76
|
Nguyen J, Teklu Y, Wu S, Kolaitis N, De Marco T, Fanous S. Oral Selexipag versus Inhaled Treprostinil Effect on Pulmonary Arterial Hypertension. J Heart Lung Transplant 2022. [DOI: 10.1016/j.healun.2022.01.1077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
|
77
|
Buchberger D, Campbell S, Wu S, Lamarre E, Prendes B, Ku J, Scharpf J, Lorenz R, Silver N, Griffith C, Geiger J, Yilmaz E, Koyfman S, Woody N. Outcomes of Patients With Adenosquamous Carcinoma of the Head and Neck after Definitive Treatment. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
78
|
Wu S, Swanton A, Gross M. New Findings Regarding the Influence of Assistants on Intraoperative Inflatable Penile Prosthesis Complications. J Sex Med 2022. [DOI: 10.1016/j.jsxm.2022.01.226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
79
|
Vajapeyam S, Brown D, Ziaei A, Wu S, Vezina G, Stern J, Panigrahy A, Patay Z, Tamrazi B, Jones J, Haque S, Enterline D, Cha S, Jones B, Yeom K, Onar-Thomas A, Dunkel I, Fouladi M, Fangusaro J, Poussaint T. ADC Histogram Analysis of Pediatric Low-Grade Glioma Treated with Selumetinib: A Report from the Pediatric Brain Tumor Consortium. AJNR Am J Neuroradiol 2022; 43:455-461. [PMID: 35210278 PMCID: PMC8910799 DOI: 10.3174/ajnr.a7433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/01/2022] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND PURPOSE Selumetinib is a promising MAP (mitogen-activated protein) kinase (MEK) 1/2 inhibitor treatment for pediatric low-grade gliomas. We hypothesized that MR imaging-derived ADC histogram metrics would be associated with survival and response to treatment with selumetinib. MATERIALS AND METHODS Children with recurrent, refractory, or progressive pediatric low-grade gliomas who had World Health Organization grade I pilocytic astrocytoma with KIAA1549-BRAF fusion or the BRAF V600E mutation (stratum 1), neurofibromatosis type 1-associated pediatric low-grade gliomas (stratum 3), or sporadic non-neurofibromatosis type 1 optic pathway and hypothalamic glioma (OPHG) (stratum 4) were treated with selumetinib for up to 2 years. Quantitative ADC histogram metrics were analyzed for total and enhancing tumor volumes at baseline and during treatment. RESULTS Each stratum comprised 25 patients. Stratum 1 responders showed lower values of SD of baseline ADC_total as well as a larger decrease with time on treatment in ADC_total mean, mode, and median compared with nonresponders. Stratum 3 responders showed a greater longitudinal decrease in ADC_total. In stratum 4, higher baseline ADC_total skewness and kurtosis were associated with shorter progression-free survival. When all 3 strata were combined, responders showed a greater decrease with time in ADC_total mode and median. Compared with sporadic OPHG, neurofibromatosis type 1-associated OPHG had lower values of ADC_total mean, mode, and median as well as ADC_enhancement mean and median and higher values of ADC_total skewness and kurtosis at baseline. The longitudinal decrease in ADC_total median during treatment was significantly greater in sporadic OPHG compared with neurofibromatosis type 1-associated OPHG. CONCLUSIONS ADC histogram metrics are associated with progression-free survival and response to treatment with selumetinib in pediatric low-grade gliomas.
