL, Hennequin AM, Hennessy K, Henry L, Herd J, Heuel J, Hicheur A, Hill D, Hollitt SE, Horswill J, Hou R, Hou Y, Howarth N, Hu J, Hu J, Hu W, Hu X, Huang W, Hulsbergen W, Hunter RJ, Hushchyn M, Hutchcroft D, Idzik M, Ilin D, Ilten P, Inglessi A, Iniukhin A, Ishteev A, Ivshin K, Jacobsson R, Jage H, Jaimes Elles SJ, Jakobsen S, Jans E, Jashal BK, Jawahery A, Jevtic V, Jiang E, Jiang X, Jiang Y, Jiang YJ, John M, Johnson D, Jones CR, Jones TP, Joshi S, Jost B, Jurik N, Juszczak I, Kaminaris D, Kandybei S, Kang Y, Karacson M, Karpenkov D, Karpov M, Kauniskangas AM, Kautz JW, Keizer F, Keller DM, Kenzie M, Ketel T, Khanji B, Kharisova A, Kholodenko S, Khreich G, Kirn T, Kirsebom VS, Kitouni O, Klaver S, Kleijne N, Klimaszewski K, Kmiec MR, Koliiev S, Kolk L, Konoplyannikov A, Kopciewicz P, Koppenburg P, Korolev M, Kostiuk I, Kot O, Kotriakhova S, Kozachuk A, Kravchenko P, Kravchuk L, Kreps M, Kretzschmar S, Krokovny P, Krupa W, Krzemien W, Kubat J, Kubis S, Kucewicz W, Kucharczyk M, Kudryavtsev V, Kulikova E, Kupsc A, Kutsenko BK, Lacarrere D, Lafferty G, Lai A, Lampis A, Lancierini D, Landesa Gomez C, Lane JJ, Lane R, Langenbruch C, Langer J, Lantwin O, Latham T, Lazzari F, Lazzeroni C, Le Gac R, Lee SH, Lefèvre R, Leflat A, Legotin S, Lehuraux M, Leroy O, Lesiak T, Leverington B, Li A, Li H, Li K, Li L, Li P, Li PR, Li S, Li T, Li T, Li Y, Li Y, Li Z, Lian Z, Liang X, Lin C, Lin T, Lindner R, Lisovskyi V, Litvinov R, Liu G, Liu H, Liu K, Liu Q, Liu S, Liu Y, Liu Y, Liu YL, Lobo Salvia A, Loi A, Lomba Castro J, Long T, Lopes JH, Lopez Huertas A, López Soliño S, Lovell GH, Lucarelli C, Lucchesi D, Luchuk S, Lucio Martinez M, Lukashenko V, Luo Y, Lupato A, Luppi E, Lynch K, Lyu XR, Ma GM, Ma R, Maccolini S, Machefert F, Maciuc F, Mackay I, Madhan Mohan LR, Madurai MM, Maevskiy A, Magdalinski D, Maisuzenko D, Majewski MW, Malczewski JJ, Malde S, Malecki B, Malentacca L, Malinin A, Maltsev T, Manca G, Mancinelli G, Mancuso C, Manera Escalero R, Manuzzi D, Marangotto D, Marchand JF, Marchevski R, Marconi U, Mariani S, Marin Benito C, Marks J, Marshall AM, Marshall PJ, Martelli G, Martellotti G, Martinazzoli L, Martinelli M, Martinez Santos D, Martinez Vidal F, Massafferri A, Materok M, Matev R, Mathad A, Matiunin V, Matteuzzi C, Mattioli KR, Mauri A, Maurice E, Mauricio J, Mayencourt P, Mazurek M, McCann M, Mcconnell L, McGrath TH, McHugh NT, McNab A, McNulty R, Meadows B, Meier G, Melnychuk D, Merk M, Merli A, Meyer Garcia L, Miao D, Miao H, Mikhasenko M, Milanes DA, Minotti A, Minucci E, Miralles T, Mitchell SE, Mitreska B, Mitzel DS, Modak A, Mödden A, Mohammed RA, Moise RD, Mokhnenko S, Mombächer T, Monk M, Monroy IA, Monteil S, Morcillo Gomez A, Morello G, Morello MJ, Morgenthaler MP, Moron J, Morris AB, Morris AG, Mountain