Refine
Year of publication
- 2021 (74) (remove)
Language
- English (74)
Has Fulltext
- yes (74)
Is part of the Bibliography
- no (74)
Keywords
- BESIII (5)
- Branching fraction (3)
- e+-e− Experiments (3)
- Lepton colliders (2)
- Particle decays (2)
- ARDS (1)
- Absolute branching fraction (1)
- Born cross section (1)
- COVID-19 (1)
- Charm physics (1)
Institute
Salt-inducible kinases (SIKs) are key metabolic regulators. Imbalance of SIK function is associated with the development of diverse cancers, including breast, gastric and ovarian cancer. Chemical tools to clarify the roles of SIK in different diseases are, however, sparse and are generally characterized by poor kinome-wide selectivity. Here, we have adapted the pyrido[2,3-d]pyrimidin-7-one-based PAK inhibitor G-5555 for the targeting of SIK, by exploiting differences in the back-pocket region of these kinases. Optimization was supported by high-resolution crystal structures of G-5555 bound to the known off-targets MST3 and MST4, leading to a chemical probe, MRIA9, with dual SIK/PAK activity and excellent selectivity over other kinases. Furthermore, we show that MRIA9 sensitizes ovarian cancer cells to treatment with the mitotic agent paclitaxel, confirming earlier data from genetic knockdown studies and suggesting a combination therapy with SIK inhibitors and paclitaxel for the treatment of paclitaxel-resistant ovarian cancer.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain. Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded. Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.
We report first results on elliptic flow of identified particles at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV using the STAR TPC at RHIC. The elliptic flow as a function of transverse momentum and centrality differs significantly for particles of different masses. This dependence can be accounted for in hydrodynamic models, indicating that the system created shows a behavior consistent with collective hydrodynamical flow. The fit to the data with a simple model gives information on the temperature and flow velocities at freeze-out.
Using data corresponding to an integrated luminosity of 651 pb−1 accumulated at 22 center-of-mass energy points between 2.0000 and 3.0800 GeV by the BESIII experiment, the process e+e−→ϕπ+π− is studied. The Born cross sections for e+e−→ϕπ+π− are consistent with previous results, but with improved precision. A fit to the cross section is performed, which reveals contributions from two structures: the first one has a mass of M = (2158+30−33 ± 4) MeV/c2 and a width of Γ = (218+81−64 ± 5) MeV, and the second one has a mass of M = (2298+60−44 ± 6) MeV/c2 and a width of Γ = (219+117−112 ± 6) MeV, where the first uncertainties are statistical and the second systematic.
Based on electron-positron collision data collected with the BESIII detector operating at the BEPCII storage rings, the value of R≡σ(e+e−→hadrons)/σ(e+e−→μ+μ−) is measured at 14 center-of-mass energies from 2.2324 to 3.6710 GeV. The resulting uncertainties are less than 3.0%, and are dominated by systematic uncertainties.
By analyzing 6.32 fb − 1 of e+ e− annihilation data collected at the center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we determine the branching fraction of the leptonic decay D + s → τ + ντ, with τ+ → π + π0¯ντ, to be B D + s → τ + ν τ = (5.29 ± 0.25 stat ± 0.20 syst) %. We estimate the product of the Cabibbo-Kobayashi-Maskawa matrix element |Vcs|and the D + s decay constant f D + s to be f D + s|Vcs| = (244.8 ± 5.8 stat ± 4.8syst) MeV, using the known values of the τ + and D + s masses as well as the D + s lifetime, together with our branching fraction measurement. Combining the value of |Vcs| obtained from a global fit in the standard model and f D + s from lattice quantum chromodynamics, we obtain f D + s = (251.6 ± 5.9 stat ± 4.9syst) MeV and |Vcs| = 0.980 ± 0.023 stat ± 0.019 syst. Using the branching fraction of B D + s → μ + νμ = (5.35±0.21)×10−3, we obtain the ratio of the branching fractions B D + s → τ + ντ/B D +s → μ+νμ = 9.89±0.71, which is consistent with the standard model prediction of lepton flavor universality.
Born cross sections for the processes e+e− → ωη and e+e− → ωπ0 have been determined for centerof-mass energies between 2.00 and 3.08 GeV with the BESIII detector at the BEPCII collider. The results obtained in this work are consistent with previous measurements but with improved precision. Two resonant structures are observed. In the e+e− → ωη cross sections, a resonance with a mass of (2176 ± 24 ± 3) MeV/c2 and a width of (89 ± 50 ± 5) MeV is observed with a significance of 6.2σ. Its properties are consistent with the φ(2170). In the e+e− → ωπ0 cross sections, a resonance denoted Y (2040) is observed with a significance of more than 10σ. Its mass and width are determined to be (2034 ± 13 ± 9) MeV/c2 and (234 ± 30 ± 25) MeV, respectively, where the first uncertainties are statistical and the second ones are systematic.
Observation of η′ → π⁺π⁻μ⁺μ⁻
(2021)
Using (1310.6±7.0)×106 𝐽/𝜓 events acquired with the BESIII detector at the BEPCII storage rings, the decay 𝜂′→𝜋+𝜋−𝜇+𝜇− is observed for the first time with a significance of 8𝜎 via the process 𝐽/𝜓→𝛾𝜂′. We measure the branching fraction of 𝜂′→𝜋+𝜋−𝜇+𝜇− to be ℬ(𝜂′→𝜋+𝜋−𝜇+𝜇−)=(1.97±0.33(stat)±0.19(syst))×10−5, where the first and second uncertainties are statistical and systematic, respectively
The rare decay 𝜂′→𝜋+𝜋−𝑒+𝑒− is studied using a sample of 1.3×109 𝐽/𝜓 events collected with the BESIII detector at BEPCII in 2009 and 2012. The branching fraction is measured with improved precision to be (2.42±0.05stat±0.08syst)×10−3. Due to the inclusion of new data, this result supersedes the last BESIII result on this branching fraction. In addition, the 𝐶𝑃-violating asymmetry in the angle between the decay planes of the 𝜋+𝜋−-pair and the 𝑒+𝑒−-pair is investigated. A measurable value would indicate physics beyond the standard model; the result is 𝒜𝐶𝑃=(2.9±3.7stat±1.1syst)%, which is consistent with the standard model expectation of no 𝐶𝑃-violation. The precision is comparable to the asymmetry measurement in the 𝐾0𝐿→𝜋+𝜋−𝑒+𝑒− decay where the observed (14±2)% effect is driven by a standard model mechanism.