Collapse
|
80
|
Cao Z, Aharonian F, An Q, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai H, Cai JT, Cao Z, Chang J, Chang JF, Chen BM, Chen ES, Chen J, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen XL, Chen Y, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, Piazzoli BD, Dai BZ, Dai HL, Dai ZG, Della Volpe D, Dong XJ, Duan KK, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo FL, Guo JG, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JC, He SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu S, Hu SC, Hu XJ, Huang DH, Huang QL, Huang WH, Huang XT, Huang XY, Huang ZC, Ji F, Ji XL, Jia HY, Jiang K, Jiang ZJ, Jin C, Ke T, Kuleshov D, Levochkin K, Li BB, Li C, Li C, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li K, Li WL, Li XR, Li X, Li X, Li Y, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Liu ZX, Long WJ, Lu R, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Min Z, Mitthumsiri W, Montaruli T, Nan YC, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Rulev V, Sáiz A, Shao L, Shchegolev O, Sheng XD, Shi JR, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang BD, Wang C, Wang H, Wang HG, Wang JC, Wang JS, Wang LP, Wang LY, Wang RN, Wang W, Wang W, Wang XG, Wang XJ, Wang XY, Wang Y, Wang YD, Wang YJ, Wang YP, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu WX, Wu XF, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xiao HB, Xin GG, Xin YL, Xing Y, Xu DL, Xu RX, Xue L, Yan DH, Yan JZ, Yang CW, Yang FF, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang HM, Zhang HY, Zhang JL, Zhang JW, Zhang LX, Zhang L, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang YL, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng Y, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Exploring Lorentz Invariance Violation from Ultrahigh-Energy γ Rays Observed by LHAASO. PHYSICAL REVIEW LETTERS 2022; 128:051102. [PMID: 35179919 DOI: 10.1103/physrevlett.128.051102] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/06/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
Recently, the LHAASO Collaboration published the detection of 12 ultrahigh-energy γ-ray sources above 100 TeV, with the highest energy photon reaching 1.4 PeV. The first detection of PeV γ rays from astrophysical sources may provide a very sensitive probe of the effect of the Lorentz invariance violation (LIV), which results in decay of high-energy γ rays in the superluminal scenario and hence a sharp cutoff of the energy spectrum. Two highest energy sources are studied in this work. No signature of the existence of the LIV is found in their energy spectra, and the lower limits on the LIV energy scale are derived. Our results show that the first-order LIV energy scale should be higher than about 10^{5} times the Planck scale M_{Pl} and that the second-order LIV scale is >10^{-3}M_{Pl}. Both limits improve by at least one order of magnitude the previous results.
Collapse
|
81
|
Wu S, Minoli M, De Menna M, Thalmann G, Kruithof-De Julio M. Application of a new 3D culture system to culture uniformly sized patient-derived bladder cancer (BLCa) organoids. Eur Urol 2022. [DOI: 10.1016/s0302-2838(22)00170-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
82
|
Wang K, Guo K, Ji Z, Liu Y, Chen F, Wu S, Zhang Q, Yao Y, Zhou Q. Association of Preeclampsia with Incident Dementia and Alzheimer’s Disease among Women in the Framingham Offspring Study. J Prev Alzheimers Dis 2022; 9:725-730. [DOI: 10.14283/jpad.2022.62] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
83
|
Edmonston D, Wu S, Li Y, Khan R, Boop F, Merchant T. Limited Surgery and Conformal Photon Radiation Therapy for Pediatric Craniopharyngioma: Long-Term Results From the RT1 Protocol. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
84
|
Peng Y, Wu S, Liu Y, Chen M, Miao J, Zhao C, Chen S, Qi Z, Deng X. Synthetic CT Generation From Multi-Sequence MR Images for Head and Neck MRI-Only Radiotherapy via Cycle-Consistent Generative Adversarial Network. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
85
|
Zhao X, Xuan L, Yin J, Tang Y, Sun H, Wu S, Jing H, Fang H, Song Y, Jin J, Liu Y, Chen B, Qi S, Li N, Tang Y, Lu N, Yang Y, Li Y, Sun B, Wang S. Radiotherapy in Breast Cancer Patients With Isolated Regional Recurrence After Mastectomy: A Joint Analysis of 144 Cases From Two Institutions. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
86
|
Konieczkowski D, Otani K, Drumm M, Wu S, Saylor P, Wu C, Efstathiou J, Miyamoto D. Impact of AR-V7 and Other Androgen Receptor Splice Variant Expression on Outcomes of Post-Prostatectomy Salvage Therapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
87
|
Wang R, Wu S, Qian D, Zhang Y, Fan B, Hu M. A Lung Cancer Auxiliary Diagnostic Method: Deep Learning Based Mediastinal Lymphatic Partitions Segmentation for Cancer Staging. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
88
|
Chen M, Wu S, Zhao W, Zhou Y, Zhou Y, Wang G. Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy. Cancer Radiother 2021; 26:494-501. [PMID: 34711488 DOI: 10.1016/j.canrad.2021.08.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/30/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022]
Abstract
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicated process. In recent years, automatic contouring of TV and OARs has rapidly developed due to the advances in deep learning (DL). This technology has the potential to save time and to reduce intra- or inter-observer variability. In this paper, the authors provide an overview of RT, introduce the concept of DL, summarize the data characteristics of the included literature, summarize the possible challenges for DL in the future, and discuss the possible research directions.
