1
|
Cai C, Zeng W, Wang H, Ren S. Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study. Int J Chron Obstruct Pulmon Dis 2024; 19:933-943. [PMID: 38646605 PMCID: PMC11027921 DOI: 10.2147/copd.s452444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/06/2024] [Indexed: 04/23/2024] Open
Abstract
Purpose Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD. Patients and Methods In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR. Results We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P<0.001), PLR (rs=0.4424, P<0.001), and MLR (rs=0.4628, P<0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD. Conclusion The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept.
Collapse
|
2
|
Cao Z, Aharonian F, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Bian W, Bukevich AV, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen HX, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen S, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang JH, Fang K, Feng CF, Feng H, Feng L, Feng SH, Feng XT, Feng Y, Feng YL, Gabici S, Gao B, Gao CD, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, Hasan M, He HH, He HN, He JY, He Y, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Karpikov I, Kuleshov D, Kurinov K, Li BB, Li CM, Li C, Li C, Li D, Li F, Li HB, Li HC, Li J, Li J, Li K, Li SD, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu DB, Liu H, Liu HD, Liu J, Liu JL, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Luo Q, Luo Y, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou LJ, Pattarakijwanich P, Pei ZY, Qi JC, Qi MY, Qiao BQ, Qin JJ, Raza A, Ruffolo D, Sáiz A, Saeed M, Semikoz D, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun DX, Sun QN, Sun XN, Sun ZB, Takata J, Tam PHT, Tang QW, Tang R, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu QW, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xiang GM, Xiao DX, Xiao G, Xin YL, Xing Y, Xiong DR, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang CY, Yang F, Yang FF, Yang LL, Yang MJ, Yang RZ, Yang WX, Yao YH, Yao ZG, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang H, Zhang HM, Zhang HY, Zhang JL, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zhao XH, Zheng F, Zhong WJ, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhou XX, Zhu BY, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zou YC, Zuo X. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A. PHYSICAL REVIEW LETTERS 2024; 132:131002. [PMID: 38613275 DOI: 10.1103/physrevlett.132.131002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 04/14/2024]
Abstract
We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be -2.7413±0.0004±0.0050, while above the knee, it is -3.128±0.005±0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.
Collapse
|
3
|
Wang X, Zhu N, Zeng W, Wang P. Hemoglobin variability in patients receiving EPO and roxadustat during maintenance hemodialysis: a self-control study. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2024; 28:303-309. [PMID: 38235900 DOI: 10.26355/eurrev_202401_34917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
OBJECTIVE The aim of this study was to investigate the hemoglobin variability in patients undergoing maintenance hemodialysis during the application of erythropoietin (EPO) and roxadustat. PATIENTS AND METHODS For this retrospective study, we analyzed the clinical records of 80 patients with renal anemia on maintenance hemodialysis (MHD) admitted to our hospital between January 2017 and December 2022. We adopted a self-control design comparing the hemoglobin variability of the values before and after roxadustat administration in each patient. The patients received EPO from January 2017 to December 2019 and roxadustat from January 2020 to December 2022. We compared the levels of serum ferritin, transferrin saturation, and hemoglobin and calculated the hemoglobin variabilities by comparing values before and after roxadustat treatments. RESULTS We found higher transferrin saturation levels at different time points after the roxadustat treatments (p<0.01); meanwhile, the serum ferritin and hemoglobin levels were significantly higher after the roxadustat treatment (p<0.001). During the treatments with EPO and roxadustat, the transferrin saturation, serum ferritin, and hemoglobin levels differed significantly at different time points for each patient (p<0.05). After roxadustat administration, the hemoglobin levels were significantly higher than after EPO administration (p<0.001) and changed more rapidly after roxadustat administration than after EPO administration (p<0.05). The hemoglobin variability after roxadustat administration was significantly lower than that after EPO administration (p<0.05). CONCLUSIONS Treatment with roxadustat led to higher hemoglobin levels and less hemoglobin variability than the treatment with EPO, with high transferrin saturation and higher ferritin levels in patients with renal anemia on MHD.