R, Mu H, Mu ZM, Muhammad E, Muheim F, Mulder M, Müller K, Mũnoz-Rojas F, Murta R, Naik P, Nakada T, Nandakumar R, Nanut T, Nasteva I, Needham M, Neri N, Neubert S, Neufeld N, Neustroev P, Newcombe R, Nicolini J, Nicotra D, Niel EM, Nikitin N, Nogga P, Nolte NS, Normand C, Novoa Fernandez J, Nowak G, Nunez C, Nur HN, Oblakowska-Mucha A, Obraztsov V, Oeser T, Okamura S, Okhotnikov A, Oldeman R, Oliva F, Olocco M, Onderwater CJG, O'Neil RH, Otalora Goicochea JM, Ovsiannikova T, Owen P, Oyanguren A, Ozcelik O, Padeken KO, Pagare B, Pais PR, Pajero T, Palano A, Palutan M, Panshin G, Paolucci L, Papanestis A, Pappagallo M, Pappalardo LL, Pappenheimer C, Parkes C, Passalacqua B, Passaleva G, Passaro D, Pastore A, Patel M, Patoc J, Patrignani C, Pawley CJ, Pellegrino A, Pepe Altarelli M, Perazzini S, Pereima D, Pereiro Castro A, Perret P, Perro A, Petridis K, Petrolini A, Petrucci S, Pham H, Pica L, Piccini M, Pietrzyk B, Pietrzyk G, Pinci D, Pisani F, Pizzichemi M, Placinta V, Plo Casasus M, Polci F, Poli Lener M, Poluektov A, Polukhina N, Polyakov I, Polycarpo E, Ponce S, Popov D, Poslavskii S, Prasanth K, Promberger L, Prouve C, Pugatch V, Puill V, Punzi G, Qi HR, Qian W, Qin N, Qu S, Quagliani R, Rabadan Trejo RI, Rachwal B, Rademacker JH, Rama M, Ramírez García M, Ramos Pernas M, Rangel MS, Ratnikov F, Raven G, Rebollo De Miguel M, Redi F, Reich J, Reiss F, Ren Z, Resmi PK, Ribatti R, Ricart GR, Riccardi D, Ricciardi S, Richardson K, Richardson-Slipper M, Rinnert K, Robbe P, Robertson G, Rodrigues E, Rodriguez Fernandez E, Rodriguez Lopez JA, Rodriguez Rodriguez E, Rogovskiy A, Rolf DL, Rollings A, Roloff P, Romanovskiy V, Romero Lamas M, Romero Vidal A, Romolini G, Ronchetti F, Rotondo M, Roy SR, Rudolph MS, Ruf T, Ruiz Diaz M, Ruiz Fernandez RA, Ruiz Vidal J, Ryzhikov A, Ryzka J, Saborido Silva JJ, Sadek R, Sagidova N, Sahoo N, Saitta B, Salomoni M, Sanchez Gras C, Sanderswood I, Santacesaria R, Santamarina Rios C, Santimaria M, Santoro L, Santovetti E, Saputi A, Saranin D, Sarpis G, Sarpis M, Sarti A, Satriano C, Satta A, Saur M, Savrina D, Sazak H, Scantlebury Smead LG, Scarabotto A, Schael S, Scherl S, Schertz AM, Schiller M, Schindler H, Schmelling M, Schmidt B, Schmitt S, Schmitz H, Schneider O, Schopper A, Schulte N, Schulte S, Schune MH, Schwemmer R, Schwering G, Sciascia B, Sciuccati A, Sellam S, Semennikov A, Senghi Soares M, Sergi A, Serra N, Sestini L, Seuthe A, Shang Y, Shangase DM, Shapkin M, Shchemerov I, Shchutska L, Shears T, Shekhtman L, Shen Z, Sheng S, Shevchenko V, Shi B, Shields EB, Shimizu Y, Shmanin E, Shorkin R, Shupperd JD, Silva Coutinho R, Simi G, Simone S, Skidmore N, Skuza R, Skwarnicki T, Slater MW, Smallwood JC, Smith E, Smith K, Smith M, Snoch A, Soares Lavra L, Sokoloff MD, Soler FJP, Solomin