Collapse
|
89
|
Jiang C, Wu S, Wang M, Li H, Zhao X. J-shaped relationship between admission diastolic blood pressure and 2-year cardiovascular mortality in elderly patients with acute coronary syndrome. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.1363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objective
To investigate the relationship between admission diastolic blood pressure (DBP) and subsequent cardiovascular and all-cause mortality in elderly patients with acute coronary syndrome (ACS).
Methods
This is a retrospective observational study. Consecutive patients ≥65 years of age admitted for ACS at a 2,300-bed tertiary hospital from December 2012 to July 2019 were included. The association between admission DBP and cardiovascular and all-cause mortality during hospitalization and over the follow-up period among this population were analyzed using multivariate COX regression model. Results were presented according to DBP quartiles: Q1, less than 67 mm Hg; Q2, from 67 to 72 mm Hg; Q3, from 73 to 80 mm Hg; and Q4, above 80 mm Hg.
Results
A total of 6 785 patients were included in this cohort study. Mean (SD) patient age was 74.0 (6.5) years, and 47.6% were women. Mean (SD) follow-up time was 2.54 (1.82) years. A non-linear relation was observed between DBP at admission and cardiovascular and all-cause mortality during hospitalization and over the follow-up period using restricted cubic splines. After adjustment for potential confounders, patients in Q3 or Q2 had lower risk for 2-year cardiovascular death by Cox proportional hazard model compared with patients in Q4 (hazard ratio [HR] 0.66; 95% confidence interval [CI], 0.48–0.90, P=0.010, for Q3 vs Q4; and HR 0.72; 95% CI, 0.53–0.99, P=0.041, for Q2vs Q4), while patients in Q1 had similar risk for cardiovascular death with that of patients in Q4. Meanwhile, when compared with patients in Q1, patients in Q3 had lower risk for 2-year cardiovascular death (HR, 0.72; 95% CI, 0.53–0.97, P=0.033). However, lower or higher admission DBP was not an independent predictor of 2-year all-cause mortality in this population.
Conclusion
Among patients aged ≥65 years admitted for ACS, there is a J-curve relationship between supine admission DBP and risk for 2-year cardiovascular death, with a nadir at 73–80 mm Hg.
Funding Acknowledgement
Type of funding sources: Other. Main funding source(s): the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support Study population and selectionAdjusted multivariate COX regression
Collapse
|
90
|
Guo W, Liang N, Ma Q, Chen X, Liu R, Wu S, Bao H, Wu X, Shao Y, Qiu B, Wang D, Tan F, Gao Y, Xue Q, Gao S. MA07.07 Detecting Stage I Lung Cancer with High Sensitivity Using Genome-wide Multi-dimensional Fragmentomic Profiles of Cell Free DNA. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
91
|
Rogg J, Ben Ma Z, Pandya M, Wu S, Sharma K. 284 An Analysis of a Novel Telemedicine Intervention to Decrease Emergency Department Visits in a County Hospital System. Ann Emerg Med 2021. [DOI: 10.1016/j.annemergmed.2021.09.297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
92
|
Zhi X, Liu J, Wu S, Niu C. A generalized l 2,p-norm regression based feature selection algorithm. J Appl Stat 2021; 50:703-723. [PMID: 36819074 PMCID: PMC9930865 DOI: 10.1080/02664763.2021.1975662] [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: 09/30/2020] [Accepted: 08/28/2021] [Indexed: 10/20/2022]
Abstract
Feature selection is an important data dimension reduction method, and it has been used widely in applications involving high-dimensional data such as genetic data analysis and image processing. In order to achieve robust feature selection, the latest works apply the l 2 , 1 or l 2 , p -norm of matrix to the loss function and regularization terms in regression, and have achieved encouraging results. However, these existing works rigidly set the matrix norms used in the loss function and the regularization terms to the same l 2 , 1 or l 2 , p -norm, which limit their applications. In addition, the algorithms for solutions they present either have high computational complexity and are not suitable for large data sets, or cannot provide satisfying performance due to the approximate calculation. To address these problems, we present a generalizedl 2 , p -norm regression based feature selection ( l 2 , p -RFS) method based on a new optimization criterion. The criterion extends the optimization criterion of ( l 2 , p -RFS) when the loss function and the regularization terms in regression use different matrix norms. We cast the new optimization criterion in a regression framework without regularization. In this framework, the new optimization criterion can be solved using an iterative re-weighted least squares (IRLS) procedure in which the least squares problem can be solved efficiently by using the least square QR decomposition (LSQR) algorithm. We have conducted extensive experiments to evaluate the proposed algorithm on various well-known data sets of both gene expression and image data sets, and compare it with other related feature selection methods.
