Plasma calprotectin as a biomarker of ultrasound synovitis in rheumatoid arthritis patients receiving IL-6 antagonists or JAK inhibitors

To analyse the accuracy of plasma calprotectin in patients with rheumatoid arthritis (RA) receiving monoclonal antibodies against IL-6 receptors (anti-rIL-6) or JAK inhibitors (JAKis) in detecting ultrasound (US) synovitis and compare it with acute phase reactants [high-sensitivity C-reactive protein (hs-CRP) and ESR].An observational cross-sectional study of RA patients receiving anti-rIL-6 (tocilizumab or sarilumab) or JAKi, (baricitinib or tofacitinib) was made. Plasma calprotectin for the diagnosis of US synovitis [synovial hypertrophy grade (SH)???2 plus power Doppler signal (PD)???1] was analysed using receiver operating characteristic curves (ROCs). The performance of ESR and hs-CRP was also studied. The three ROC curves were compared to determine which had the highest discriminatory power. Associations between plasma calprotectin and US scores were made using correlation analysis.Sixty-three RA patients were included. Mean plasma calprotectin levels were significantly higher in patients with US synovitis than in those without (0.89?±?0.85 vs 0.30?±?0.12 ?g/ml; p?=?0.0003). A moderate correlation between calprotectin and all US scores (HS score Rho?=?0.479; PD score Rho?=?0.492; and global score Rho?=?0.495) was found. The discriminatory capacity of plasma calprotectin showed an AUC of 0.795 (95% CI: 0.687-0.904). The AUC of hs-CRP and ESR was 0.721 and 0.564, respectively. hs-CRP serum levels showed a low positive correlation with the three US scores (Rho?<?0.40). After analysis according to the drugs administered, the correlation disappeared in patients receiving anti-rIL-6.Plasma calprotectin may be a sensitive biomarker of synovial inflammation in RA patients treated with anti-rIL-6 or JAKi.© The Author(s), 2022

High Sensitivity C Reactive Protein in Patients with Rheumatoid Arthritis Treated with Antibodies against IL-6 or Jak Inhibitors: A Clinical and Ultrasonographic Study

Background: We examined whether high-sensitivity CRP (hsCRP) reflected the inflammatory disease status evaluated by clinical and ultrasound (US) parameters in RA patients receiving IL-6 receptor antibodies (anti-IL-6R) or JAK inhibitors (JAKi). Methods: We conducted a cross-sectional study of patients with established RA receiving anti-IL-6R (tocilizumab, sarilumab) or JAKi (tofacitinib, baricitinib). Serum hsCRP and US synovitis in both hands were measured. Associations between hsCRP and clinical inflammatory activity were evaluated using composite activity indices. The association between hsCRP and US synovitis was analyzed. Results: 63 (92% female) patients (42 anti- IL-6R and 21 JAKi) were included, and the median disease duration was 14.4 (0.2–37.5) years. Most patients were in remission or had low levels of disease. Overall hsCRP values were very low, and significantly lower in anti-IL-6R patients (median 0.04 mg/dL vs. 0.16 mg/dL). Anti-IL-6R (82.4%) patients and 48% of JAKi patients had very low hsCRP levels (≤0.1 mg/dL) (p = 0.002). In the anti-IL-6R group, hsCRP did not correlate with the composite activity index or US synovitis. In the JAKi group, hsCRP moderately correlated with US parameters (r = 0.5) but not clinical disease activity, and hsCRP levels were higher in patients with US synovitis (0.02 vs. 0.42 mg/dL) (p = 0.001). Conclusion: In anti-IL-6R RA-treated patients, hsCRP does not reflect the inflammatory disease state, but in those treated with JAKi, hsCRP was associated with US synovitis

The synovial and blood monocyte DNA methylomes mirror prognosis, evolution and treatment in early arthritis

Identifying predictive biomarkers at early stages of early inflammatory arthritis is crucial for starting appropriate therapies to avoid poor outcomes. Monocytes and macrophages, largely associated with arthritis, are contributors and sensors of inflammation through epigenetic modifications. In this study, we investigated associations between clinical features and DNA methylation in blood and synovial fluid (SF) monocytes in a prospective cohort of early inflammatory arthritis patients. Undifferentiated arthritis (UA) blood monocyte DNA methylation profiles exhibited significant alterations in comparison with those from healthy donors. We identified additional differences both in blood and SF monocytes after comparing UA patients grouped by their future outcomes, good versus poor. Patient profiles in subsequent visits revealed a reversion towards a healthy level in both groups, those requiring disease-modifying antirheumatic drugs (DMARDs) and those that remitted spontaneously. Changes in disease activity between visits also impacted DNA methylation, partially concomitant in the SF of UA and in blood monocytes of rheumatoid arthritis patients. Epigenetic similarities between arthritis types allow a common prediction of disease activity. Our results constitute a resource of DNA methylation-based biomarkers of poor prognosis, disease activity and treatment efficacy in early untreated UA patients for the personalized clinical management of early inflammatory arthritis patients