A, Solovev A, Solovyev I, Song R, Song Y, Song Y, Song YS, Souza De Almeida FL, Souza De Paula B, Spadaro Norella E, Spedicato E, Speer JG, Spiridenkov E, Spradlin P, Sriskaran V, Stagni F, Stahl M, Stahl S, Stanislaus S, Stein EN, Steinkamp O, Stenyakin O, Stevens H, Strekalina D, Su Y, Suljik F, Sun J, Sun L, Sun Y, Swallow PN, Swientek K, Swystun F, Szabelski A, Szumlak T, Szymanski M, Tan Y, Taneja S, Tat MD, Terentev A, Terzuoli F, Teubert F, Thomas E, Thompson DJD, Tilquin H, Tisserand V, T'Jampens S, Tobin M, Tomassetti L, Tonani G, Tong X, Torres Machado D, Toscano L, Tou DY, Trippl C, Tuci G, Tuning N, Uecker LH, Ukleja A, Unverzagt DJ, Ursov E, Usachov A, Ustyuzhanin A, Uwer U, Vagnoni V, Valassi A, Valenti G, Valls Canudas N, Van Hecke H, van Herwijnen E, Van Hulse CB, Van Laak R, van Veghel M, Vazquez Gomez R, Vazquez Regueiro P, Vázquez Sierra C, Vecchi S, Velthuis JJ, Veltri M, Venkateswaran A, Vesterinen M, Vieira D, Vieites Diaz M, Vilasis-Cardona X, Vilella Figueras E, Villa A, Vincent P, Volle FC, Vom Bruch D, Vorobyev V, Voropaev N, Vos K, Vouters G, Vrahas C, Walsh J, Walton EJ, Wan G, Wang C, Wang G, Wang J, Wang J, Wang J, Wang J, Wang M, Wang NW, Wang R, Wang X, Wang XW, Wang Y, Wang Z, Wang Z, Wang Z, Ward JA, Watson NK, Websdale D, Wei Y, Westhenry BDC, White DJ, Whitehead M, Wiederhold AR, Wiedner D, Wilkinson G, Wilkinson MK, Williams M, Williams MRJ, Williams R, Wilson FF, Wislicki W, Witek M, Witola L, Wong CP, Wormser G, Wotton SA, Wu H, Wu J, Wu Y, Wyllie K, Xian S, Xiang Z, Xie Y, Xu A, Xu J, Xu L, Xu L, Xu M, Xu Z, Xu Z, Xu Z, Yang D, Yang S, Yang X, Yang Y, Yang Z, Yang Z, Yeroshenko V, Yeung H, Yin H, Yu CY, Yu J, Yuan X, Zaffaroni E, Zavertyaev M, Zdybal M, Zeng M, Zhang C, Zhang D, Zhang J, Zhang L, Zhang S, Zhang S, Zhang Y, Zhang Y, Zhang YZ, Zhao Y, Zharkova A, Zhelezov A, Zheng XZ, Zheng Y, Zhou T, Zhou X, Zhou Y, Zhovkovska V, Zhu LZ, Zhu X, Zhu X, Zhu Z, Zhukov V, Zhuo J, Zou Q, Zuliani D, Zunica G. Modification of χ_{c1}(3872) and ψ(2S) Production in pPb Collisions at sqrt[s_{NN}]=8.16 TeV. PHYSICAL REVIEW LETTERS 2024; 132:242301. [PMID: 38949352 DOI: 10.1103/physrevlett.132.242301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/17/2024] [Indexed: 07/02/2024]
Abstract
The LHCb Collaboration measures production of the exotic hadron χ_{c1}(3872) in proton-nucleus collisions for the first time. Comparison with the charmonium state ψ(2S) suggests that the exotic χ_{c1}(3872) experiences different dynamics in the nuclear medium than conventional hadrons, and comparison with data from proton-proton collisions indicates that the presence of the nucleus may modify χ_{c1}(3872) production rates. This is the first measurement of the nuclear modification factor of an exotic hadron.