Collapse
|
4
|
Cao Z, Aharonian F, An Q, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Della Volpe D, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gabici S, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JY, He XB, He Y, Heller M, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Ke T, Kuleshov D, Kurinov K, Li BB, Li C, Li C, Li D, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li J, Li K, Li WL, Li WL, Li XR, Li X, 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 JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Semikoz D, Shao CY, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang QW, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang F, Yang FF, Yang HW, 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, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A. PHYSICAL REVIEW LETTERS 2023; 131:151001. [PMID: 37897763 DOI: 10.1103/physrevlett.131.151001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/08/2023] [Accepted: 08/18/2023] [Indexed: 10/30/2023]
Abstract
The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of -2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of -2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.
Collapse
|
5
|
Zeng L, Zeng W, Gao Q, Qiao N, Du K, Yue A. Anaemia prevalence and risk factors among children aged 6 to 23 months in rural China. Hong Kong Med J 2023; 29:432-442. [PMID: 37524686 DOI: 10.12809/hkmj219899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023] Open
Abstract
INTRODUCTION Anaemia is a global public health problem among children. However, few studies have examined anaemia prevalence and risk factors among Chinese children of different ages, particularly in poor rural areas. This study investigated these two aspects among children aged 6 to 23 months in poor rural areas of China. METHODS This cross-sectional study included 1132 children aged 6 to 23 months in three prefectures of the Qinba Mountains area. A finger prick blood test for haemoglobin and anaemia was conducted, along with household surveys of socio-demographic characteristics, illness characteristics, and feeding practices. Multiple linear and logistic regression analyses were used to determine predictors of anaemia. RESULTS Overall, 42.6% of children in the study displayed anaemia. Children aged 6 to 11 months had the highest anaemia prevalence (53.6%). Anaemia risk factors differed among age-groups and throughout the overall sample. Bivariate and multivariable regression results showed that continued breastfeeding, any history of formula feeding, and consumption of iron-rich or iron-fortified foods were prominent risk factors for anaemia. However, continued breastfeeding and any history of formula feeding had the greatest impact across age-groups (both P<0.05). CONCLUSION Anaemia remains a severe public health problem among children aged 6 to 23 months in rural China. Healthy feeding practices, nutritional health knowledge, and nutrition improvement projects are needed to reduce the burden of anaemia among children in rural areas of China.
Collapse
|
6
|
Zuo Z, Zeng W, Peng K, Mao Y, Wu Y, Zhou Y, Qi W. Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study. Clin Radiol 2023; 78:e698-e706. [PMID: 37487842 DOI: 10.1016/j.crad.2023.07.002] [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: 04/01/2022] [Revised: 11/30/2022] [Accepted: 07/01/2023] [Indexed: 07/26/2023]
Abstract
AIM To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs). MATERIALS AND METHODS This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration. RESULTS The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration. CONCLUSION The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.
Collapse
|
7
|
Peng Y, Wang LY, Zhang G, Liu JQ, Zeng W, Li Z, Lu X. [Construction of a dual fluorescent reporter system for tracing horizontal transfer of mcr-1-carrying plasmid]. ZHONGHUA YU FANG YI XUE ZA ZHI [CHINESE JOURNAL OF PREVENTIVE MEDICINE] 2023; 57:1063-1067. [PMID: 37400217 DOI: 10.3760/cma.j.cn112150-20230103-00004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The green fluorescent reporter gene was inserted into the gene interval of polymyxin resistant mcr-1-carrying plasmid (pSH13G841) by homologous recombination of suicide plasmid. At the same time, E. coli J53 with red fluorescent reporter gene was constructed. Using the ability of spontaneous conjugation of drug resistant plasmid (pSH13G841), pSH13G841-GFP plasmid was transferred into J53 RFP bacteria to construct a double fluorescent labeled donor bacterium. The two light-emitting systems could stably and spontaneously express fluorescence without mutual interference. The dual fluorescence report system constructed can be used for visual tracing horizontal transfer of mcr-1-carrying plasmid, the subsequent model can study the colonization, transfer and prognosis of drug-resistant bacteria/drug-resistant genes mcr-1 by using mouse in vivo imaging technology.