Imaging Findings in Patients with Immune Checkpoint Inhibitor-Induced Arthritis

Immune checkpoint inhibitor (ICI)-induced arthritis is an increasingly recognized adverse event in patients with oncologic disease during immunotherapy. Four patterns are well described, including rheumatoid arthritis (RA)-like, polymyalgia rheumatica (PMR)-like, psoriatic arthritis (PsA)-like, and oligo-monoarthritis, among others. Despite better clinical recognition of these syndromes, information about the main imaging findings is limited. Methods: We conducted a retrospective observational study including all adult patients referred to the Rheumatology Department of a single-center due to ICI-induced arthritis who underwent imaging studies [ultrasound (US), magnetic resonance imaging (MRI), and (18)F-FDG PET/CT)] between January 2017 and January 2022. Results: Nineteen patients with ICI-induced arthritis with at least one diagnostic imaging assessment were identified (15 US, 4 MRI, 2 (18)F-FDG PET/CT). Most patients were male (84.2%), with a median age at inclusion of 73 years. The main underlying diagnoses for ICI treatment were melanoma in five cases. The distribution of ICI-induced arthritis was as follows: PMR-like (5, 26.2%), RA-like (4, 21.1%), PsA-like (4, 21.1%), and others (6, 31.6%). All RA-like patients had US findings indistinguishable from conventional RA patients. In addition, 3/5 (60%) of PMR-like patients had significant involvement of the hands and wrists. Abnormal findings on MRI or PET-CT were reported by clinical symptoms. No erosions or myofascitis were seen. Conclusions: ICI-induced arthritis patients present inflammatory patterns on imaging studies similar to conventional inflammatory arthropathies, and therefore these syndromes should be followed carefully and treated according to these findings

Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

[EN] Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analysesThis study used computing resources from Artemisa, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following agencies and institutions: Xunta de Galicia (Centro singularde investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the "Maria de Maeztu" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency"; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-20140398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Kekic, M.; Adams, C.; Woodruff, K.; Renner, J.; Church, E.; Del Tutto, M.; Hernando Morata, JA.... (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. Journal of High Energy Physics (Online). (1):1-22. https://doi.org/10.1007/JHEP01(2021)189S1221NEXT collaboration, The Next White (NEW) Detector, 2018 JINST 13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of 136Xe, JHEP 10 (2019) 230 [arXiv:1905.13110] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, JHEP 10 (2019) 052 [arXiv:1905.13141] [INSPIRE].NEXT collaboration, Radiogenic Backgrounds in the NEXT Double Beta Decay Experiment, JHEP 10 (2019) 051 [arXiv:1905.13625] [INSPIRE].G. Carleo et al., Machine learning and the physical sciences, Rev. Mod. Phys. 91 (2019) 045002 [arXiv:1903.10563] [INSPIRE].A. Aurisano et al., A Convolutional Neural Network Neutrino Event Classifier, 2016 JINST 11 P09001 [arXiv:1604.01444] [INSPIRE].MicroBooNE collaboration, Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber, 2017 JINST 12 P03011 [arXiv:1611.05531] [INSPIRE].MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber, Phys. Rev. D 99 (2019) 092001 [arXiv:1808.07269] [INSPIRE].N. Choma et al., Graph Neural Networks for IceCube Signal Classification, in proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, U.S.A., 17–20 December 2018, pp. 386–391 [arXiv:1809.06166] [INSPIRE].E. Racah et al., Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, in proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, U.S.A., 18–20 December 2016, pp. 892–897 [arXiv:1601.07621] [INSPIRE].EXO collaboration, Deep Neural Networks for Energy and Position Reconstruction in EXO-200, 2018 JINST 13 P08023 [arXiv:1804.09641] [INSPIRE].H. Qiao, C. Lu, X. Chen, K. Han, X. Ji and S. Wang, Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron. 61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].P. Ai, D. Wang, G. Huang and X. Sun, Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber, 2018 JINST 13 P08015 [arXiv:1803.01482] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST 12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP 05 (2016) 159 [arXiv:1511.09246] [INSPIRE].D. Nygren, High-pressure xenon gas electroluminescent TPC for 0-ν ββ-decay search, Nucl. Instrum. Meth. A 603 (2009) 337 [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using 83mKr decays, 2018 JINST 13 P10014 [arXiv:1804.01780] [INSPIRE].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. Thesis, University of Valencia, Valencia Spain (2015) [INSPIRE].GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [INSPIRE].C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On calibration of modern neural networks, arXiv:1706.04599.K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].K. He, X. Zhang, S. Ren and J. Sun, Identity mappings in deep residual networks, arXiv:1603.05027.X. Li, S. Chen, X. Hu and J. Yang, Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift, in proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, U.S.A., 15–20 June 2019, pp. 2677–2685.J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, U.S.A., 20–25 June 2009, pp. 248–255.B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102 (2020) 012005 [arXiv:1903.05663] [INSPIRE].C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019) 60.G.J. Székely and M.L. Rizzo, Testing for equal distributions in high dimension, InterStat 5 (2004) 1.G. Székely and M.L. Rizzo, Energy statistics: A class of statistics based on distances, J. Stat. Plann. Infer. 8 (2013) 1249.R.A. Fisher, The Design of Experiments, Oliver and Boyd (1935).NEXT collaboration, Sensitivity of a tonne-scale NEXT detector for neutrinoless double beta decay searches, arXiv:2005.06467 [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST 8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST 8 P09011 [arXiv:1306.0471] [INSPIRE]