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Luo D, Xiang CX, Ma DS, Liu GZ, Fan MT, Wang YB, Zhao J, Yuan YQ, Shen QQ, Liu XY, Liu H. [Diagnosis and differential diagnosis of large B-cell lymphoma with IRF4 rearrangement]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2024; 53:563-569. [PMID: 38825901 DOI: 10.3760/cma.j.cn112151-20231106-00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Objective: To analyze the clinicopathological features and differential diagnosis of large B-cell lymphoma with IRF4 rearrangement, aiming enhance its recognition and prevent misdiagnosis. Methods: The clinicopathological features, immunophenotype, and fluorescence in situ hybridization (FISH) results of six cases diagnosed with IRF4 rearrangement-positive B-cell lymphoma at the Affiliated Hospital of Xuzhou Medical University from 2015 to 2023 were retrospectively analyzed. Additionally, a comprehensive review of the literature was conducted. Results: Six patients with IRF4 rearrangement-positive large B-cell lymphoma were included. Patients 1 to 5 included three males and two females with a median age of 19 years ranging from 11 to 34 years. Four patients presented with head and neck lesions, while the other one had a breast nodule; all were in clinical Ann Arbor stages I to Ⅱ. Morphologically, entirely diffuse pattern was present in two cases, purely follicular pattern in one case, and diffuse and follicular patterns in other two cases. The tumor cells, predominantly centroblasts mixed with some irregular centrocytes, were of medium to large size, with a starry sky appearance observed in two cases. Immunophenotyping revealed all cases were positive for bcl-6 and MUM1, with a Ki-67 index ranging from 70% to 90%, and CD10 was positive in two cases. IRF4 rearrangement was confirmed in all cases by FISH analysis, with dual IRF4/bcl-6 rearrangements identified in two cases, leading to a diagnosis of LBCL-IRF4. Case 6, a 39-year-old female with a tonsillar mass and classified as clinical Ann Arbor stage Ⅳ, displayed predominantly diffuse large B-cell lymphoma (DLBCL) morphology with 20% high-grade follicular lymphoma characteristics. Immunohistochemistry showed negative CD10 and positive bcl-6/MUM1, with a Ki-67 index of approximately 80%. Triple rearrangements of IRF4/bcl-2/bcl-6 were identified by FISH, leading to a diagnosis of DLBCL with 20% follicular lymphoma (FL). All six patients achieved complete remission after treatment, with no progression or relapse during a follow-up period of 31-100 months. Conclusions: Large B-cell lymphoma with IRF4 rearrangement is a rare entity with pathological features that overlap with those of FL and DLBCL. While IRF4 rearrangement is necessary for diagnosing LBCL-IRF4, it is not specific and requires differentiation from other aggressive B-cell lymphomas with IRF4 rearrangement.