Collapse
|
8
|
Cheng D, Li Z, Zeng W, Jiang T, Guo Y, Zhang Y. [Progress of researches on the role and mechanisms of non - coding RNA in Angiostrongylus cantonensis infection]. ZHONGGUO XUE XI CHONG BING FANG ZHI ZA ZHI = CHINESE JOURNAL OF SCHISTOSOMIASIS CONTROL 2023; 35:407-412. [PMID: 37926478 DOI: 10.16250/j.32.1374.2022283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Angiostrongylus cantonensis is a food-borne zoonotic parasite, and human infection may cause eosinophilic meningitis. Non-coding RNAs (ncRNAs) may regulate physiological and pathological processes at multiple biological levels; however, there are few studies pertaining to the regulatory role of ncRNAs in A. cantonensis infection. Based on publications retrieved from PubMed, Wanfang Data and CNKI, the regulatory role of ncRNAs in A. cantonensis infections mainly includes immune responses, cell apoptosis and signaling transduction, and ncRNAs may serve as biomarkers for diagnosis of angiostrongyliasis. This review summarizes the main roles of ncRNAs in A. cantonensis infections and the underlying mechanisms, so as to provide insights into diagnosis and treatment of angiostrongyliasis.
Collapse
|
9
|
Cao Z, Aharonian F, An Q, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng HL, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Della Volpe D, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JY, He XB, He Y, Heller M, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Ke T, Kuleshov D, Kurinov K, Li BB, Li C, Li C, Li D, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li J, Li K, Li WL, Li WL, Li XR, Li X, 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 JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Long WJ, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Nan YC, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Shao CY, Shao L, Shchegolev O, Sheng XD, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang JS, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RF, Xu RX, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang F, Yang FF, Yang HW, 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, Yue H, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhang B, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng JH, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. A tera-electron volt afterglow from a narrow jet in an extremely bright gamma-ray burst. Science 2023:eadg9328. [PMID: 37289911 DOI: 10.1126/science.adg9328] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/25/2023] [Indexed: 06/10/2023]
Abstract
Some gamma-ray bursts (GRBs) have a tera-electron volt (TeV) afterglow, but the early onset of this has not been observed. We report observations with the Large High Altitude Air Shower Observatory of the bright GRB 221009A, which serendipitously occurred within the instrument field of view. More than 64,000 photons >0.2 TeV were detected within the first 3000 seconds. The TeV flux began several minutes after the GRB trigger, then rose to a peak about 10 seconds later. This was followed by a decay phase, which became more rapid ~650 seconds after the peak. We interpret the emission using a model of a relativistic jet with half-opening angle ~0.8°. This is consistent with the core of a structured jet and could explain the high isotropic energy of this GRB.
Collapse
|
10
|
Zeng W, Zhou SL, Guo JX, Tang W. [Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning]. ZHONGHUA KOU QIANG YI XUE ZA ZHI = ZHONGHUA KOUQIANG YIXUE ZAZHI = CHINESE JOURNAL OF STOMATOLOGY 2023; 58:542-548. [PMID: 37271998 DOI: 10.3760/cma.j.cn112144-20230302-00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Objective: To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. Methods: The CT data of patients treated in the Department of Radiology, West China Hospital of Stomatology, Sichuan University from January 2017 to June 2022 were collected. A total of 1 000 cases of artifact-free CT data and 620 cases of metal artifact CT data were obtained, including 5 types of metal restorative materials, namely, fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies. Four hundred metal artifact CT data and 1 000 artifact-free CT data were utilized for simulation synthesis, and 1 000 pairs of simulated artifacts and metal images and simulated metal images (200 pairs of each type) were constructed. Under the condition that the data of the five metal artifacts were equal, the entire data set was randomly (computer random) divided into a training set (800 pairs) and a test set (200 pairs). The former was used to train the GAN model, and the latter was used to evaluate the performance of the GAN model. The test set was evaluated quantitatively and the quantitative indexes were root-mean-square error (RMSE) and structural similarity index measure (SSIM). The trained GAN model was employed to eliminate the metal artifacts from the CT data of the remaining 220 clinical cases of metal artifact CT data, and the elimination results were evaluated by two senior attending doctors using the modified LiKert scale. Results: The RMSE values for artifact elimination of fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies in test set were 0.018±0.004, 0.023±0.007, 0.015±0.003, 0.019±0.004, 0.024±0.008, respectively (F=1.29, P=0.274). The SSIM values were 0.963±0.023, 0.961±0.023, 0.965±0.013, 0.958±0.022, 0.957±0.026, respectively (F=2.22, P=0.069). The intra-group correlation coefficient of 2 evaluators was 0.972. For 220 clinical cases, the overall score of the modified LiKert scale was (3.73±1.13), indicating a satisfactory performance. The scores of modified LiKert scale for fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies were (3.68±1.13), (3.67±1.16), (3.97±1.03), (3.83±1.14), (3.33±1.12), respectively (F=1.44, P=0.145). Conclusions: The metal artifact reduction GAN model constructed in this study can effectively remove the interference of metal artifacts and improve the image quality.
