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Li M, Liao L, Huang W, Feng H, Wang W, Huang N, Zhao Z, Shi Y, Ye J, Gu K. Patients with advanced cancer were treated with immune checkpoint inhibitors and injected with COVID-19 vaccine to improve their prognosis without increasing pancreatic related adverse events. Hum Vaccin Immunother 2024; 20:2358575. [PMID: 38836382 PMCID: PMC11155700 DOI: 10.1080/21645515.2024.2358575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024] Open
Abstract
To investigate immune checkpoint inhibitors (ICIs) induced pancreatic injury (ICIPI), the prognostic effect of COVID-19 vaccine on cancer patients, and whether COVID-19 vaccine increases the incidence of ICIPI. We conducted a retrospective study of 256 stage IV cancer patients treated with ICIs at The First Affiliated Hospital of Anhui Medical University from January 2020 to November 2022. Data collected included pancreatic enzyme levels, treatment outcomes, and vaccination status. Statistical significance was determined using the χ2 test and Kaplan-Meier method (p < .05). Compared to the control group, the vaccinated group (p < .0001) and the group with elevated pancreatic enzyme levels (p = .044) demonstrated higher disease control rates, indicating a direct benefit of vaccination and enzyme monitoring on treatment outcomes. Additionally, vaccinated patients demonstrated longer overall survival versus unvaccinated patients (23.9 months [95% CI, 22.3-25.5] vs 23.6 months [95% CI, 21.1-26.2], HR = 0.45 [95% CI, 0.24-0.86], p = .015) and progression-free survival (17.2 months [95% CI, 14.3-20.1] vs 13.7 months [95% CI, 11.3-16.1], HR = 0.54 [95% CI, 0.36-0.82], p = .004). Importantly, the analysis revealed no significant association between vaccination and pancreatic injury (p = .46). Monitoring pancreatic enzymes can effectively evaluate the therapeutic impact in patients using ICIs. Patients vaccinated against COVID-19 experience better immunotherapy outcomes without an increased risk of ICIPI.
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Li J, Wang W, Jiang K, Cui J, Wang C, Liang T, Wang Y, Liu S, Zhou W. Risk Factors of Chemotherapy-Induced Thrombocytopenia After Oxaliplatin-Containing Chemotherapy for Gastrointestinal Malignancies. J Gastrointest Cancer 2024; 55:1144-1153. [PMID: 38713434 PMCID: PMC11347477 DOI: 10.1007/s12029-024-01059-x] [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] [Accepted: 04/20/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE Thrombocytopenia is among the most common chemotherapy-related hematologic toxicities. We aim to determine the predictors of oxaliplatin chemotherapy-induced thrombocytopenia in patients with gastrointestinal tumors to guide the clinic. METHODS Clinical data of 750 patients with a malignant gastrointestinal tumor were included as the primary cohort. Basic clinical data, serological indices, and anthropometric indices of these patients were collected. According to the presence or absence of CIT, univariate analysis was performed to identify significant factors for multivariate analysis. In R language software, nomogram was constructed based on the results of multi-factor analysis, and the calibration curve and ROC curve were drawn. RESULTS Univariate analysis identified 17 factors as closely related to CIT occurrence, namely age, lymph node metastasis (N) stage, metastasis (M) stage, lung metastasis, other site metastasis, chemotherapy regimen, course of treatment, total dose of oxaliplatin, AST, albumin, neutrophils, monocytes, baseline platelets, transferrin, natural killer (NK) cell, phase angle, and SMI (P < 0.10). The binary logistic multivariate regression analysis revealed five independent risk factors for developing CIT (P < 0.05), including the M stage, total dose of oxaliplatin, albumin, baseline thrombocyte count, and NK cell. Based on the results of multivariate logistic regression analysis, R software was used to establish a nomogram model. The calibration curve shows that the combined predictor has good consistency. The area under the ROC curve was 0.877 and the best cut-off value was 0.3579613 (sensitivity, 78.9%; specificity, 81.8%), which showed the better prediction efficiency. CONCLUSION The total dose of oxaliplatin, M stage, albumin, baseline platelet count, and NK cell was independent risk factors for CIT. The sequentially constructed histogram model had a good predictive effect on the risk of thrombocytopenia caused by oxaliplatin chemotherapy in patients with gastrointestinal malignancies.
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Liu W, Wang W, Guo M, Zhang H. Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas. Clin Radiol 2024; 79:e1117-e1125. [PMID: 38862335 DOI: 10.1016/j.crad.2024.05.010] [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: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
AIM To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas. MATERIALS AND METHODS We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models. RESULTS The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769. CONCLUSION Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.
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Zhao X, Li H, Jin L, Xue J, Yao Y, Pang W, Liu X, Wang W, Han Q, Zhang B, Zhao X, Zhang Q, Wu X, Tan Z, Zhang X, Su X, Zhang C. A risk-prediction score about colorectal lesions based on the Chinese population of high-risk participants aged 50-65 years. Public Health 2024; 234:164-169. [PMID: 39013238 DOI: 10.1016/j.puhe.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 05/26/2024] [Accepted: 06/08/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVES The present study aims to develop an effective risk-prediction score (RPS) to improve screening efficiency and contribute to secondary prevention of colorectal cancer (CRC). STUDY DESIGN Screening for colorectal lesions. METHODS 14,398 high-risk individuals aged 50-65 years were included. The baseline characteristics of participants with and without colorectal lesions (CL) were compared using a Chi-squared test. The overall population was randomly split into a training set and a test set in the ratio of 80% and 20%. One-factor and multifactor logistic regression analyses were performed in the training set to construct the RPS (scores of 0-9.62). Area under curve (AUC) was calculated as an estimate of predictive performance using the receiver-operating characteristic (ROC) curve in the test set. RESULTS In the study population, being male, advanced age, current or previous smoking, weekly alcohol consumption, high body mass index (BMI ≥24 kg/m2), and previously detected colonic polyp were associated with higher risk of CL. Compared to the low-risk group (0-2.31 points), the ORs and 95% confidence intervals (CIs) for the moderate-risk group (2.31-3.85 points) and high-risk group (3.85-8.42 points) were 1.58 (1.44, 1.73) and 2.52 (2.30, 2.76), respectively. For every 1-point increase in score, participants had a 27% increased risk of CL (OR:1.27, 95% CI: 1.24, 1.30). For participants with CL predicted by RPS, the area under the working characteristic curve was 0.61 (P < 0.001). CONCLUSION Our RPS can quickly and efficiently identify multiple lesions of the colorectum. Combining RPS with existing screening strategies facilitates the identification of very high-risk individuals and may help to prevent CRC.
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Qiu Y, Wang W. [Novel advances in assessment and treatment of obstructive sleep apnea]. ZHONGHUA JIE HE HE HU XI ZA ZHI = ZHONGHUA JIEHE HE HUXI ZAZHI = CHINESE JOURNAL OF TUBERCULOSIS AND RESPIRATORY DISEASES 2024; 47:781-784. [PMID: 39069857 DOI: 10.3760/cma.j.cn112147-20240123-00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The current indicators for assessing obstructive sleep apnea are sleep apnea-hypopnea index (AHI) and the lowest nocturnal oxygen saturation, but they do not comprehensively reflect the severity of the disease. The main treatments for OSA are continuous positive airway pressure, mandibular advancement devices and surgery, which have poor compliance and limited effectiveness. Therefore, there is a need to explore novel methods to assess and treat OSA. This article systematically summarized recent advances in this field.
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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. Constraints on Ultraheavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations. PHYSICAL REVIEW LETTERS 2024; 133:061001. [PMID: 39178452 DOI: 10.1103/physrevlett.133.061001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 05/01/2024] [Accepted: 06/12/2024] [Indexed: 08/25/2024]
Abstract
In this Letter we try to search for signals generated by ultraheavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible γ rays by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of the LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter that have low fluxes of astrophysical γ-ray background while having large amount of dark matter. By analyzing more than 700 days of observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultraheavy dark matter annihilation cross section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived.