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MESH Headings
- Humans
- Interferon Regulatory Factors/genetics
- Interferon Regulatory Factors/metabolism
- Diagnosis, Differential
- Female
- Male
- Lymphoma, Large B-Cell, Diffuse/genetics
- Lymphoma, Large B-Cell, Diffuse/pathology
- Lymphoma, Large B-Cell, Diffuse/diagnosis
- Lymphoma, Large B-Cell, Diffuse/metabolism
- Adult
- In Situ Hybridization, Fluorescence
- Gene Rearrangement
- Adolescent
- Retrospective Studies
- Proto-Oncogene Proteins c-bcl-6/genetics
- Proto-Oncogene Proteins c-bcl-6/metabolism
- Child
- Young Adult
- Immunophenotyping
- Proto-Oncogene Proteins c-bcl-2/genetics
- Proto-Oncogene Proteins c-bcl-2/metabolism
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Ablikim M, Achasov MN, Adlarson P, Afedulidis O, Ai XC, Aliberti R, Amoroso A, 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, 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 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, 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, 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 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, 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 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. First Study of Antihyperon-Nucleon Scattering Λ[over ¯]p→Λ[over ¯]p and Measurement of Λp→Λp Cross Section. PHYSICAL REVIEW LETTERS 2024; 132:231902. [PMID: 38905649 DOI: 10.1103/physrevlett.132.231902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/04/2024] [Accepted: 05/07/2024] [Indexed: 06/23/2024]
Abstract
Using (10.087±0.044)×10^{9} J/ψ events collected with the BESIII detector at the BEPCII storage ring, the processes Λp→Λp and Λ[over ¯]p→Λ[over ¯]p are studied, where the Λ/Λ[over ¯] baryons are produced in the process J/ψ→ΛΛ[over ¯] and the protons are the hydrogen nuclei in the cooling oil of the beam pipe. Clear signals are observed for the two reactions. The cross sections in -0.9≤cosθ_{Λ/Λ[over ¯]}≤0.9 are measured to be σ(Λp→Λp)=(12.2±1.6_{stat}±1.1_{syst}) and σ(Λ[over ¯]p→Λ[over ¯]p)=(17.5±2.1_{stat}±1.6_{syst}) mb at the Λ/Λ[over ¯] momentum of 1.074 GeV/c within a range of ±0.017 GeV/c, where the θ_{Λ/Λ[over ¯]} are the scattering angles of the Λ/Λ[over ¯] in the Λp/Λ[over ¯]p rest frames. Furthermore, the differential cross sections of the two reactions are also measured, where there is a slight tendency of forward scattering for Λp→Λp, and a strong forward peak for Λ[over ¯]p→Λ[over ¯]p. We present an approach to extract the total elastic cross sections by extrapolation. The study of Λ[over ¯]p→Λ[over ¯]p represents the first study of antihyperon-nucleon scattering, and these new measurements will serve as important inputs for the theoretical understanding of the (anti)hyperon-nucleon interaction.
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Zhang J, Li J, Liu H, Liu J, Zhao L, Li X, Li X. Relationship between fasting prior to contrast-enhanced CT and adverse reaction in patients with allergies history. Clin Radiol 2024; 79:420-427. [PMID: 38599950 DOI: 10.1016/j.crad.2024.02.014] [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: 11/03/2023] [Revised: 01/24/2024] [Accepted: 02/16/2024] [Indexed: 04/12/2024]
Abstract
AIM To examine the relationship between fasting prior to contrast-enhanced CT (CECT) and adverse reaction (AR) in patients with allergies history. MATERIALS AND METHODS Patients with allergies history who underwent CECT from January 2014 to December 2020 (713 cases with iodinated contrast media (ICM)-related allergy history and 27045 cases with unrelated allergies history) were retrospectively analyzed. The occurrence of ICM-related AR and patient information were recorded. The relationship between fasting and AR and emetic complications was analyzed. RESULTS There was no statistical difference in the overall incidence of AR and emetic complications between fasting group and non-fasting group (P>0.05) and fasting was not an influence factor for overall AR occurrence in patients with both ICM-related and unrelated allergies history. However, the incidence of severe AR in fasting group was higher than that in non-fasting group (P=0.01) in patients with unrelated allergies history. The AR incidence in fasting group was higher than that in non-fasting group (P=0.022) when receiving abdominal examinations in patients with unrelated allergies history. There was no statistical difference in the incidence of AR with different occurrence time between fasting group and non-fasting group (P>0.05) in patients with both ICM-related and unrelated allergies history. CONCLUSIONS Fasting was associated with higher incidence of severe AR and was associated with higher AR incidence when receiving abdominal examinations in patients with unrelated allergies history. Fasting did not have effects on the occurrence time of AR in patients with allergies history. These provided new guidance for usage of ICM in patients with allergies history.