Collapse
|
11
|
Yang ZM, Huang J, Chen XM, Meng X, Qiu Y, Zeng W, Zhang JQ. [Advances in clinical characteristics of talaromycosis combined with other opportunistic infections]. ZHONGHUA JIE HE HE HU XI ZA ZHI = ZHONGHUA JIEHE HE HUXI ZAZHI = CHINESE JOURNAL OF TUBERCULOSIS AND RESPIRATORY DISEASES 2023; 46:503-506. [PMID: 37147814 DOI: 10.3760/cma.j.cn112147-20220807-00659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Talaromycosis (TSM) is an opportunistic deep mycosis prevalent in southeast Asia and southern China, affecting HIV-positive, anti-interferon-gamma autoantibody-positive and other immunodeficiency hosts. These hosts are often co-infected with mycobacterium tuberculosis, non-tuberculosis mycobacteria, bacteria, fungi, viruses and other opportunistic infections. The clinical characteristics and the pathogenic spectrum of TSM with opportunistic infections vary with different immune states. The rates of misdiagnosis, missed diagnosis and mortality are high. This review summarized the clinical characteristics of TSM with opportunistic infections in order to improve the level of clinical diagnosis and treatment.
Collapse
|
12
|
Wang S, Zhao J, Wang L, Zhang T, Zeng W, Lu H. METTL21C mediates lysine trimethylation of IGF2BP1 to regulate chicken myoblast proliferation. Br Poult Sci 2023; 64:74-80. [PMID: 36069737 DOI: 10.1080/00071668.2022.2121639] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
1. Methyltransferase-like 21C (METTL21C) and insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) play important roles in the proliferation of chicken myoblasts. However, it remains unclear whether there is protein-protein interaction between METTL21C and IGF2BP1 to regulate proliferation of chicken myoblasts.2. In this study, the Igf2bp1 gene was amplified from cDNA of liver tissue of Lueyang black-bone chicken to construct the overexpression vector HA-Igf2bp1. The HA-Igf2bp1 and Flag-Mettl21c vectors were individually transfected and co-transfected into HEK293T, respectively. Co-immunoprecipitation (Co-IP) assay indicated a protein-protein interaction between METTL21C and IGF2BP1.3. Using the Western blotting and LC-MS/MS, it was found that METTL21C could mediate the lysine methylation modification of IGF2BP1. Furthermore, the His-tagged overexpression vector HA-Igf2bp1-His was constructed, transfected and co-transfected with Flag-Mettl21c into HEK293T. His-tagged IGF2BP1 was purified by nickel ion affinity chromatography. Western blotting revealed that IGF2BP1 was successfully purified, and the trimethylation modification level of co-transfection group was significantly elevated compared with the single-transfection Igf2bp1 group.4. Mettl21c and Igf2bp1 overexpression vectors were transfected and co-transfected into primary chicken myoblasts, respectively. The results of 5-ethynyl-2'-deoxyuridine assay and the expression level of Pax7 and MyoD indicated that overexpression of Igf2bp1 alone inhibited the chicken myoblast proliferation, whereas co-expression of Mettl21c and Igf2bp1 eliminated the inhibitory effects of Igf2bp1, thereby favouring cell proliferation and differentiation.5. The results, for the first time, revealed that METTL21C mediated the lysine trimethylation modification of IGF2BP1 to regulate the proliferation of chicken myoblasts, which provided a new insight into in-depth analysis of the molecular mechanism of METTL21C methylation involved in regulating the growth and development of skeletal muscle in Lueyang black-bone chicken.