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Li CY, Wang W, Huang WJ, Xu HH, Yi HL, Guan J, Li G, Yin SK. [Diagnosis of obstructive sleep apnea by a new radar device: a parallel controlled study evaluating agreement with polysomnographic monitoring]. ZHONGHUA ER BI YAN HOU TOU JING WAI KE ZA ZHI = CHINESE JOURNAL OF OTORHINOLARYNGOLOGY HEAD AND NECK SURGERY 2024; 59:857-863. [PMID: 39193596 DOI: 10.3760/cma.j.cn115330-20231204-00267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Objective: This study evaluates the agreement between a new low-load sleep monitoring system, QSA600, based on millimeter-wave radar technology, and polysomnography (PSG) in diagnosing obstructive sleep apnea (OSA). Methods: A total of 155 subjects were recruited for a parallel agreement study in the sleep laboratory of the Department of Otorhinolaryngology Head and Neck Surgery at Shanghai Sixth People's Hospital from July to September 2023. The subjects underwent simultaneous monitoring with both PSG and the QSA600 system. One hundred and forty-five subjects consisting of 75 males and 70 females included in the final analysis, with an average age of (35.30±12.41) years, an average height of (168.23±8.08) cm, and an average weight of (68.28±13.74) kg. The subjects were divided into four groups based on the apnea-hypopnea index (AHI): <5.0 events/h (non-OSA group, 39 cases), ≥5.0-<15.0 events/h (mild OSA group, 47 cases), ≥15.0-<30.0 events/h (moderate OSA group, 25 cases), and≥30.0 events/h (severe OSA group, 34 cases). Intraclass correlation coefficients (ICC), Pearson correlation coefficients (r), and Bland-Altman analysis were employed to assess the agreement between the two monitoring techniques regarding AHI and other parameters. Sensitivity and specificity of the QSA600 in diagnosing OSA were evaluated at different AHI thresholds. Statistical analyses were conducted using MATLAB R2022a. Results: Using AHI 5 events/h, 15 events/h and 30 events/h as thresholds, the sensitivity for diagnosing mild, moderate, and severe OSA was 88.68%, 89.83% and 97.06%, respectively. The specificity was 94.87%, 98.84% and 99.10%, respectively. The areas under the receiver operating characteristic (ROC) curve was 0.973 4, 0.990 9 and 0.999 5, respectively. The comparison of key indicators between QSA600 and PSG diagnostic results revealed:a Pearson correlation coefficient of 0.987 2(P<0.001) between the AHI measurement values. The mean difference between the Bland-Altman measurement values of the two was -1.43(95%CI:-8.74-5.88) events/h and the ICC between the two was 0.985 0(95%CI: 0.975 4-0.990 4). Conclusions: As a new low-load sleep monitoring system, QSA600 demonstrates high concordance with traditional PSG in diagnosing OSA and stratifying its severity, which has promising potential for clinical application. (Clinical trial registration number: NCT06038006).
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Li D, Chen S, Chen B, Li B, Lin C, Wang W. Gastrointestinal: A rare etiology of pneumoperitoneum and epigastric pain. J Gastroenterol Hepatol 2024. [PMID: 39104189 DOI: 10.1111/jgh.16684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/18/2024] [Accepted: 07/13/2024] [Indexed: 08/07/2024]
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An FP, Bai WD, Balantekin AB, Bishai M, Blyth S, Cao GF, Cao J, Chang JF, Chang Y, Chen HS, Chen HY, Chen SM, Chen Y, Chen YX, Chen ZY, Cheng J, Cheng YC, Cheng ZK, Cherwinka JJ, Chu MC, Cummings JP, Dalager O, Deng FS, Ding XY, Ding YY, Diwan MV, Dohnal T, Dolzhikov D, Dove J, Dugas KV, Duyang HY, Dwyer DA, Gallo JP, Gonchar M, Gong GH, Gong H, Gu WQ, Guo JY, Guo L, Guo XH, Guo YH, Guo Z, Hackenburg RW, Han Y, Hans S, He M, Heeger KM, Heng YK, Hor YK, Hsiung YB, Hu BZ, Hu JR, Hu T, Hu ZJ, Huang HX, Huang JH, Huang XT, Huang YB, Huber P, Jaffe DE, Jen KL, Ji XL, Ji XP, Johnson RA, Jones D, Kang L, Kettell SH, Kohn S, Kramer M, Langford TJ, Lee J, Lee JHC, Lei RT, Leitner R, Leung JKC, Li F, Li HL, Li JJ, Li QJ, Li RH, Li S, Li S, Li SC, Li WD, Li XN, Li XQ, Li YF, Li ZB, Liang H, Lin CJ, Lin GL, Lin S, Ling JJ, Link JM, Littenberg L, Littlejohn BR, Liu JC, Liu JL, Liu JX, Lu C, Lu HQ, Luk KB, Ma BZ, Ma XB, Ma XY, Ma YQ, Mandujano RC, Marshall C, McDonald KT, McKeown RD, Meng Y, Napolitano J, Naumov D, Naumova E, Nguyen TMT, Ochoa-Ricoux JP, Olshevskiy A, Park J, Patton S, Peng JC, Pun CSJ, Qi FZ, Qi M, Qian X, Raper N, Ren J, Morales Reveco C, Rosero R, Roskovec B, Ruan XC, Russell B, Steiner H, Sun JL, Tmej T, Tse WH, Tull CE, Tung YC, Viren B, Vorobel V, Wang CH, Wang J, Wang M, Wang NY, Wang RG, Wang W, Wang X, Wang YF, Wang Z, Wang Z, Wang ZM, Wei HY, Wei LH, Wei W, Wen LJ, Whisnant K, White CG, Wong HLH, Worcester E, Wu DR, Wu Q, Wu WJ, Xia DM, Xie ZQ, Xing ZZ, Xu HK, Xu JL, Xu T, Xue T, Yang CG, Yang L, Yang YZ, Yao HF, Ye M, Yeh M, Young BL, Yu HZ, Yu ZY, Yuan CZ, Yue BB, Zavadskyi V, Zeng S, Zeng Y, Zhan L, Zhang C, Zhang FY, Zhang HH, Zhang JL, Zhang JW, Zhang QM, Zhang SQ, Zhang XT, Zhang YM, Zhang YX, Zhang YY, Zhang ZJ, Zhang ZP, Zhang ZY, Zhao J, Zhao RZ, Zhou L, Zhuang HL, Zou JH. Search for a Sub-eV Sterile Neutrino Using Daya Bay's Full Dataset. PHYSICAL REVIEW LETTERS 2024; 133:051801. [PMID: 39159085 DOI: 10.1103/physrevlett.133.051801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/21/2024] [Indexed: 08/21/2024]
Abstract
This Letter presents results of a search for the mixing of a sub-eV sterile neutrino with three active neutrinos based on the full data sample of the Daya Bay Reactor Neutrino Experiment, collected during 3158 days of detector operation, which contains 5.55×10^{6} reactor ν[over ¯]_{e} candidates identified as inverse beta-decay interactions followed by neutron capture on gadolinium. The analysis benefits from a doubling of the statistics of our previous result and from improvements of several important systematic uncertainties. No significant oscillation due to mixing of a sub-eV sterile neutrino with active neutrinos was found. Exclusion limits are set by both Feldman-Cousins and CLs methods. Light sterile neutrino mixing with sin^{2}2θ_{14}≳0.01 can be excluded at 95% confidence level in the region of 0.01 eV^{2}≲|Δm_{41}^{2}|≲0.1 eV^{2}. This result represents the world-leading constraints in the region of 2×10^{-4} eV^{2}≲|Δm_{41}^{2}|≲0.2 eV^{2}.