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE

Combined search for the quarks of a sequential fourth generation

Results are presented from a search for a fourth generation of quarks produced singly or in pairs in a data set corresponding to an integrated luminosity of 5 inverse femtobarns recorded by the CMS experiment at the LHC in 2011. A novel strategy has been developed for a combined search for quarks of the up and down type in decay channels with at least one isolated muon or electron. Limits on the mass of the fourth-generation quarks and the relevant Cabibbo-Kobayashi-Maskawa matrix elements are derived in the context of a simple extension of the standard model with a sequential fourth generation of fermions. The existence of mass-degenerate fourth-generation quarks with masses below 685 GeV is excluded at 95% confidence level for minimal off-diagonal mixing between the third- and the fourth-generation quarks. With a mass difference of 25 GeV between the quark masses, the obtained limit on the masses of the fourth-generation quarks shifts by about +/- 20 GeV. These results significantly reduce the allowed parameter space for a fourth generation of fermions.Comment: Replaced with published version. Added journal reference and DO

Measurement of the t t-bar production cross section in the dilepton channel in pp collisions at sqrt(s) = 7 TeV

The t t-bar production cross section (sigma[t t-bar]) is measured in proton-proton collisions at sqrt(s) = 7 TeV in data collected by the CMS experiment, corresponding to an integrated luminosity of 2.3 inverse femtobarns. The measurement is performed in events with two leptons (electrons or muons) in the final state, at least two jets identified as jets originating from b quarks, and the presence of an imbalance in transverse momentum. The measured value of sigma[t t-bar] for a top-quark mass of 172.5 GeV is 161.9 +/- 2.5 (stat.) +5.1/-5.0 (syst.) +/- 3.6(lumi.) pb, consistent with the prediction of the standard model.Comment: Replaced with published version. Included journal reference and DO

Search for anomalous t t-bar production in the highly-boosted all-hadronic final state

A search is presented for a massive particle, generically referred to as a Z', decaying into a t t-bar pair. The search focuses on Z' resonances that are sufficiently massive to produce highly Lorentz-boosted top quarks, which yield collimated decay products that are partially or fully merged into single jets. The analysis uses new methods to analyze jet substructure, providing suppression of the non-top multijet backgrounds. The analysis is based on a data sample of proton-proton collisions at a center-of-mass energy of 7 TeV, corresponding to an integrated luminosity of 5 inverse femtobarns. Upper limits in the range of 1 pb are set on the product of the production cross section and branching fraction for a topcolor Z' modeled for several widths, as well as for a Randall--Sundrum Kaluza--Klein gluon. In addition, the results constrain any enhancement in t t-bar production beyond expectations of the standard model for t t-bar invariant masses larger than 1 TeV.Comment: Submitted to the Journal of High Energy Physics; this version includes a minor typo correction that will be submitted as an erratu