Collapse
|
93
|
Yang L, Wang Z, Wu S, Lu WJ, Xiong H. [The correlation between post-allo-HSCT CMV infection and the difference affinity of donor HLA-type recognition of CMV antigen peptide in children]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2021; 42:757-762. [PMID: 34753231 PMCID: PMC8607047 DOI: 10.3760/cma.j.issn.0253-2727.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Indexed: 11/07/2022]
Abstract
Objective: To explore the correlation between the affinity while donor's HLA type recognizing different cytomegalovirus (CMV) antigen peptide and the occurrence of CMV infection after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in children. Methods: To investigate the relationship between CMV reactivation, CMV infection or CMV related tissue/organ diseases and the different HLA-type recognition antigen peptide of the donors, we retrospectively analyzed the clinical data of 146 children with CMV infection for 6 months since from the time they underwent transplantation in Wuhan Children's Hospital. Results: Among 146 patients, the HLA type of 82 (56.16%) cases had high affinity with PP65 alone, and 34 cases of CMV infection occurred after transplantation (41.46%) . None of 5 cases that had a high affinity with IE-1 alone got CMV infection. None of 2 cases with no clear high-affinity peptide had CMV infection. Three of 5 cases that had a high affinity with PP65 and PP50 had CMV infection. Thirteen of 52 cases that had a high affinity with PP65 and IE-1 had CMV infection (25.00%) . HLA with exclusive PP50 high affinity was not encountered. Donors with a high-affinity HLA locus associated with IE-1 showed a lower incidence of CMV infection after HSCT compared to those carrying only the PP65 high-affinity allele (22.81% vs 41.46%, P=0.029) . Conclusion: HLA type with PP65 and IE-1 high-affinity covers approximately 99.8% of the donors. Stem cells generated from HLA donors with high affinity with the CMV antigen peptide IE-1 can reduce the risk of post-transplantation CMV-activated infection in children.
Collapse
|
94
|
Zhen Q, Zhang Y, Yu Y, Yang H, Zhang T, Li X, Mo X, Li B, Wu J, Liang Y, Ge H, Xu Q, Chen W, Qian W, Xu H, Chen G, Bai B, Zhang J, Lu Y, Chen S, Zhang H, Zhang Y, Chen X, Li X, Jin X, Lin X, Yong L, Fang M, Zhao J, Lu Y, Wu S, Jiang D, Shi J, Cao H, Qiu Y, Li S, Kang X, Shen J, Ma H, Sun S, Fan Y, Chen W, Bai M, Jiang Q, Li W, Lv C, Li S, Chen M, Li F, Li Y, Sun L. Three Novel Structural Variations at MHC and IL12B Predisposing to Psoriasis. Br J Dermatol 2021; 186:307-317. [PMID: 34498260 DOI: 10.1111/bjd.20752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Structural variations (SVs, defined as DNA variants ≥50 bp) have been associated with various complex human diseases. However, research to screen the whole genome for SVs predisposing to psoriasis is still lacking. OBJECTIVES This study aimed to investigate the association of SVs and psoriasis. METHODS We performed a genome-wide screen on SVs using an imputation method on 5 independent cohorts with 45,386 subjects from the Chinese Han population. Fine mapping analysis, genetic interaction analysis and RNA expression analysis were conducted to explore the mechanism of SVs. RESULTS We obtained 4,535 SVs in total and identified 2 novel deletions (esv3608550, OR=2.73, P<2.00×10-308 ; esv3608542, OR=0.47, P=7.40×10-28 ) at 6q21.33 (MHC), 1 novel Alu element insertion (esv3607339, OR=1.22, P=1.18×10-35 ) at 5q33.3 (IL12B), and confirmed 1 previously reported deletion (esv3587563, OR=1.30, P=9.52×10-60 ) at 1q21.2 (LCE) for psoriasis. Fine mapping analysis including SNPs and small Insertions/Deletions (InDels) revealed that esv3608550 and esv3608542 were independently associated with psoriasis, and a novel independent SNP (rs9378188, OR=1.65, P=3.46×10-38 ) was identified at 6q21.33. By genetic interaction analysis and RNA expression analysis, we speculate that the association of 2 deletions at 6q21.33 with psoriasis might relate to their influence on the expression of HLA-C. CONCLUSIONS Our study constructed the most comprehensive SV map for psoriasis thus far and enriched the genetic architecture and pathogenesis of psoriasis as well as highlighted the nonnegligible impact of SVs on complex diseases.