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Liu J, Fang X, Cao S, Shi Y, Li S, Liu H, Li Y, Xu S, Xia W. Associations of ambient temperature and total cloud cover during pregnancy with newborn vitamin D status. Public Health 2024; 231:179-186. [PMID: 38703492 DOI: 10.1016/j.puhe.2024.03.026] [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: 09/20/2023] [Revised: 02/24/2024] [Accepted: 03/27/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVES We aimed to estimate the effects of temperature and total cloud cover before birth on newborn vitamin D status. STUDY DESIGN Prospective birth cohort. METHODS This study included 2055 mother-newborn pairs in Wuhan, Hubei province, China. The data of temperature and total cloud cover from 30 days before birth were collected, and cord blood 25-hydroxyvitamin D [25(OH)D] were determined. Restricted cubic spline regression models, multiple linear regression models, and logistic regression models were applied to estimate the associations. RESULTS A "J" shaped curve was observed between temperature and vitamin D status, and an inverse "J" shaped curve was observed between total cloud cover and vitamin D status. Compared to the fourth quartile (75-100th percentile, Q4) of average temperature (30 days before birth), the odds ratio (OR) for Q1 (0-25th percentile) associated with the vitamin D deficiency occurrence (<20 ng/mL) was 3.63 (95% CI, 1.54, 8.65). Compared to Q1 of the average total cloud cover (30 days before birth), the OR associated with the occurrence of vitamin D deficiency was 2.38 (95% CI, 1.63, 3.50) for the Q4. CONCLUSIONS Low temperature and high cloud cover before delivery were significantly associated with an increased probability of vitamin D deficiency in newborns. The findings suggested that pregnancy women lacking sufficient sunlight exposure still need vitamin D supplement to overcome the potential vitamin D deficiency status.
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Bao J, Zhang X, Xiang S, Liu H, Cheng M, Yang Y, Huang X, Xiang W, Cui W, Lai HC, Huang S, Wang Y, Qian D, Yu H. Deep Learning-Based Facial and Skeletal Transformations for Surgical Planning. J Dent Res 2024:220345241253186. [PMID: 38808566 DOI: 10.1177/00220345241253186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024] Open
Abstract
The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of craniomaxillofacial landmarks) of P2P-ConvGC in the upper skull, mandible, and facial soft tissues were 1.964 ± 0.904 mm, 2.398 ± 1.174 mm, and 2.226 ± 0.774 mm, respectively. Furthermore, the clinical feasibility of the bidirectional model was validated using a clinical cohort. The result demonstrated its prediction ability with average surface deviation errors of 0.895 ± 0.175 mm for facial prediction and 0.906 ± 0.082 mm for skeletal prediction. To conclude, our proposed model achieved good performance on the subject-specific prediction of facial and skeletal shapes and showed clinical application potential in postoperative facial prediction and VSP for orthognathic surgery.