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Wang X, Zheng R, Liang W, Qiu H, Yuan T, Wang W, Deng H, Kong W, Chen J, Bai Y, Li Y, Chen Y, Wu Q, Wu S, Huang X, Shi Z, Fu Q, Zhang Y, Yang Q. Small extracellular vesicles facilitate epithelial-mesenchymal transition in chronic rhinosinusitis with nasal polyps via the miR-375-3p/QKI axis. Rhinology 2024; 62:466-479. [PMID: 38557580 DOI: 10.4193/rhin23.520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, the involvement of small extracellular vesicles (sEVs) in EMT and their contributions to CRSwNP has not been extensively investigated. METHODS SEVs were isolated from nasal mucosa through ultracentrifugation. MicroRNA sequencing and reverse-transcription quantitative polymerase chain reaction were employed to analyze the differential expression of microRNAs carried by sEVs. Human nasal epithelial cells (hNECs) were used to assess the EMT-inducing effect of sEVs/microRNAs. EMT-associated markers were detected by western blotting and immunofluorescence. Dual-luciferase reporter assay was performed to determine the target gene of miR-375-3p. MicroRNA mimic, lentiviral, and plasmid transduction were used for functional experiments. RESULTS In line with the greater EMT status in eosinophilic CRSwNP (ENP), sEVs derived from ENP (ENP-sEVs) could induce EMT in hNECs. MiR-375-3p was elevated in ENP-sEVs compared to that in control and nonENP. MiR-375-3p carried by ENP-sEVs facilitated EMT by directly targeting KH domain containing RNA binding (QKI) at seed sequences of 913-919, 1025-1033, and 2438-2444 in 3’-untranslated region. Inhibition of QKI by miR-375-3p overexpression promoted EMT, which could be reversed by restoration of QKI. Furthermore, the abundance of miR-375-3p in sEVs was closely correlated with the clinical symptom score and disease severity. CONCLUSIONS MiR-375-3p-enriched sEVs facilitated EMT by suppressing QKI in hNECs. The association of miR-375-3p with disease severity underscores its potential as both a diagnostic marker and a therapeutic target for the innovative management of CRSwNP.
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Tang M, Zhen X, Zhao G, Wu S, Hua W, Qiang J, Yanling C, Wang W. The metabolic pathways of carbon assimilation and polyhydroxyalkanoate production by Rhodospirillum rubrum in response to different atmospheric fermentation. PLoS One 2024; 19:e0306222. [PMID: 39046963 PMCID: PMC11268599 DOI: 10.1371/journal.pone.0306222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/12/2024] [Indexed: 07/27/2024] Open
Abstract
The purple nonsulfur bacteria, Rhodospirillum rubrum, is recognized as a potential strain for PHAs bioindustrial processes since they can assimilate a broad range of carbon sources, such as syngas, to allow reduction of the production costs. In this study, we comparatively analyzed the biomass and PHA formation behaviors of R. rubrum under 100% CO and 50% CO gas atmosphere and found that pure CO promoted the PHA synthesis (PHA content up to 23.3% of the CDW). Hydrogen addition facilitated the uptake and utilization rates of CO and elevated 3-HV monomers content (molar proportion of 3-HV up to 9.2% in the presence of 50% H2). To elucidate the genetic events culminating in the CO assimilation process, we performed whole transcriptome analysis of R. rubrum grown under 100% CO or 50% CO using RNA sequencing. Transcriptomic analysis indicated different CO2 assimilation strategy was triggered by the presence of H2, where the CBB played a minor role. An increase in BCAA biosynthesis related gene abundance was observed under 50% CO condition. Furthermore, we detected the α-ketoglutarate (αKG) synthase, converting fumarate to αKG linked to the αKG-derived amino acids synthesis, and series of threonine-dependent isoleucine synthesis enzymes were significantly induced. Collectively, our results suggested that those amino acid synthesis pathways represented a key way for carbon assimilation and redox potential maintenance by R. rubrum growth under syngas condition, which could partly replace the PHA production and affect its monomer composition in copolymers. Finally, a fed-batch fermentation of the R. rubrum in a 3-l bioreactor was carried out and proved H2 addition indeed increased the PHA accumulation rate, yielding 20% ww-1 PHA production within six days.
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Li MM, Yang Y, Long DY, Jiang CX, Tang RB, Sang CH, Wang W, Zhao X, Guo XY, Li SN, Li CY, Ning M, Jia CQ, Feng L, Wen D, Zhu H, Jiang YX, Liu F, Liu T, Dong JZ, Ma CS. [Early experience with mechanical hemodynamic support for catheter ablation of malignant ventricular tachycardia]. ZHONGHUA XIN XUE GUAN BING ZA ZHI 2024; 52:768-776. [PMID: 39019825 DOI: 10.3760/cma.j.cn112148-20240410-00194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
Objective: To explore the role of mechanical hemodynamic support (MHS) in mapping and catheter ablation of patients with hemodynamically unstable ventricular tachycardia (VT), report single-center experience in a cohort of consecutive patients receiving VT ablation during MHS therapy, and provide evidence-based medical evidence for clinical practice. Methods: This was a retrospective cohort study. Patients with hemodynamically unstable VT who underwent catheter ablation with MHS at Beijing Anzhen Hospital, Capital Medical University between August 2021 and December 2023 were included. Patients were divided into rescue group and preventive group according to the purpose of treatment. Their demographic data, periprocedural details, and clinical outcomes were collected and analyzed. Results: A total of 15 patients with hemodynamically unstable VT were included (8 patients in the rescue group and 7 patients in the preventive group). The acute procedure was successful in all patients. One patient in the rescue group had surgical left ventricular assist device (LVAD) implantation, remaining 14 patients received extracorporeal membrane oxygenation (ECMO) for circulation support. ECMO decannulation was performed in 12 patients due to clinical and hemodynamic stability, of which 6 patients were decannulation immediately after surgery and the remaining patients were decannulation at 2.0 (2.5) d after surgery. Two patients in the rescue group died during the index admission due to refractory heart failure and cerebral hemorrhage. During a median follow-up of 30 d (1 d to 12 months), one patient with LVAD had one episode of ventricular fibrillation at 6 months after discharge, and no further episodes of ventricular fibrillation and/or VT occurred after treatment with antiarrhythmic drugs. No malignant ventricular arrhythmia occurred in the remaining 12 patients who were followed up. Conclusions: MHS contributes to the successful completion of mapping and catheter ablation in patients with hemodynamically unstable VT, providing desirable hemodynamic status for emergency and elective conditions.
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Wang N, Wang W, Liu XY, Luo Y, Sun BR, Yan ST, Fang FS. [Correlation analysis between serum free triiodothyronine and C-peptide-based insulin resistance index in euthyroid adults]. ZHONGHUA YI XUE ZA ZHI 2024; 104:2626-2631. [PMID: 39019819 DOI: 10.3760/cma.j.cn112137-20231205-01298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
Objective: To explore the correlation between serum free triiodothyronine (FT3) and C-peptide-based insulin resistance index (HOMA2 IR-CP) in euthyroid adults. Methods: A cross-sectional study. The clinical data of euthyroid adult participants who underwent physical examination in the Second Medical Center of Chinese PLA General Hospital from January to December in 2019 were retrospectively analyzed. According to the HOMA2 IR-CP level, the participants were divided into HOMA2 IR-CP>2.18 group (n=3 463) and HOMA2 IR-CP≤2.18 group (n=8 204). Univariate Pearson correlation analysis and multivariate logistic regression analysis were used to analyze the correlation between FT3 and HOMA2 IR-CP. The interaction model was used to analyze the interaction between FT3 and related factors, and the dose-response relationship between continuity variable FT3 and HOMA2 IR-CP was explored by using restricted cubic spline plots. Results: A total of 11 667 euthyroid adult participants aged (50.7±10.0)years were recruited according to the inclusion and exclusion criteria, with 7 756 males and 3 911 females. The proportion of males, body mass index, systolic blood pressure, glycated hemoglobin A1c, fasting plasma glucose, triglyceride, hemoglobin, alanine aminotransferase and FT3 levels in HOMA2 IR-CP>2.18 group were significantly higher than those in HOMA2 IR-CP≤2.18 group (all P<0.05). The levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and free thyroxine in HOMA2 IR-CP>2.18 group were lower than those in HOMA2 IR-CP≤2.18 group (all P<0.001). Univariate Pearson correlation analysis showed that FT3 was associated with HOMA2 IR-CP (r=0.21, P<0.001). Multivariate logistic regression analysis suggested an association between FT3 and HOMA2 IR-CP after adjusting for confounding factors(Pfor trend<0.001). Subgroup analysis showed an association between FT3 and HOMA2 IR-CP in different subgroups of gender, age and glucose metabolism status (Pfor trend<0.05). Multiplication interaction analysis suggested that there was an interaction between FT3 and age (Pinteraction<0.001). Restricted cubic spline model analysis demonstrated that the correlation between FT3 and HOMA2 IR-CP was linear (Poverall<0.001, Pnonlinear=0.479). Conclusions: There is a correlation between serum FT3 and HOMA2 IR-CP in euthyroid adults. With the increase of FT3 level, insulin resistance increases gradually.