Collapse
|
95
|
Aharonian F, An Q, Axikegu, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai H, Cai JT, Cao Z, Cao Z, Chang J, Chang JF, Chang XC, Chen BM, Chen J, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen XL, Chen Y, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Volpe DD, Piazzoli BD, Dong XJ, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng YL, Gao B, Gao CD, Gao Q, Gao W, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo JG, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JC, He SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu S, Hu SC, Hu XJ, Huang DH, Huang QL, Huang WH, Huang XT, Huang Y, Huang ZC, Ji F, Ji XL, Jia HY, Jiang K, Jiang ZJ, Jin C, Kuleshov D, Levochkin K, Li BB, Li C, Li C, Li F, Li HB, Li HC, Li HY, Li J, Li K, Li WL, Li X, Li X, Li XR, Li Y, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu YN, Liu ZX, Long WJ, Lu R, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Mitthumsiri W, Montaruli T, Nan YC, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Ruffolo D, Rulev V, Sáiz A, Shao L, Shchegolev O, Sheng XD, Shi JR, Song HC, Stenkin YV, Stepanov V, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang BD, Wang C, Wang H, Wang HG, Wang JC, Wang JS, Wang LP, Wang LY, Wang RN, Wang W, Wang W, Wang XG, Wang XJ, Wang XY, Wang YD, Wang YJ, Wang YP, Wang Z, Wang Z, Wang ZH, Wang ZX, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu WX, Wu XF, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao G, Xiao HB, Xin GG, Xin YL, Xing Y, Xu DL, Xu RX, Xue L, Yan DH, Yang CW, Yang FF, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang HM, Zhang HY, Zhang JL, Zhang JW, Zhang L, Zhang L, Zhang LX, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang Y, Zhang Y, Zhang YF, Zhang YL, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng Y, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. A dynamic range extension system for LHAASO WCDA-1. RADIATION DETECTION TECHNOLOGY AND METHODS 2021. [DOI: 10.1007/s41605-021-00275-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
96
|
Zhang X, Wang Z, Wang X, Tang W, Liu R, Bao H, Chen X, Wu S, Wu X, Shao Y, Fan J, Zhou J. 950P Ultra-sensitive and cost-effective method for early stage hepatocellular carcinoma and intrahepatic cholangiocarcinoma detection using plasma cfDNA fragmentomic profiles. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
|
97
|
Li A, Zhang W, Wu S, Li J, Yu Y. 1021P Long non-coding RNAs influence cancer immunotherapy efficacy through regulating T-cell infiltration and activity or tumor antigenicity. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
|
98
|
Wu S, Liu S, Chen A, Qian D, Lu Y. OC-0477 Self-attention Condition GAN for Synthetic CT Generation from CBCT for Head and Neck Radiotherapy. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)06924-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
99
|
Jabri MA, Wu S, Zhang Y, Wang H, Pan Y, Ma J, Wang L. Accuracy of Bolton's Analysis among Different Malocclusion Patients Plaster Models and Digital Models Obtained by Ex Vivo Scanning with iTero Scanner in Chinese Han Population. Niger J Clin Pract 2021; 24:1086-1091. [PMID: 34290188 DOI: 10.4103/njcp.njcp_307_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Aim This investigation aimed to compare the accuracy of Bolton's analysis on plaster models of various malocclusion groups by utilizing digital calipers and iTero scanner. Materials and Methods The data consisted maxillary and mandibular plaster study casts of 61 patients (Class I-20, Class II-20, Class III-21) there were 31 males and 30 females. iTero®element scanner was utilized to scan the models and Bolton's analysis was performed on digital models. Also, the Digital caliper was utilized to perform the Manual measurements. Mesiodistal tooth widths, Anterior and Overall Bolton ratio was measured utilizing OrthoCAD™ software on digital models and plaster models with