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Ren JY, Yan MM, Li XT, Liu H, Tang NE, Zheng RJ, Lu XB. [Analysis of clinical characteristics and risk factors for nosocomial mortality in patients with liver cirrhosis combined with atrial arrhythmia]. ZHONGHUA GAN ZANG BING ZA ZHI = ZHONGHUA GANZANGBING ZAZHI = CHINESE JOURNAL OF HEPATOLOGY 2024; 32:453-460. [PMID: 38858195 DOI: 10.3760/cma.j.cn501113-20231225-00296] [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
Objective: To analyze and explore the clinical characteristics and risk factors related to nosocomial mortality in patients with liver cirrhosis combined with atrial arrhythmia. Methods: 252 hospitalized patients with liver cirrhosis combined with atrial arrhythmia from January 2014 to December 2021 were enrolled, and their clinical characteristics were analyzed. The above-mentioned patients were divided into groups according to their nosocomial mortality rate. Among them, 45 nosocomial mortality cases were classified as the mortality group, and 207 survival cases were classified as the survival group. The differences in clinical data and laboratory data between the two groups were compared. The risk factors for nosocomial mortality in patients with liver cirrhosis combined with atrial arrhythmia were analyzed. The t-test, or rank-sum test, was used to compare measurement data. The chi-square test, or Fisher's exact probability method, was used to compare enumeration data. Multivariate analysis was performed by the logistic regression method. Results: Among the 252 cases, the male-to-female ratio was the same (male/female ratio: 126/126). The age range was 26 to 89 (66.77±10.46) years. Han ethnicity accounted for 79.5%. The main type of atrial arrhythmia was atrial fibrillation (P < 0.001). The main cause of liver cirrhosis was post-hepatitis B cirrhosis (56.3%). There were 57/72/123 cases of CTP grade A/B/C. The CTP and Model for End-Stage Liver Disease (MELD) scores were 10.30±1.77 and 18.0(11.0, 29.0), respectively. The nosocomial mortality rate was 17.9% (45/252). The overall incidence rate of complications in all patients was 89.28%, with complications occurring in the following order: 71.4% ascites, 71.0% hypersplenism, 64.7% spontaneous peritonitis, 64.3% esophageal gastric varices, 32.5% hepatorenal syndrome, 32.1% hepatic encephalopathy, and 26.2% esophageal gastric variceal bleeding. The incidence rate of new-onset atrial fibrillation in the nosocomial mortality group was 73.3%, which was much higher than the 44.0% rate in the survival group (P < 0.05). Multivariate logistic regression analysis showed that new-onset atrial fibrillation (OR=2.707, 95%CI 1.119 ~ 6.549), esophageal-gastric varices (OR=3.287, 95%CI 1.189 ~ 9.085), serum potassium (OR=3.820, 95%CI 1.532 ~ 9.526), and MELD score (OR=1.108, 95%CI 1.061~1.157) were independent risk factors for nosocomial mortality in patients with liver cirrhosis combined with atrial arrhythmia. Conclusion: Patients with cirrhosis combined with atrial arrhythmias have more severe liver function damage and are more likely to develop complications such as ascites, hypersplenism, and hepatorenal syndrome. New-onset atrial fibrillation, esophageal-gastric varices, hyperkalemia, and a high MELD score are risk factors for nosocomial mortality in patients with liver cirrhosis combined with atrial arrhythmia, so more attention should be paid to corresponding patients for timely symptomatic treatment.