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Shi B, Yang CY, Zhao YY, Yan Q, Liu L, Wang W, Bai Y, Fan SF, Gu XH. [A multicenter prospective clinical trial on the effect of domestic bone level implants on single tooth implantation and restoration]. ZHONGHUA KOU QIANG YI XUE ZA ZHI = ZHONGHUA KOUQIANG YIXUE ZAZHI = CHINESE JOURNAL OF STOMATOLOGY 2024; 59:811-816. [PMID: 39036913 DOI: 10.3760/cma.j.cn112144-20240205-00064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Objective: To evaluate the clinical application effects of a domestic bone-level implant system for restoring single tooth loss, and provide clinical evidence for the promotion and application of domestic implants. Methods: A prospective, multicenter clinical trial was conducted from April 2018 to January 2020 in three institutions: Department of Oral Implantology, School & Hospital of Stomatology, Wuhan University, Department of Stomatology, The First Affiliated Hospital of Zhejiang University School of Medicine, and Department of Stomatology, The Third Hospital of Hebei Medical University. The trial planned to include 100 patients for single tooth implantation and restoration, followed up for 1 year, to evaluate the implantation success rate and other related outcomes. Results: This study screened a total of 142 patients and ultimately included 100, comprising 43 males and 57 females with age of (47.0±12.2) years. Ninety-eight out of 100 patients completed a one-year follow-up (98.0%), while 2 patients terminated the trial early due to implant loosening (2.0%). After a one-year follow-up, the implants of the 98 patients were all functioning successfully, with a success rate of 98.0% (98/100). The patients were satisfied with the overall restoration effect. Conclusions: This study indicates that the domestic bone-level implant system has achieved favorable short-term clinical outcomes for single-tooth implantation and restoration.
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Chen Y, Qi Y, Pu R, Lin H, Wang W, Sun B. CT histogram analysis to distinguish between acute intracerebral hemorrhage and cavernous hemangioma. Clin Radiol 2024:S0009-9260(24)00397-0. [PMID: 39129104 DOI: 10.1016/j.crad.2024.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/21/2024] [Accepted: 07/16/2024] [Indexed: 08/13/2024]
Abstract
OBJECTIVE Acute intracerebral hemorrhage (AICH) and cerebral cavernous hemangioma (CCM) are two common cerebral hemorrhage diseases with partially overlapping CT findings and clinical symptoms, making it hard to distinguish between them. The current study used histogram analysis based on CT images to differentiate between CCM and AICH and test its diagnosis performance. METHODS This retrospective study included 158 patients with CCM and 137 patients with AICH. The histograms of brain CT plain scan images of both groups were extracted using Python code and included 18 histogram parameters of the lesions. The most effective parameters were selected by univariate logistic regression analysis and Spearman correlation analysis and included in the final multivariate logistic regression model. The sample was randomly divided into the training set and the validation set by 7:3. The ROC curve was constructed to evaluate the discriminant efficiency of the final logistic regression model in distinguishing between AICH and CCM. RESULTS The univariate analysis identified seven significant histogram parameters with the following final logistic regression model: F = 3.731 + 2.6411 × 10-9 × Energy-1.192 × Kurtosis-0.003 × Minimum-1.449 × Skewness + 2.5002 × 10-10 × Total Energy-1.103 × Uniformity+0.009 × Variance. The model showed good diagnostic performance in distinguishing between AICH and CCM, with an AUC of 0.876, sensitivity of 70.8%, and specificity of 91.9% in the training set, and an AUC of 0.870, sensitivity of 82.9%, and specificity of 85.1% in the validation set. CONCLUSIONS The histogram analysis of brain CT images can be used as an auxiliary method to distinguish between AICH and CCM effectively.
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Anders JK, Andrean SY, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Anthony MT, Antipov E, Antonelli M, Anulli F, Aoki M, Aoki T, Aparisi Pozo JA, Aparo MA, Aperio Bella L, Appelt C, Apyan A, Aranzabal N, Arbiol Val SJ, Arcangeletti C, Arce ATH, Arena E, Arguin JF, Argyropoulos S, Arling JH, Arnaez O, Arnold H, Artoni G, Asada H, Asai K, Asai S, Asbah NA, Assamagan K, Astalos R, Atashi S, Atkin RJ, Atkinson M, Atmani H, Atmasiddha PA, Augsten K, Auricchio S, Auriol AD, Austrup VA, Avolio G, Axiotis K, Azuelos G, Babal D, Bachacou H, Bachas K, Bachiu A, Backman F, Badea A, Baer TM, Bagnaia P, Bahmani M, Bahner D, Bailey AJ, Bailey VR, Baines JT, Baines L, Baker OK, Bakos E, Bakshi Gupta D, Balakrishnan V, Balasubramanian R, Baldin EM, Balek P, Ballabene E, Balli F, Baltes LM, Balunas WK, Balz J, Banas E, Bandieramonte M, Bandyopadhyay A, Bansal S, Barak L, Barakat M, Barberio EL, Barberis D, Barbero M, Barel MZ, Barends KN, Barillari T, Barisits MS, Barklow T, Baron P, Baron Moreno DA, Baroncelli A, Barone G, Barr AJ, Barr JD, Barranco Navarro L, Barreiro F, Barreiro Guimarães da Costa J, Barron U, Barros Teixeira MG, Barsov S, Bartels F, Bartoldus R, Barton AE, Bartos P, Basan A, Baselga M, Bassalat A, Basso MJ, Basson CR, Bates RL, Batlamous S, Batley JR, Batool B, Battaglia M, Battulga D, Bauce M, Bauer M, Bauer P, Bazzano Hurrell LT, Beacham JB, Beau T, Beaucamp JY, Beauchemin PH, Becherer F, Bechtle P, Beck HP, Becker K, Beddall AJ, Bednyakov VA, Bee CP, Beemster LJ, Beermann TA, Begalli M, Begel M, Behera A, Behr JK, Beirer JF, Beisiegel F, Belfkir M, Bella G, Bellagamba L, Bellerive A, Bellos P, Beloborodov K, Benchekroun D, Bendebba F, Benhammou Y, Benoit M, Bensinger JR, Bentvelsen S, Beresford L, Beretta M, Bergeaas Kuutmann E, Berger N, Bergmann B, Beringer J, Bernardi G, Bernius C, Bernlochner FU, Bernon F, Berrocal Guardia A, Berry T, Berta P, Berthold A, Bertram IA, Bethke S, Betti A, Bevan AJ, Bhalla NK, Bhamjee M, Bhatta 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Buat Q, Buchin D, Buckley AG, Bulekov O, Bullard BA, Burdin S, Burgard CD, Burger AM, Burghgrave B, Burlayenko