Collapse
|
13
|
Cao Z, Aharonian F, An Q, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Z, Chang J, Chang JF, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng HL, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, D'Ettorre Piazzoli B, Dai BZ, Dai HL, Dai ZG, Della Volpe D, Duan KK, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, 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 SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu Q, Hu S, Hu SC, Hu XJ, Huang DH, Huang WH, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang ZJ, Jin M, Kang MM, 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 J, Li K, Li WL, Li XR, Li X, Li X, 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, Long WJ, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Min Z, Mitthumsiri W, Nan YC, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Shao CY, 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 R, Wang RN, Wang W, Wang XG, 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 XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RX, Xue L, Yan DH, Yan JZ, Yang CW, Yang FF, Yang HW, 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, Yue H, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, 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, Ando S, Chianese M, Fiorillo DFG, Miele G, Ng KCY. Constraints on Heavy Decaying Dark Matter from 570 Days of LHAASO Observations. PHYSICAL REVIEW LETTERS 2022; 129:261103. [PMID: 36608208 DOI: 10.1103/physrevlett.129.261103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/19/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
The kilometer square array (KM2A) of the large high altitude air shower observatory (LHAASO) aims at surveying the northern γ-ray sky at energies above 10 TeV with unprecedented sensitivity. γ-ray observations have long been one of the most powerful tools for dark matter searches, as, e.g., high-energy γ rays could be produced by the decays of heavy dark matter particles. In this Letter, we present the first dark matter analysis with LHAASO-KM2A, using the first 340 days of data from 1/2-KM2A and 230 days of data from 3/4-KM2A. Several regions of interest are used to search for a signal and account for the residual cosmic-ray background after γ/hadron separation. We find no excess of dark matter signals, and thus place some of the strongest γ-ray constraints on the lifetime of heavy dark matter particles with mass between 10^{5} and 10^{9} GeV. Our results with LHAASO are robust, and have important implications for dark matter interpretations of the diffuse astrophysical high-energy neutrino emission.
Collapse
|
14
|
Yang F, Wei Y, Sun C, Yuan M, Zeng W, Liu C, Fu H. Pinoxaden Degradation Characteristics of Acinetobacter pittobacter and Prediction of Related Genes. Microbiology (Reading) 2022. [DOI: 10.1134/s002626172210109x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
|
15
|
Ikuero FE, Zeng W. Improving cybersecurity incidents reporting in Nigeria: micro and small enterprises perspectives. NIGERIAN JOURNAL OF TECHNOLOGY 2022. [DOI: 10.4314/njt.v41i3.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Leveraging on the provisions of the internet enhances the productivity of Micro and Small Enterprises (MSEs), increases industrial growth and their contributions to national prosperity. Every cyber-attack against their businesses should be reported to the requisite incident response body through the appropriate channels for quick recovery from attack. This research examines how the MSEs in Nigeria report cybersecurity incidents. This study surveyed 100 MSEs. The outcome of the research shows that 72% of the MSEs is unaware of the channel of reporting cyber incidents and does not report cyber incidents. Participants totaling 90% believe that the Sectoral Computer Security Incident Response Team (CSIRT) could improve on reporting of cybersecurity incidents through sensitisation. Amongst others, we recommended the Sectoral CSIRTs were to develop an Incident Report and Response Plan (IRRP) for managing cybersecurity incidents in MSEs.
Collapse
|
16
|
Xiong YT, Xu L, Zeng W, Liu C, Guo JX, Tang W. [Virtual reconstruction and clinical verification of maxillary defect based on deep learning]. ZHONGHUA KOU QIANG YI XUE ZA ZHI = ZHONGHUA KOUQIANG YIXUE ZAZHI = CHINESE JOURNAL OF STOMATOLOGY 2022; 57:1029-1035. [PMID: 36266076 DOI: 10.3760/cma.j.cn112144-20220714-00384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To construct a virtual reconstruction method including midspan maxillary defects and provide clinical reference by training a generative adversarial network (GAN) model. Methods: The CT data of middle-aged Han patients with oral diseases who visited the Department of Radiology, West China Hospital of Stomatology, Sichuan University from June 2015 to June 2022 were collected, where the CT data of 100 healthy maxilla and 15 maxillary defects (5 simple unilateral defects, 5 unilateral defects involving zygomatic bone, 5 midspan defects) were selected. Mimics was used to create spherical phantom and simulate bone defects around the healthy maxillas, including simple unilateral defects, unilateral defects involving zygomatic bone and midspan defects. The original image was set as the correct reference for the reconstruction: artificial defects paired with the correct reference were divided into training set (n=70), validation set (n=20) and test set (n=10), where the first two were used to train the GAN model, and the test set was used to evaluate the GAN performance. Data from 15 clinical defects were imported into