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Liu H, Wang X, He K, Chen Z, Li X, Ren J, Zhao X, Liu S, Zhou T, Chen H. Oxidized DJ-1 activates the p-IKK/NF-κB/Beclin1 pathway by binding PTEN to induce autophagy and exacerbate myocardial ischemia-reperfusion injury. Eur J Pharmacol 2024; 971:176496. [PMID: 38508437 DOI: 10.1016/j.ejphar.2024.176496] [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: 01/21/2024] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 03/22/2024]
Abstract
Patients with myocardial infarction have a much worse prognosis when they have myocardial ischemia-reperfusion (I/R) injury. Further research into the molecular basis of myocardial I/R injury is therefore urgently needed, as well as the identification of novel therapeutic targets and linkages to interventions. Three cysteine residues are present in DJ-1 at amino acids 46, 53, and 106 sites, with the cysteine at position 106 being the most oxidation-prone. This study sought to understand how oxidized DJ-1(C106) contributes to myocardial I/R damage. Rats' left anterior descending branches were tied off to establish a myocardial I/R model in vivo. A myocardial I/R model in vitro was established via anoxia/reoxygenation (A/R) of H9c2 cells. The results showed that autophagy increased after I/R, accompanied by the increased expression of oxidized DJ-1 (ox-DJ-1). In contrast, after pretreatment with NAC (N-acetylcysteine, a ROS scavenger) or Comp-23 (Compound-23, a specific antioxidant binding to the C106 site of DJ-1), the levels of ox-DJ-1, autophagy and LDH release decreased, and cell survival rate increased. Furthermore, the inhibition of interaction between ox-DJ-1 and PTEN could increase PTEN phosphatase activity, inhibit the p-IKK/NF-κB/Beclin1 pathway, reduce injurious autophagy, and alleviate A/R injury. However, BA (Betulinic acid, a NF-κB agonist) was able to reverse the protective effects produced by Comp-23 pretreatment. In conclusion, ox-DJ-1 could activate detrimental autophagy through the PTEN/p-IKK/NF-κB/Beclin1 pathway and exacerbate myocardial I/R injury.
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Hakonen M, Dahmani L, Lankinen K, Ren J, Barbaro J, Blazejewska A, Cui W, Kotlarz P, Li M, Polimeni JR, Turpin T, Uluç I, Wang D, Liu H, Ahveninen J. Individual connectivity-based parcellations reflect functional properties of human auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576475. [PMID: 38293021 PMCID: PMC10827228 DOI: 10.1101/2024.01.20.576475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.
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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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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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Gao X, Bian J, Luo J, Guo K, Xiang Y, Liu H, Ding J. Radiomics-based distinction of small (≤2 cm) hepatocellular carcinoma and precancerous lesions based on unenhanced MRI. Clin Radiol 2024; 79:e659-e664. [PMID: 38341345 DOI: 10.1016/j.crad.2024.01.019] [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: 06/09/2023] [Revised: 12/08/2023] [Accepted: 01/12/2024] [Indexed: 02/12/2024]
Abstract
AIM To assess the feasibility of a radiomics model based on unenhanced magnetic resonance imaging (MRI) to differentiate small hepatocellular carcinoma (S-HCC) (≤2 cm) and pre-hepatocellular carcinoma (Pre-HCC). MATERIALS AND METHODS One hundred and fourteen histopathologically confirmed 114 hepatic nodules were analysed retrospectively. All patients had undergone MRI before surgery using a 3 T MRI system. Each nodule was segmented on unenhanced MRI sequences (T1-weighted imaging [T1] and T2WI with fat-suppression [FS-T2]). Radiomics features were extracted and the optimal features were selected using the least absolute shrinkage and selection operator (LASSO). The support vector machine (SVM) was used to establish the radiomics model. One abdominal radiologist performed the conventional qualitative analysis for classification of S-HCC and Pre-HCC. The diagnostic performances of the radiomics and radiologist models were evaluated using receiver operating characteristic (ROC) analysis. RESULT Radiomics features (n=1,223) were extracted from each sequence and the optimal features were selected from T1, FS-T2, and T1+FS-T2 to construct the radiomics models. The radiomics model based on T1+FS-T2 showed the best performance among the three models, with areas under the ROC curves (AUCs) of 0.95 (95 % confidence interval [CI], 0.875-0.986) and 0.942 (95 % CI, 0.775-0.985), accuracies of 86 % and 88.5 %, sensitivities of 94.12 % and 100 %, and specificities of 85.48 % and 85.19 %, respectively. The radiomics model on FS-T2 showed better performance on a single sequence than that of the T1-based model. The diagnostic performance for the radiomic model was significantly higher than that for the radiologist (AUC = 0.518, p<0.05). CONCLUSION This study suggested that a radiomics model based on unenhanced MRI may serve as a feasible and non-invasive tool to classify S-HCC and Pre-HCC.
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