digital calipers. Statistical analysis was performed utilizing One-way ANOVA and independent T-test. Results Results revealed anterior and overall Bolton ratios showed significant differences (P < 0.05) for the measurements performed utilizing digital models. Anterior ratio for (Group 1) iTero measurements depicted the statistical significant value (P < 0.03) and overall ratio for (Group 2) digital caliper measurements depicted the statistical significant value (P < 0.02). Conclusion With the introduction of intra-oral laser scanners it has become more convenient for the practitioner to perform the intra-oral digital scanning and carry out the model analysis digitally and iTero scanner can also be utilized extra-orally to perform the scanning and model analysis. Our study concludes that intra-oral laser scanner like iTero is more convenient for an orthodontist, and can be utilized for extra-oral scanning of orthodontic dental models as the measurements obtained on digital models was as accurate as the conventional method.
Collapse
|
100
|
Cao Z, Aharonian F, An Q, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai H, Cai JT, Cao Z, Chang J, Chang JF, Chen BM, Chen ES, Chen J, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen XL, Chen Y, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, D'Ettorre Piazzoli B, Dai BZ, Dai HL, Dai ZG, Della Volpe D, Dong XJ, Duan KK, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo FL, Guo JG, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JC, He SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu S, Hu SC, Hu XJ, Huang DH, Huang QL, Huang WH, Huang XT, Huang XY, Huang ZC, Ji F, Ji XL, Jia HY, Jiang K, Jiang ZJ, Jin C, Ke T, Kuleshov D, Levochkin K, Li BB, Li C, Li C, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li K, Li WL, Li XR, Li X, Li X, Li Y, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Liu ZX, Long WJ, Lu R, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Min Z, Mitthumsiri W, Montaruli T, Nan YC, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Rulev V, Saiz A, Shao L, Shchegolev O, Sheng XD, Shi JY, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang BD, Wang C, Wang H, Wang HG, Wang JC, Wang JS, Wang LP, Wang LY, Wang RN, Wang W, Wang W, Wang XG, Wang XJ, Wang XY, Wang Y, Wang YD, Wang YJ, Wang YP, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu WX, Wu XF, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xiao HB, Xin GG, Xin YL, Xing Y, Xu DL, Xu RX, Xue L, Yan DH, Yan JZ, Yang CW, Yang FF, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang HM, Zhang HY, Zhang JL, Zhang JW, Zhang LX, Zhang L, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang YL, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng Y, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Peta-electron volt gamma-ray emission from the Crab Nebula. Science 2021; 373:425-430. [PMID: 34261813 DOI: 10.1126/science.abg5137] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/23/2021] [Indexed: 11/03/2022]
Abstract
The Crab Nebula is a bright source of gamma rays powered by the Crab Pulsar's rotational energy through the formation and termination of a relativistic electron-positron wind. We report the detection of gamma rays from this source with energies from 5 × 10-4 to 1.1 peta-electron volts with a spectrum showing gradual steepening over three energy decades. The ultrahigh-energy photons imply the presence of a peta-electron volt electron accelerator (a pevatron) in the nebula, with an acceleration rate exceeding 15% of the theoretical limit. We constrain the pevatron's size between 0.025 and 0.1 parsecs and the magnetic field to ≈110 microgauss. The production rate of peta-electron volt electrons, 2.5 × 1036 ergs per second, constitutes 0.5% of the pulsar spin-down luminosity, although we cannot exclude a contribution of peta-electron volt protons to the production of the highest-energy gamma rays.
Collapse
|