O, Burr JTP, Burton CD, Burzynski JC, Busch EL, Büscher V, Bussey PJ, Butler JM, Buttar CM, Butterworth JM, Buttinger W, Buxo Vazquez CJ, Buzykaev AR, Cabrera Urbán S, Cadamuro L, Caforio D, Cai H, Cai Y, Cai Y, Cairo VMM, Cakir O, Calace N, Calafiura P, Calderini G, Calfayan P, Callea G, Caloba LP, Calvet D, Calvet S, Calvet TP, Calvetti M, Camacho Toro R, Camarda S, Camarero Munoz D, Camarri P, Camerlingo MT, Cameron D, Camincher C, Campanelli M, Camplani A, Canale V, Canesse A, Cantero J, Cao Y, Capocasa F, Capua M, Carbone A, Cardarelli R, Cardenas JCJ, Cardillo F, Carducci G, Carli T, Carlino G, Carlotto JI, Carlson BT, Carlson EM, Carminati L, Carnelli A, Carnesale M, Caron S, Carquin E, Carrá S, Carratta G, Carrio Argos F, Carter JWS, Carter TM, Casado MP, Caspar M, Castillo FL, Castillo Garcia L, Castillo Gimenez V, Castro NF, Catinaccio A, Catmore JR, Cavaliere V, Cavalli N, Cavasinni V, Cekmecelioglu YC, Celebi E, Celli F, Centonze MS, Cepaitis V, Cerny K, Cerqueira AS, Cerri A, Cerrito L, Cerutti F, Cervato B, Cervelli A, Cesarini G, Cetin SA, Chakraborty D, Chan J, Chan WY, Chapman JD, Chapon E, Chargeishvili B, Charlton DG, Chatterjee M, Chauhan C, Chekanov S, Chekulaev SV, Chelkov GA, Chen A, Chen B, Chen B, Chen H, Chen H, Chen J, Chen J, Chen M, Chen S, Chen SJ, Chen X, Chen X, Chen Y, Cheng CL, Cheng HC, Cheong S, Cheplakov A, Cheremushkina E, Cherepanova E, Cherkaoui El Moursli R, Cheu E, Cheung K, Chevalier L, Chiarella V, Chiarelli G, Chiedde N, Chiodini G, Chisholm AS, Chitan A, Chitishvili M, Chizhov MV, Choi K, Chomont AR, Chou Y, Chow EYS, Chowdhury T, Chu KL, Chu MC, Chu X, Chudoba J, Chwastowski JJ, Cieri D, Ciesla KM, Cindro V, Ciocio A, Cirotto F, Citron ZH, Citterio M, Ciubotaru DA, Clark A, Clark PJ, Clarry C, Clavijo Columbie JM, Clawson SE, Clement C, Clercx J, Coadou Y, Cobal M, Coccaro A, Barrue RFC, Coelho Lopes De Sa R, Coelli S, Coimbra AEC, Cole B, Collot J, Conde Muiño P, Connell MP, Connell SH, Connelly IA, Conroy EI, Conventi F, Cooke HG, Cooper-Sarkar AM, Cordeiro Oudot Choi A, Corpe LD, Corradi M, Corriveau F, Cortes-Gonzalez A, Costa MJ, Costanza F, Costanzo D, Cote BM, Cowan G, Cranmer K, Cremonini D, Crépé-Renaudin S, Crescioli F, Cristinziani M, Cristoforetti M, Croft V, Crosby JE, Crosetti G, Cueto A, Cuhadar Donszelmann T, Cui H, Cui Z, Cunningham WR, Curcio F, Czodrowski P, Czurylo MM, De Sousa MJDCS, Da Fonseca Pinto JV, Da Via C, Dabrowski W, Dado T, Dahbi S, Dai T, Dal Santo D, Dallapiccola C, Dam M, D'amen G, D'Amico V, Damp J, Dandoy JR, Daneri MF, Danninger M, Dao V, Darbo G, Darmora S, Das SJ, D'Auria S, David C, Davidek T, Davis-Purcell B, Dawson I, Day-Hall HA, De K, De Asmundis R, De Biase N, De Castro S, De Groot N, de Jong P, De la Torre H, De Maria A, De Salvo A, De Sanctis U, De Santis F, De Santo A, De Vivie De Regie JB, Dedovich DV, Degens J, Deiana AM, Del Corso F, Del Peso J, Del Rio F, Delagrange L, Deliot F, Delitzsch CM, Della Pietra M, Della Volpe D, Dell'Acqua A, Dell'Asta L, Delmastro M, Delsart PA, Demers S, Demichev M, Denisov SP, D'Eramo L, Derendarz D, Derue F, Dervan P, Desch K, Deutsch C, Di Bello FA, Di Ciaccio A, Di Ciaccio L, Di Domenico A, Di Donato C, Di Girolamo A, Di Gregorio G, Di Luca A, Di Micco B, Di Nardo R, Diaconu C, Diamantopoulou M, Dias FA, Vale TDD, Diaz MA, Diaz Capriles FG, Didenko M, Diehl EB, Diehl L, Díez Cornell S, Diez Pardos C, Dimitriadi C, Dimitrievska A, Dingfelder J, Dinu IM, Dittmeier SJ, Dittus F, Djama F, Djobava T, Djuvsland JI, Doglioni C, Dohnalova A, Dolejsi J, Dolezal Z, Dona KM, Donadelli M, Dong B, Donini J, D'Onofrio A, D'Onofrio M, Dopke J, Doria A, Dos Santos Fernandes N, Dougan P, Dova MT, Doyle AT, Draguet MA, Dreyer E, Drivas-Koulouris I, Drnevich M, Drobac AS, Drozdova M, Du D, du Pree TA, Dubinin F, Dubovsky M, Duchovni E, Duckeck G, Ducu OA, Duda D, Dudarev A, Duden ER, D'uffizi M, 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Fenyuk AB, Ferencz L, Ferguson RAM, Fernandez Luengo SI, Fernandez Martinez P, Fernoux MJV, Ferrando J, Ferrari A, Ferrari P, Ferrari R, Ferrere D, Ferretti C, Fiedler F, Fiedler P, Filipčič A, Filmer EK, Filthaut F, Fiolhais MCN, Fiorini L, Fisher WC, Fitschen T, Fitzhugh PM, Fleck I, Fleischmann P, Flick T, Flores M, Flores Castillo LR, Flores Sanz De Acedo L, Follega FM, Fomin N, Foo JH, Forland BC, Formica A, Forti AC, Fortin E, Fortman AW, Foti MG, Fountas L, Fournier D, Fox H, Francavilla P, Francescato S, Franchellucci S, Franchini M, Franchino S, Francis D, Franco L, Franco Lima V, Franconi L, Franklin M, Frattari G, Freegard AC, Freund WS, Frid YY, Friend J, Fritzsche N, Froch A, Froidevaux D, Frost JA, Fu Y, Fuenzalida Garrido S, Fujimoto M, Fung KY, De Simas Filho EF, Furukawa M, Fuster J, Gabrielli A, Gabrielli A, Gadow P, Gagliardi G, Gagnon LG, Gallas EJ, Gallop BJ, Gan KK, Ganguly S, Gao Y, Garay Walls FM, Garcia B, García C, Garcia Alonso A, Garcia Caffaro AG, García 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PV, Tsigaridas S, Tsirigotis A, Tsiskaridze V, Tskhadadze EG, Tsopoulou M, Tsujikawa Y, Tsukerman II, Tsulaia V, Tsuno S, Tsuri K, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna AN, Turchikhin S, Turk Cakir I, Turra R, Turtuvshin T, Tuts PM, Tzamarias S, Tzanis P, Tzovara E, Ukegawa F, Ulloa Poblete PA, Umaka EN, Unal G, Unal M, Undrus A, Unel G, Urban J, Urquijo P, Urrejola P, Usai G, Ushioda R, Usman M, Uysal Z, Vacek V, Vachon B, Vadla KOH, Vafeiadis T, Vaitkus A, Valderanis C, Valdes Santurio E, Valente M, Valentinetti S, Valero A, Valiente Moreno E, Vallier A, Valls Ferrer JA, Van Arneman DR, Van Daalen TR, Van Der Graaf A, Van Gemmeren P, Van Rijnbach M, Van Stroud S, Van Vulpen I, Vanadia M, Vandelli W, Vandenbroucke M, Vandewall ER, Vannicola D, Vannoli L, Vari R, Varnes EW, Varni C, Varol T, Varouchas D, Varriale L, Varvell KE, Vasile ME, Vaslin L, Vasquez GA, Vasyukov A, Vazeille F, Vazquez Schroeder T, Veatch J, Vecchio V, Veen MJ, Veliscek I, Veloce LM, Veloso F, Veneziano S, Ventura A, Ventura Gonzalez S, Verbytskyi A, Verducci M, Vergis C, Verissimo De Araujo M, Verkerke W, Vermeulen JC, Vernieri C, Vessella M, Vetterli MC, Vgenopoulos A, Viaux Maira N, Vickey T, Vickey Boeriu OE, Viehhauser GHA, Vigani L, Villa