the trained GAN model for reconstruction, with mirroring and GAN-based virtual reconstruction for unilateral clinical defects, and only the latter method was adopted for midspan defects. The reconstruction results were divided into mirror reconstruction group (n=10), unilateral defect GAN reconstruction group (n=10) and midspan defect GAN reconstruction group (n=5). The test set, mirror reconstruction group, and unilateral defect GAN reconstruction group were quantitatively evaluated, whose quantitative indicators were Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95), and the group results were subjected to one-way ANOVA and Tukey test. The test set, mirror reconstruction group, unilateral defect GAN reconstruction group and midspan defect GAN reconstruction group were qualitatively scored, and Kruskal-Wallis test and Bonferroni correction were used for the total score of each group. Results: The total differences in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group DCS (0.891±0.049, 0.721±0.047, 0.778±0.057, respectively) and HD95 [(3.58±1.51), (5.19±1.38), (4.51±1.10) mm, respectively] were statistically significant (F=28.08, P<0.001; F=3.62, P=0.041); among them, the test set DSC was significantly larger than the mirror reconstruction group (P<0.05), and the test set HD95 was significantly less than the mirror reconstruction group (P<0.05). Overall difference in qualitative total scores [8 (1), 6 (2), 6 (2), and 4 (2) points, respectively] in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group, and midspan defect GAN reconstruction group were statistical significance (H=18.13, P<0.001); pairwise comparison showed that the total score of the test set was significantly higher than that of the mirror reconstruction group (P<0.05). Conclusions: The virtual reconstruction method based on GAN proposed in this study has better virtual reconstruction effect of unilateral defect than mirror technique, and can also realize virtual reconstruction of maxillary midspan defect.
Collapse
|
17
|
Zeng W, Li W, Liu S, Chen L, Tyler R, Tang H, Luo J, Zhang S. A preclinical toxicology and pharmacology study of OQL051, a gut-restricted CDK4/6 inhibitor for the prophylaxis of chemotherapy-induced diarrhea. Eur J Cancer 2022. [DOI: 10.1016/s0959-8049(22)01007-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
18
|
Aharonian F, An Q, Axikegu, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Z, Cao Z, Chang J, Chang JF, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng HL, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, D’Ettorre Piazzoli B, Dai BZ, Dai HL, Dai ZG, Danzengluobu, della Volpe D, Duan KK, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, 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 SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu Q, Hu S, Hu SC, Hu XJ, Huang DH, Huang WH, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang ZJ, Jin M, Kang MM, 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 J, Li K, Li WL, Li XR, Li X, Li X, 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, Long WJ, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Min Z, Mitthumsiri W, Nan YC, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Shao CY, 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 R, Wang RN, Wang W, Wang XG, 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 XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RX, Xue L, Yan DH, Yan JZ, Yang CW, Yang FF, Yang HW, 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, Yue H, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, 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. Reconstruction of Cherenkov image by multiple telescopes of LHAASO-WFCTA. RADIATION DETECTION TECHNOLOGY AND METHODS 2022. [DOI: 10.1007/s41605-022-00342-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
19
|
Finke A, Illava G, Jayne R, Closs D, Zeng W, Milano S, Huang Q, Kriksunov I, Apker B, Thorne R, Szebenyi M. Serial crystallography made simple: easing the learning curve of multi-crystal diffraction experiments with new fixed-target methods. ACTA CRYSTALLOGRAPHICA SECTION A FOUNDATIONS AND ADVANCES 2022. [DOI: 10.1107/s2053273322093408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
|
20
|
Du X, Wang Z, Chen B, Zeng W. LB893 Macrophage recruitment after dermal pigmentation removal by 1064 nm laser is mediated by Fn14 upregulation of skin fibroblast. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
21
|
Zhao X, Zeng W, Geng S, Wang Z. LB979 Mast cell activation via mas-related g protein-coupled receptor X2 is regulated by ryanodine-sensitive calcium stores. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.1002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
22
|
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
|
23
|
Zuo Z, Li Y, Peng K, Li X, Tan Q, Mo Y, Lan Y, Zeng W, Qi W. CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study. Clin Radiol 2021; 77:e215-e221. [PMID: 34916048 DOI: 10.1016/j.crad.2021.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 11/12/2021] [Indexed: 12/29/2022]
Abstract
AIM To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma. MATERIALS AND METHODS A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated. RESULTS Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867-0.914) and 0.864 (95% CI: 0.817-0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively). CONCLUSIONS A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making.
Collapse
|
24
|
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]
|
25
|
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
|