M, Villaplana Perez M, Villhauer EM, Vilucchi E, Vincter MG, Virdee GS, Vishwakarma A, Visibile A, Vittori C, Vivarelli I, Voevodina E, Vogel F, Voigt JC, Vokac P, Volkotrub Y, Von Ahnen J, Von Toerne E, Vormwald B, Vorobel V, Vorobev K, Vos M, Voss K, Vossebeld JH, Vozak M, Vozdecky L, Vranjes N, Vranjes Milosavljevic M, Vreeswijk M, Vuillermet R, Vujinovic O, Vukotic I, Wada S, Wagner C, Wagner JM, Wagner W, Wahdan S, Wahlberg H, Wakida M, Walder J, Walker R, Walkowiak W, Wall A, Wamorkar T, Wang AZ, Wang C, Wang C, Wang H, Wang J, Wang RJ, Wang R, Wang R, Wang SM, Wang S, Wang T, Wang WT, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Warburton A, Ward RJ, Warrack N, Watson AT, Watson H, Watson MF, Watton E, Watts G, Waugh BM, Weber C, Weber HA, Weber MS, Weber SM, Wei C, Wei Y, Weidberg AR, Weik EJ, Weingarten J, Weirich M, Weiser C, Wells CJ, Wenaus T, Wendland B, Wengler T, Wenke NS, Wermes N, Wessels M, Wharton AM, White AS, White A, White MJ, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wollrath J, Wolter MW, Wolters H, Wongel AF, Woodward EL, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu SL, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt TR, Wynne BM, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, Xiong A, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi Y, Yamashita E, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang HJ, Yang HT, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita K, Younas S, Young CJS, Young C, Yu C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zak ZK, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Statistical Combination of ATLAS Run 2 Searches for Charginos and Neutralinos at the LHC. PHYSICAL REVIEW LETTERS 2024; 133:031802. [PMID: 39094132 DOI: 10.1103/physrevlett.133.031802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/07/2024] [Indexed: 08/04/2024]
Abstract
Statistical combinations of searches for charginos and neutralinos using various decay channels are performed using 139 fb^{-1} of pp collision data at sqrt[s]=13 TeV with the ATLAS detector at the Large Hadron Collider. Searches targeting pure-wino chargino pair production, pure-wino chargino-neutralino production, or Higgsino production decaying via standard model W, Z, or h bosons are combined to extend the mass reach to the produced supersymmetric particles by 30-100 GeV. The depth of the sensitivity of the original searches is also improved by the combinations, lowering the 95% C.L. cross-section upper limits by 15%-40%.
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Cao Z, Aharonian F, Axikegu, Bai Y, Bao Y, Bastieri D, Bi X, Bi Y, Bian W, Bukevich A, Cao Q, Cao W, Cao Z, Chang J, Chang J, Chen A, Chen E, Chen H, Chen L, Chen L, Chen L, Chen M, Chen M, Chen Q, Chen S, Chen S, Chen S, Chen T, Chen Y, Cheng N, Cheng Y, Cui M, Cui S, Cui X, Cui Y, Dai B, Dai H, Dai Z, Danzengluobu, Dong X, Duan K, Fan J, Fan Y, Fang J, Fang J, Fang K, Feng C, Feng H, Feng L, Feng S, Feng X, Feng Y, Feng Y, Gabici S, Gao B, Gao C, Gao Q, Gao W, Gao W, Ge M, Geng L, Giacinti G, Gong G, Gou Q, Gu M, Guo F, Guo X, Guo Y, Guo Y, Han Y, Hasan M, He H, He H, He J, He Y, Hor Y, Hou B, Hou C, Hou X, Hu H, Hu Q, Hu S, Huang D, Huang T, Huang W, Huang X, Huang X, Huang Y, Ji X, Jia H, Jia K, Jiang K, Jiang X, Jiang Z, Jin M, Kang M, Karpikov I, Kuleshov D, Kurinov K, Li B, Li C, Li C, Li C, Li D, Li F, Li H, Li H, Li J, Li J, Li K, Li S, Li W, Li W, Li X, Li X, Li Y, Li Z, Li Z, Liang E, Liang Y, Lin S, Liu B, Liu C, Liu D, Liu D, Liu H, Liu H, Liu J, Liu J, Liu M, Liu R, Liu S, Liu W, Liu Y, Liu Y, Luo Q, Luo Y, Lv H, Ma B, Ma L, Ma X, Mao J, Min Z, Mitthumsiri W, Mu H, Nan Y, Neronov A, Ou L, Pattarakijwanich P, Pei Z, Qi J, Qi M, Qiao B, Qin J, Raza A, Ruffolo D, Sáiz A, Saeed M, Semikoz D, Shao L, Shchegolev O, Sheng X, Shu F, Song H, Stenkin Y, Stepanov V, Su Y, Sun D, Sun Q, Sun X, Sun Z, Takata J, Tam P, Tang Q, Tang R, Tang Z, Tian W, Wang C, Wang C, Wang G, Wang H, Wang H, Wang J, Wang K, Wang K, Wang L, Wang L, Wang P, Wang R, Wang W, Wang X, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Wang Z, Wei D, Wei J, Wei Y, Wen T, Wu C, Wu H, Wu Q, Wu S, Wu X, Wu Y, Xi S, Xia J, Xiang G, Xiao D, Xiao G, Xin Y, Xing Y, Xiong D, Xiong Z, Xu D, Xu R, Xu R, Xu W, Xue L, Yan D, Yan J, Yan T, Yang C, Yang C, Yang F, Yang F, Yang L, Yang M, Yang R, Yang W, Yao Y, Yao Z, Yin L, Yin N, You X, You Z, Yu Y, Yuan Q, Yue H, Zeng H, Zeng T, Zeng W, Zha M, Zhang B, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang L, Zhang P, Zhang P, Zhang R, Zhang S, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao L, Zhao S, Zhao X, Zheng F, Zhong W, Zhou B, Zhou H, Zhou J, Zhou M, Zhou P, Zhou R, Zhou X, Zhou X, Zhu B, Zhu C, Zhu F, Zhu H, Zhu K, Zou Y, Zuo X, Celli S. Evidence for particle acceleration approaching PeV energies in the W51 complex. Sci Bull (Beijing) 2024:S2095-9273(24)00496-1. [PMID: 39153903 DOI: 10.1016/j.scib.2024.07.017] [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: 04/23/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 08/19/2024]
Abstract
The γ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays (CRs) accelerated by the shock of supernova remnant (SNR) W51C and the dense molecular clouds in the adjacent star-forming region, W51B. However, the maximum acceleration capability of W51C for CRs remains elusive. Based on observations conducted with the Large High Altitude Air Shower Observatory (LHAASO), we report a significant detection of γ rays emanating from the W51 complex, with energies from 2 to 200 TeV. The LHAASO measurements, for the first time, extend the γ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of TeV. By combining the "π0-decay bump" featured data from Fermi-LAT, the broadband γ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision model. The observed spectral bending feature suggests an exponential cutoff at ∼400 TeV or a power-law break at ∼200 TeV in the CR proton spectrum, most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV regime. Additionally, two young star clusters within W51B could also be theoretically viable to produce the most energetic γ rays observed by LHAASO. Our findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.
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Liu J, Chen Y, Wang W, Hao C, Cai F, Teng L, Luo X. Impact analysis of dust evolution pattern and determination of key ventilation parameters in highland highway construction tunnels. Heliyon 2024; 10:e33758. [PMID: 39027509 PMCID: PMC11255486 DOI: 10.1016/j.heliyon.2024.e33758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/20/2024] Open
Abstract
In order to study the influence of ventilation parameters on the ventilation of plateau highway construction tunnels, a highway tunnel construction section in Yunnan is taken as the research background, and Fluent software is used for simulation. The results of the study show that: under the conditions of press-in ventilation, the wind speed in the center of the vortex area in the wind flow field is smaller than the wind speed in the surrounding area, and with the diffusion of the flow field, the average wind speed in the tunnel section gradually decreases, and ultimately stabilizes at the level of 0.5 m/s. After blasting, the dust mass concentration on the return side of the tunnel is higher than that on the duct side. Dust with a particle size of 30 μm or more settled rapidly within 100 m from the boring face, while dust with a particle size of 30 μm or less gradually diffused outward under the action of the wind flow. In the vicinity of the tunnel boring face, reducing the distance from the air outlet to the boring face and increasing the air velocity can improve the dust removal effect. This conclusion can provide theoretical basis and certain guidance for the evolution of dust and dust prevention in the tunnel construction process in plateau area.
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Snyder K, Ahmed CMS, Ali MY, Butler S, DeNieu M, Houser W, Paisley B, Rosentreter M, Wang W, Larsen B. sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis. FRONTIERS IN TOXICOLOGY 2024; 6:1392686. [PMID: 39077556 PMCID: PMC11284615 DOI: 10.3389/ftox.2024.1392686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/03/2024] [Indexed: 07/31/2024] Open
Abstract
The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR. sendigR reference manual: https://phuse-org.github.io/sendigR/. sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.
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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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Ge C, Wang Q, Wang W, Cheng LQ, Wang YE, Huang LL, Li YJ, Wu HB, Zhang AL. [Pituitary Crooke cell neuroendocrine tumor of adrenocorticotropic hormone differentiation-specific transcription factor lineage: a clinicopathological analysis of six cases]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2024; 53:722-727. [PMID: 38955705 DOI: 10.3760/cma.j.cn112151-20231208-00397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Objective: To investigate the clinicopathological features of Crooke cell tumor of adrenocorticotropic hormone differentiation specific transcription factor (TPIT, also known as transcription factor 19, TBX19) lineage neuroendocrine tumors. Methods: Six cases of Crooke cell tumor diagnosed at the First Affiliated Hospital of University of Science and Technology of China, Hefei, China from October 2019 to October 2023 were collected. The clinical and pathological features of these cases were analyzed. Results: Among the six cases, one was male and five were female, with ages ranging from 26 to 75 years, and an average age of 44 years. All tumors occurred within the sella turcica. Clinical presentations included visual impairment in two cases, menstrual disorders in one case, Cushing's syndrome in one case, headache in one case, and one asymptomatic case discovered during a physical examination. Preoperative serum analyses revealed elevated levels of cortisol and adrenocorticotropic hormones in two cases, elevated cortisol in two cases, elevated adrenocorticotropic hormone in one case, and one case with a mild increase in prolactin due to the pituitary stalk effect. Magnetic resonance imaging revealed uneven enhancement of masses with maximum diameters ranging from 1.7 to 3.2 cm, all identified as macroadenomas. Microscopically, tumor cells exhibited irregular polygonal shapes, solid sheets, or pseudo-papillary arrangements around blood vessels. The cell nuclei were eccentric or centrally located, varying in size, with abundant cytoplasm. Some tumor cells showed perinuclear halo. Immunohistochemistry demonstrated diffuse strong positivity for TPIT in five cases, focal weak positivity for TPIT in one case, diffuse strong positivity for adrenocorticotropic hormone in all cases, and faint staining around the nuclei in a few cells. CK8/18 showed a strong positive ring pattern in more than 50% of tumor cells, focal weak positive expression of p53, and the Ki-67 positive index ranged 1%-5%. Periodic acid-Schiff staining revealed positive cytoplasm and negative perinuclear areas. Conclusions: Crooke cell tumor is a rare type of pituitary neuroendocrine tumors. Its pathological characteristics include a distinctive perinuclear clear zone and immunohistochemical markers, such as CK8/18 exhibiting a ring or halo pattern. This entity represents a high-risk subtype among pituitary neuroendocrine tumors, displaying a high risk of invasion and a propensity for recurrence. Accurate diagnosis is crucial for the postoperative follow-up and multimodal treatment planning.
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Smith H, Smith J, Smith R, Smizanska M, Smolek K, Snesarev A, Snider S, Snoek H, Snyder S, Sobie R, Soffer A, Sanchez CS, Soldatov E, Soldevila U, Solodkov A, Solomon S, Soloshenko A, Solovieva K, Solovyanov O, Solovyev V, Sommer P, Sonay A, Song W, Sonneveld J, Sopczak A, Sopio A, Sopkova F, Sothilingam V, Sottocornola S, Soualah R, Soumaimi Z, South D, Soybelman N, Spagnolo S, Spalla M, Sperlich D, Spigo G, Spinali S, Spiteri D, Spousta M, Staats E, Stabile A, Stamen R, Stampekis A, Standke M, Stanecka E, Stange M, Stanislaus B, Stanitzki M, Stapf B, Starchenko E, Stark G, Stark J, Starko D, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer H, Stelzer-Chilton O, Stenzel H, Stevenson T, Stewart G, Stewart J, Stockton M, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom D, Strom L, Stroynowski R, Strubig A, Stucci S, Stugu B, Stupak J, Styles N, Su D, Su S, Su W, Su X, Sugizaki K, Sulin V, Sullivan M, Sultan D, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Gudnadottir OS, Sur N, Sutton M, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Vargas JT, Takeva E, Takubo Y, Talby M, Talyshev A, Tam K, Tamir N, Tanaka A, Tanaka J, Tanaka R, Tanasini M, Tao Z, Araya ST, Tapprogge S, Mohamed ATA, Tarem S, Tariq K, Tarna G, Tartarelli G, Tas P, Tasevsky M, Tassi E, Tate A, Tateno G, Tayalati Y, Taylor G, Taylor W, Teagle H, Tee A, De Lima RT, Teixeira-Dias P, Teoh J, Terashi K, Terron J, Terzo S, Testa M, Teuscher R, Thaler A, Theiner O, Themistokleous N, Theveneaux-Pelzer T, Thielmann O, Thomas D, Thomas J, Thompson E, Thompson P, Thomson E, Tian Y, Tikhomirov V, Tikhonov Y, Timoshenko S, Timoshyn D, Ting E, Tipton P, Tlou S, Tnourji A, Todome K, Todorova-Nova S, Todt S, Togawa M, Tojo J, Tokár S, Tokushuku K, Toldaiev O, Tombs R, Tomoto M, Tompkins L, Topolnicki K, Torrence E, Torres H, Pastor ET, Toscani M, Tosciri C, Tost M, Tovey D, Traeet A, Trandafir I, Trefzger T, Tricoli A, Trigger I, Trincaz-Duvoid S, Trischuk D, Trocmé B, Troncon C, Truong L, Trzebinski M, Trzupek A, Tsai F, Tsai M, Tsiamis A, Tsiareshka P, Tsigaridas S, Tsirigotis A, Tsiskaridze V, Tskhadadze E, Tsopoulou M, Tsujikawa Y, Tsukerman I, Tsulaia V, Tsuno S, Tsur O, Tsuri K, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna A, Turchikhin S, Cakir IT, Turra R, Turtuvshin T, Tuts P, Tzamarias S, Tzanis P, Tzovara E, Ukegawa F, Poblete PU, Umaka E, Unal G, Unal M, Undrus A, Unel G, Urban J, Urquijo P, Usai G, Ushioda R, Usman M, Uysal Z, Vacavant L, Vacek V, Vachon B, Vadla K, Vafeiadis T, Vaitkus A, Valderanis C, Santurio EV, Valente M, Valentinetti S, Valero A, Moreno EV, Vallier A, Ferrer JV, Van Arneman D, Van Daalen T, Van Der Graaf A, Van Gemmeren P, Van Rijnbach M, Van Stroud S, Van Vulpen I, Vanadia M, Vandelli W, Vandenbroucke M, Vandewall E, Vannicola D, 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Wang S, Wang T, Wang W, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Warburton A, Ward R, Warrack N, Watson A, Watson H, Watson M, Watton E, Watts G, Waugh B, Weber C, Weber H, Weber M, Weber S, Wei C, Wei Y, Weidberg A, Weik E, Weingarten J, Weirich M, Weiser C, Wells C, Wenaus T, Wendland B, Wengler T, Wenke N, Wermes N, Wessels M, Wharton A, White A, White A, White M, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wiglesworth C, Wilbern D, Wilkens H, Williams D, Williams H, Williams S, Willocq S, Wilson B, Windischhofer P, Winkel F, Winklmeier F, Winter B, Winter J, Wittgen M, Wobisch M, Wolffs Z, Wölker R, Wollrath J, Wolter M, Wolters H, Wongel A, Worm S, Wosiek B, Woźniak K, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu S, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt T, Wynne B, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi Y, Yamashita E, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang H, Yang H, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap Y, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo B, Yexley M, Yin P, Yorita K, Younas S, Young C, Young C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Saa JZ, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng J, Zenger D, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang D, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu C, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine N, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas T, Zormpa O, Zou W, Zwalinski L. Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb -1 of s=13 TeV pp collision data. Sci Bull (Beijing) 2024:S2095-9273(24)00399-2. [PMID: 39179456 DOI: 10.1016/j.scib.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/03/2023] [Accepted: 05/27/2024] [Indexed: 08/26/2024]
Abstract
Results from a wide range of searches targeting different experimental signatures with and without missing transverse momentum (ETmiss) are used to constrain a Two-Higgs-Doublet Model (2HDM) with an additional pseudo-scalar mediating the interaction between ordinary and dark matter (2HDM+a). The analyses use up to 139 fb-1 of proton-proton collision data at a centre-of-mass energy s=13 TeV recorded with the ATLAS detector at the Large Hadron Collider during 2015-2018. The results from three of the most sensitive searches are combined statistically. These searches target signatures with large ETmiss and a leptonically decaying Z boson; large ETmiss and a Higgs boson decaying to bottom quarks; and production of charged Higgs bosons in final states with top and bottom quarks, respectively. Constraints are derived for several common and new benchmark scenarios in the 2HDM+a.
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Fu YY, Zhou JJ, Zhang CK, Sun LH, Wang W, Ren J, Wang LH, Tang D, Ma Y, Wang DR. [Application of D-type stoma repair in parastomal hernia after permanent sigmoidostomy]. ZHONGHUA WEI CHANG WAI KE ZA ZHI = CHINESE JOURNAL OF GASTROINTESTINAL SURGERY 2024; 27:621-624. [PMID: 38901996 DOI: 10.3760/cma.j.cn441530-20230831-00078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
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Wang W, Yang H, Fan Z, Shi R. STL Inhibited Angiogenesis of DPSCs Through Depressing Mitochondrial Respiration by Enhancing RNF217. Adv Biol (Weinh) 2024:e2400042. [PMID: 38880848 DOI: 10.1002/adbi.202400042] [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: 01/22/2024] [Revised: 05/10/2024] [Indexed: 06/18/2024]
Abstract
Angiogenesis is the determining factor during dental pulp regeneration. Six-twelve leukemia (STL) is identified as a key regulatory factor on the biological function of dental pulp stem cells (DPSCs) under hypoxic conditions, but its effect on angiogenesis is unclear. Co-culture of DPSCs and human umbilical vein endothelial cells (HUVECs) is used to detect tubule formation ability in vitro and the angiogenesis ability in vivo. RNA-seq and bioinformatic analyses are performed to screen differentially expressed genes. Seahorse Cell Mito Stress Test is proceeded to exam mitochondrial respiration. STL decreased tubule formation and mitochondrial respiration of DPSCs in vitro and restrained the number of blood vessels and the expression of VEGF in new formed tissue in vivo. Furthermore, pretreating STL-depleted DPSCs with rotenone, a mitochondrial respiration inhibitor, counteracted the promoting effect of STL knockdown on tubule formation. Then, RNA-seq and bioinformatic analyses identified some angiogenesis relevant genes and pathways in STL-depleted DPSCs. And STL enhanced expression of mRNA-ring finger protein 217 (RNF217), which inhibited the tubule formation and mitochondrial respiration of DPSCs. STL inhibited the angiogenesis of DPSCs through depressing mitochondrial respiration by enhancing RNF217, indicating that STL is a potential target for angiogenesis of DPSCs.
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Huang H, Fan Y, Yan F, Hu Y, He H, Xu T, Zhu X, Zhu Y, Diao W, Xia X, Tu J, Li A, Lin B, Liu Q, Lu Z, Xi T, Wang W, Xu D, Chen Z, Wang Z, Chen X, Shan G. Diabetes and long duration leading to speech-, low/mid-, and high- frequency hearing loss: current evidence from the China National Health Survey 2023. J Endocrinol Invest 2024:10.1007/s40618-024-02406-2. [PMID: 38869778 DOI: 10.1007/s40618-024-02406-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/29/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE To examine the effect of diabetes, duration of diabetes, and blood glucose on speech-, low/mid-, and high-frequency hearing loss. METHODS In this cross-sectional study, 2821 participants aged 20-87 years in the China National Health Survey were included. Diabetes was defined as valid fasting blood glucose (FBG) of ≥ 7.0 mmol/L, a self-reported history of diabetes or the use of anti-diabetic medications. Speech-(500, 1000, 2000, and 4000 Hz), low/mid- (500, 1000 and 2000 Hz), and high-frequency (4000, 6000, and 8000 Hz) hearing loss was defined as pure tone average of responding frequencies > 20 dB HL in the better ear, respectively. RESULTS In fully adjusted models, for speech-, low/mid-, and high-frequency hearing loss, compared with no diabetes, those with diabetes (OR[95%CI]: 1.44 [1.12, 1.86], 1.23 [0.94, 1.61], and 1.75 [1.28, 2.41], respectively) and with diabetes for > 5 years duration (OR[95%CI]: 1.63 [1.09, 2.42], and 1.63 [1.12, 2.36], 2.15 [1.25, 3.70], respectively) were at higher risk. High FBG level was associated with a higher risk of speech-, low/ mid-, and high-frequency hearing loss. And there were stronger associations between HL and diabetes, longer duration and higher in "healthier population" (no hypertension, no dyslipidemia and younger age). CONCLUSION Diabetes, longer duration, and higher FBG level were independently associated with hearing loss for speech-, low/mid- and high-frequency hearing loss, particularly in higher frequency and "healthier population". Paying more attention to hearing loss in those populations could lower the burden of hearing loss.
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